"""``MatlabDataset`` loads/saves data from/to a Matlab file using an underlying
filesystem ?(e.g.: local, S3, GCS)?. The underlying functionality is supported by
the specified backend library passed in (defaults to the ``matlab`` library), so it
supports all allowed options for loading and saving matlab files.
"""
from copy import deepcopy
from pathlib import PurePosixPath
from typing import Any
import fsspec
import numpy as np
from kedro.io.core import (
AbstractVersionedDataset,
DatasetError,
Version,
get_filepath_str,
get_protocol_and_path,
)
from scipy import io
[docs]
class MatlabDataset(AbstractVersionedDataset[np.ndarray, np.ndarray]):
"""`MatlabDataSet` loads and saves data from/to a MATLAB file using scipy.io.
Example usage for the
`YAML API <https://kedro.readthedocs.io/en/stable/data/\
data_catalog_yaml_examples.html>`_:
.. code-block:: yaml
cars:
type: matlab.MatlabDataset
filepath: gcs://your_bucket/cars.mat
fs_args:
project: my-project
credentials: my_gcp_credentials
Example usage for the
`Python API <https://kedro.readthedocs.io/en/stable/data/\
advanced_data_catalog_usage.html>`_:
.. code-block:: pycon
>>> from kedro_datasets.matlab import MatlabDataset
>>> import numpy as np
>>> data = np.array([1, 2, 3])
>>> dataset = MatlabDataset(filepath=tmp_path / "test.mat")
>>> dataset.save(data)
>>> reloaded = dataset.load()
>>> assert (data == reloaded["data"]).all()
"""
DEFAULT_SAVE_ARGS: dict[str, Any] = {"indent": 2}
[docs]
def __init__( # noqa = PLR0913
self,
filepath: str,
save_args: dict[str, Any] = None,
version: Version = None,
credentials: dict[str, Any] = None,
fs_args: dict[str, Any] = None,
metadata: dict[str, Any] = 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 `r` when loading
and to `w` 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 arguments
self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS)
if save_args is not None:
self._save_args.update(save_args)
_fs_open_args_save.setdefault("mode", "w")
self._fs_open_args_load = _fs_open_args_load
self._fs_open_args_save = _fs_open_args_save
def _describe(self) -> dict[str, Any]:
return {
"filepath": self._filepath,
"protocol": self._protocol,
"save_args": self._save_args,
"version": self._version,
}
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
def _save(self, data: np.ndarray) -> None:
save_path = get_filepath_str(self._get_save_path(), self._protocol)
with self._fs.open(save_path, mode="wb") as f:
io.savemat(f, {"data": data})
self._invalidate_cache()
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)
def _release(self) -> None:
super()._release()
self._invalidate_cache()
def _invalidate_cache(self) -> None:
"""Invalidate underlying filesystem caches."""
filepath = get_filepath_str(self._filepath, self._protocol)
self._fs.invalidate_cache(filepath)
