clouddrift.adapters.gdp.gdp6h.to_raggedarray

clouddrift.adapters.gdp.gdp6h.to_raggedarray(drifter_ids: list[int] | None = None, n_random_id: int | None = None, tmp_path: str = '/tmp/clouddrift/gdp6h') → RaggedArray

Download and process individual GDP 6-hourly files and return a RaggedArray instance with the data.

Parameters

drifter_idslist[int], optional

List of drifters to retrieve (Default: all)

n_random_idlist[int], optional

Randomly select n_random_id drifter NetCDF files

tmp_pathstr, optional

Path to the directory where the individual NetCDF files are stored (default varies depending on operating system; /tmp/clouddrift/gdp6h on Linux)

Returns

outRaggedArray

A RaggedArray instance of the requested dataset

Examples

Invoke to_raggedarray without any arguments to download all drifter data from the 6-hourly GDP feed:

>>> from clouddrift.adapters.gdp.gdp6h import to_raggedarray
>>> ra = to_raggedarray()

To download a random sample of 100 drifters, for example for development or testing, use the n_random_id argument:

>>> ra = to_raggedarray(n_random_id=100)

To download a specific list of drifters, use the drifter_ids argument:

>>> ra = to_raggedarray(drifter_ids=[54375, 114956, 126934])

Finally, to_raggedarray returns a RaggedArray instance which provides a convenience method to emit a xarray.Dataset instance:

>>> ds = ra.to_xarray()

To write the ragged array dataset to a NetCDF file on disk, do

>>> ds.to_netcdf("gdp6h.nc", format="NETCDF4")

Alternatively, to write the ragged array to a Parquet file, first create it as an Awkward Array:

>>> arr = ra.to_awkward()
>>> arr.to_parquet("gdp6h.parquet")