pymovements.dataset.DatasetDefinition#
- class pymovements.dataset.DatasetDefinition(name: str = '.', mirrors: tuple[str, ...] = <factory>, resources: tuple[dict[str, str], ...] = <factory>, experiment: Experiment | None = None, filename_regex: str = '.*', filename_regex_dtypes: dict[str, type] = <factory>, custom_read_kwargs: dict[str, Any] = <factory>, column_map: dict[str, str] = <factory>)[source]#
Definition to initialize a
Dataset.- name#
The name of the dataset.
- Type:
str
- mirrors#
A tuple of mirrors of the dataset. Each entry must be of type str and end with a ‘/’.
- Type:
tuple[str, …]
- resources#
A tuple of dataset resources. Each list entry must be a dictionary with the following keys: - resource: The url suffix of the resource. This will be concatenated with the mirror. - filename: The filename under which the file is saved as. - md5: The MD5 checksum of the respective file.
- Type:
tuple[dict[str, str], …]
- experiment#
The experiment definition.
- Type:
- filename_regex#
Regular expression which will be matched before trying to load the file. Namedgroups will appear in the fileinfo dataframe.
- Type:
str
- filename_regex_dtypes#
If named groups are present in the filename_regex, this makes it possible to cast specific named groups to a particular datatype.
- Type:
dict[str, type], optional
- column_map#
The keys are the columns to read, the values are the names to which they should be renamed.
- Type:
dict[str, str]
- custom_read_kwargs#
If specified, these keyword arguments will be passed to the file reading function.
- Type:
dict[str, Any], optional
- __init__(name: str = '.', mirrors: tuple[str, ...] = <factory>, resources: tuple[dict[str, str], ...] = <factory>, experiment: Experiment | None = None, filename_regex: str = '.*', filename_regex_dtypes: dict[str, type] = <factory>, custom_read_kwargs: dict[str, Any] = <factory>, column_map: dict[str, str] = <factory>) None
Methods
__init__([name, mirrors, resources, ...])Attributes