kedro_datasets_experimental.rioxarray.GeoTIFFDataset

class kedro_datasets_experimental.rioxarray.GeoTIFFDataset(*, filepath, load_args=None, save_args=None, version=None, metadata=None)

GeoTIFFDataset loads and saves rasterdata files and reads them as xarray DataArrays. The underlying functionality is supported by rioxarray, rasterio and xarray.

Reading and writing of single and multiband GeoTIFFs data is supported. There are sanity checks to ensure that a coordinate reference system (CRS) is present. Supported dimensions are (“band”, “x”, “y”) and (“x”, “y”) and xarray.DataArray with other dimension can not be saved to a GeoTIFF file. Have a look at netcdf if this is what you need.

sentinal_data:
  type: rioxarray.GeoTIFFDataset
  filepath: sentinal_data.tif

Example usage for the

Python API:

 from kedro_datasets.rioxarray import GeoTIFFDataset
 import xarray as xr
 import numpy as np

 data = xr.DataArray(
...     np.random.randn(2, 3, 2),
...     dims=("band", "y", "x"),
...     coords={"band": [1, 2], "y": [0.5, 1.5, 2.5], "x": [0.5, 1.5]},
... )
 data_crs = data.rio.write_crs("epsg:4326")
 data_spatial_dims = data_crs.rio.set_spatial_dims("x", "y")
 dataset = GeoTIFFDataset(filepath="test.tif")
 dataset.save(data_spatial_dims)
 reloaded = dataset.load()
 xr.testing.assert_allclose(data_spatial_dims, reloaded, rtol=1e-5)

Attributes

DEFAULT_LOAD_ARGS
DEFAULT_SAVE_ARGS

Methods

exists()	Checks whether a dataset's output already exists by calling the provided _exists() method.
from_config(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
load()	Loads data by delegation to the provided load method.
release()	Release any cached data.
resolve_load_version()	Compute the version the dataset should be loaded with.
resolve_save_version()	Compute the version the dataset should be saved with.
save(data)	Saves data by delegation to the provided save method.
to_config()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_LOAD_ARGS: dict[str, Any] = {}

DEFAULT_SAVE_ARGS: dict[str, Any] = {}

__init__(*, filepath, load_args=None, save_args=None, version=None, metadata=None)

Creates a new instance of GeoTIFFDataset pointing to a concrete geospatial raster data file.

Parameters:

• filepath (str) – Filepath in POSIX format to a rasterdata file. The prefix should be any protocol supported by fsspec.

• load_args (Optional[dict[str, Any]]) – rioxarray options for loading rasterdata files. Here you can find all available arguments: https://corteva.github.io/rioxarray/html/rioxarray.html#rioxarray-open-rasterio All defaults are preserved.

• save_args (Optional[dict[str, Any]]) – options for rioxarray for data without the band dimension and rasterio otherwhise.

• version (Optional[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.

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

exists()

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)

Create a dataset instance using the configuration provided.

Parameters:

• name (str) – Data set name.

• config (dict[str, Any]) – Data set config dictionary.

• load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

• save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()

Loads data by delegation to the provided load method.

Return type:

DataArray

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

resolve_load_version()

Compute the version the dataset should be loaded with.

Return type:

Optional[str]

resolve_save_version()

Compute the version the dataset should be saved with.

Return type:

Optional[str]

save(data)

Saves data by delegation to the provided save method.

Parameters:

data (DataArray) – the value to be saved by provided save method.

Raises:

• DatasetError – when underlying save method raises error.

• FileNotFoundError – when save method got file instead of dir, on Windows.

• NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None

to_config()

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:

dict[str, Any]

Returns:

A dictionary containing the dataset’s type and initialization arguments.