gedidb.GEDIProvider#

class gedidb.GEDIProvider(storage_type: str | None = None, s3_bucket: str | None = None, local_path: str | None = None, url: str | None = None, region: str | None = 'eu-central-1', credentials: dict | None = None)[source]#

GEDIProvider class to interface with GEDI data stored in TileDB, with support for flexible storage types.

scalar_array_uri#

URI for accessing the scalar data array.

Type:

str

ctx#

TileDB context for the configured storage type (S3 or local).

Type:

tiledb.Ctx

get_available_variables() → pd.DataFrame[source]#

Retrieve a list of available variables with descriptions and units.

query_nearest_shots(...) → Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray]][source]#

Query data for the nearest shots to a specified point.

query_data(...) → Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray]][source]#

Query data within specified spatial and temporal bounds.

get_data(...) → pd.DataFrame | xr.Dataset | None[source]#

Retrieve queried data in either Pandas DataFrame or Xarray Dataset format.

__init__(storage_type: str | None = None, s3_bucket: str | None = None, local_path: str | None = None, url: str | None = None, region: str | None = 'eu-central-1', credentials: dict | None = None)[source]#

Initialize GEDIProvider with URIs for scalar and profile data arrays, configured based on storage type.

Parameters:

  • storage_type (str, optional) – Storage type, either ‘s3’ or ‘local’. Defaults to ‘local’.

  • s3_bucket (str, optional) – The S3 bucket name for GEDI data storage. Required if storage_type is ‘s3’.

  • local_path (str, optional) – The local path for storing GEDI data arrays. Used if storage_type is ‘local’.

  • url (str, optional) – Custom endpoint URL for S3-compatible object stores (e.g., MinIO).

  • region (str, optional) – AWS region for S3 access. Defaults to ‘eu-central-1’.

Notes

Supports both S3 and local storage configurations based on storage_type.

Methods

__init__([storage_type, s3_bucket, ...])

Initialize GEDIProvider with URIs for scalar and profile data arrays, configured based on storage type.

get_available_variables()

Retrieve metadata for available variables in the scalar TileDB array.

get_data(variables[, geometry, start_time, ...])

Retrieve GEDI data based on spatial, temporal, and quality filters, and return it in either Pandas or Xarray format.

query_data(variables[, geometry, ...])

Query GEDI data from TileDB arrays within a specified spatial bounding box and time range, applying optional quality filters with flexible filter expressions.

query_nearest_shots(variables, point, ...[, ...])

Retrieve data for the nearest GEDI shots around a specified reference point, within a given radius.

to_dataframe(scalar_data)

Convert scalar and profile data dictionaries into a unified pandas DataFrame.

to_xarray(scalar_data, metadata, profile_vars)

Convert scalar and profile data to an Xarray Dataset, with metadata attached.