read_mat

ReadMat

Bases: NumpyOp

A class for reading .mat files from disk.

This expects every sample to have a separate .mat file.

Parameters:

Name	Type	Description	Default
file	str	Dictionary key that contains the .mat path.	required
keys	Union[str, Iterable[str]]	Key(s) to read from the .mat file.	required
mode	Union[None, str, Iterable[str]]	What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument like "!infer" or "!train".	None
ds_id	Union[None, str, Iterable[str]]	What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all ds_ids except for a particular one, you can pass an argument like "!ds1".	None
parent_path	str	Parent path that will be prepended to a given filepath.	''

Source code in fastestimator\fastestimator\op\numpyop\multivariate\read_mat.py

@traceable()
class ReadMat(NumpyOp):
    """A class for reading .mat files from disk.

    This expects every sample to have a separate .mat file.

    Args:
        file: Dictionary key that contains the .mat path.
        keys: Key(s) to read from the .mat file.
        mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
        ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
            ds_ids except for a particular one, you can pass an argument like "!ds1".
        parent_path: Parent path that will be prepended to a given filepath.
    """
    def __init__(self,
                 file: str,
                 keys: Union[str, Iterable[str]],
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None,
                 parent_path: str = ""):
        super().__init__(inputs=file, outputs=keys, mode=mode, ds_id=ds_id)
        self.parent_path = parent_path
        self.out_list = True

    def forward(self, data: str, state: Dict[str, Any]) -> List[Dict[str, Any]]:
        data = loadmat(os.path.normpath(os.path.join(self.parent_path, data)))
        results = [data[key] for key in self.outputs]
        return results