clouddrift.ragged.unpack(ragged_array: ndarray, rowsize: ndarray[int], rows: int | Iterable[int] = None, axis: int = 0) list[ndarray][source]#

Unpack a ragged array into a list of regular arrays.

Unpacking a np.ndarray ragged array is about 2 orders of magnitude faster than unpacking an xr.DataArray ragged array, so unless you need a DataArray as the result, we recommend passing np.ndarray as input.



A ragged_array to unpack


An array of integers whose values is the size of each row in the ragged array

rowsint or Iterable[int], optional

A row or list of rows to unpack. Default is None, which unpacks all rows.

axisint, optional

The axis along which to unpack the ragged array. Default is 0.



A list of array-likes with sizes that correspond to the values in rowsize, and types that correspond to the type of ragged_array


Unpacking longitude arrays from a ragged Xarray Dataset:

lon = unpack(ds.lon, ds["rowsize"]) # return a list[xr.DataArray] (slower)
lon = unpack(ds.lon.values, ds["rowsize"]) # return a list[np.ndarray] (faster)
first_lon = unpack(ds.lon.values, ds["rowsize"], rows=0) # return only the first row
first_two_lons = unpack(ds.lon.values, ds["rowsize"], rows=[0, 1]) # return first two rows

Looping over trajectories in a ragged Xarray Dataset to compute velocities for each:

for lon, lat, time in list(zip(
    unpack(ds.lon.values, ds["rowsize"]),
    unpack(, ds["rowsize"]),
    unpack(ds.time.values, ds["rowsize"])
    u, v = velocity_from_position(lon, lat, time)