hydromt.data_catalog.sources.DataFrameSource#
- pydantic model hydromt.data_catalog.sources.DataFrameSource[source]#
DataSource for DataFrames.
Reads and validates DataCatalog entries.
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- field driver: DataFrameDriver [Required]#
- field data_adapter: DataFrameAdapter [Optional]#
- read_data(*, variables: List[str] | None = None, time_range: TimeRange | None = None, handle_nodata: NoDataStrategy = NoDataStrategy.RAISE) DataFrame | None[source]#
Use the resolver, driver, and data adapter to read and harmonize the data.
- to_file(file_path: Path | str, *, driver_override: DataFrameDriver | None = None, variables: list[str] | None = None, time_range: TimeRange | None = None, handle_nodata: NoDataStrategy = NoDataStrategy.RAISE, write_kwargs: dict[str, Any] | None = None) DataFrameSource | None[source]#
Write the DataFrameSource to a local file.
args:
- to_stac_catalog(handle_nodata: NoDataStrategy = NoDataStrategy.IGNORE) Catalog | None[source]#
Convert a dataframe into a STAC Catalog representation.
The collection will contain an asset for each of the associated files.
- Parameters:
(str (- handle_nodata) – Options are: “raise” to raise an error on failure, “skip” to skip the dataframe on failure, and “coerce” (default) to set default values on failure.
optional) (
The error handling strategy.) – Options are: “raise” to raise an error on failure, “skip” to skip the dataframe on failure, and “coerce” (default) to set default values on failure.
- Returns:
- Optional[StacCatalog] – None if the dataset was skipped.
- Return type:
The STAC Catalog representationofthe dataframe,or