seaduck.eulerian

seaduck.eulerian#

class seaduck.eulerian.Position(*arg, **kwarg)#

Bases: object

The Position object that performs the interpolation.

Create a empty one by default. To actually do interpolation, use from_latlon method to tell the ducks where they are.

fatten(knw, four_d=False, required='all', ind_moves_kwarg={})#

Fatten in all the required dimensions.

Finding the neighboring center grid points in all 4 dimensions.

Parameters:

  • knw (KnW object) – The kernel used to find neighboring points.

  • four_d (Boolean, default False) – When we are doing nearest neighbor interpolation on some of the dimensions, with four_d = True, this will create dimensions with length 1, and will squeeze the dimension if four_d = False

  • required (str, iterable of str, default "all") – Which dims is needed in the process

  • ind_moves_kward (dict, optional) – Key word argument to put into ind_moves method of the Topology object. Read Topology.ind_moves for more detail.

from_bool_array(t=None, data=None, bool_array=None, num=None, random_seed=None)#

Update/Generate new object with random points in given grid boxes.

Use the methods from the ocedata to transform from lat-lon-dep-time coords to rel-coords store the output in the Position object.

Parameters:

  • t (numpy.ndarray, float or None, default None) – 1D array of the time coords

  • data (OceData object) – The field where the Positions are defined on.

  • bool_array (numpy.ndarray, or xr.DataArray) – Points are generated where it is True.

  • num (int) – Total number of particles to seed (approximately).

  • random_seed (int optional) – The random seed used for reproducible results.

from_latlon(x=None, y=None, z=None, t=None, data=None)#

Fill in the coord info using lat-lon-dep-time dims.

Use the methods from the ocedata to transform from lat-lon-dep-time coords to rel-coords store the output in the Position object.

Parameters:

  • x (numpy.ndarray, float or None, default None) – 1D array of the lat-lon-dep-time coords

  • y (numpy.ndarray, float or None, default None) – 1D array of the lat-lon-dep-time coords

  • z (numpy.ndarray, float or None, default None) – 1D array of the lat-lon-dep-time coords

  • t (numpy.ndarray, float or None, default None) – 1D array of the lat-lon-dep-time coords

  • data (OceData object) – The field where the Positions are defined on.

get_f_node_weight()#

Find weight for the corner points interpolation.

get_px_py()#

Get the nearest 4 corner points of the given point.

Used for oceanparcel style horizontal interpolation.

Returns:

  • px (numpy.ndarray) – the longitude of the Position’s surrounding 4 corner points.

  • py (numpy.ndarray) – the latitude of those points mentioned above.

interpolate(var_name, knw, vec_transform=True, prefetched=None, prefetch_prefix=None)#

Do interpolation.

This is the method that does the actual interpolation/derivative. It is a combination of the following methods: _register_interpolation_input, _fatten_required_index_and_register, _transform_vector_and_register, _read_data_and_register, _mask_value_and_register, _compute_weight_and_registe,.

Parameters:

  • var_name (list, str, tuple) – The variables to interpolate. Tuples are used for horizontal vectors. Put str and list in a list if you have multiple things to interpolate. This input also defines the format of the output.

  • knw (KnW object, tuple, list, dict) – The kernel object used for the operation. Put them in the same order as var_name. Some level of automatic broadcasting is also supported.

  • vec_transform (Boolean) – Whether to project the vector field to the local zonal/meridional direction.

  • prefetched (numpy.ndarray, tuple, list, dict, None, default None) – The prefetched array for the data, this will effectively overwrite var_name. Put them in the same order as var_name. Some level of automatic broadcasting is also supported.

  • prefetch_prefix (tuple, list, dict, None, default None) – The prefix of the prefetched array. Put them in the same order as var_name. Some level of automatic broadcasting is also supported.

Returns:

to_return – The interpolation/derivative output in the same format as var_name.

Return type:

list, numpy.array, tuple

subset(which)#

Create a subset of the Position object.

Parameters:

which (numpy.ndarray, optional) – Define which points survive the subset operation. It be an array of either boolean or int. The selection is similar to that of selecting from a 1D numpy array.

Returns:

the_subset – The selected Positions.

Return type:

Position object

update_from_subset(sub, which)#

Update from the original one from a subset of the Position object.

Parameters:

  • sub (Position object) – The Position object to be updated from.

  • which (numpy.ndarray, optional) – Define which points correpond to the subset It be an array of either boolean or int. The selection is similar to that of selecting from a 1D numpy array.