"""``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 __future__ import annotations
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://docs.kedro.org/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://docs.kedro.org/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}
DEFAULT_FS_ARGS: dict[str, Any] = {"open_args_save": {"mode": "wb"}}
[docs]
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 {}),
}
def _describe(self) -> dict[str, Any]:
return {
"filepath": self._filepath,
"protocol": self._protocol,
"save_args": self._save_args,
"version": self._version,
}
[docs]
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
[docs]
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()
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)
