Particle-field interaction#

parcels.interaction module#

parcels.interaction.neighborsearch module#

class parcels.interaction.neighborsearch.BruteFlatNeighborSearch(inter_dist_vert, inter_dist_horiz, max_depth=100000, periodic_domain_zonal=None)[source]#

Bases: BaseFlatNeighborSearch

Brute force implementation to find the neighbors.

Attributes:
active_idx

Indices of the currently active mask.

Methods

find_neighbors_by_coor(coor)

Get the neighbors around a certain location.

find_neighbors_by_idx(particle_idx)

Get the neighbors around a certain particle.

rebuild(values[, active_mask])

Rebuild the neighbor structure from scratch.

update_values(new_values[, new_active_mask])

Update the coordinates of the particles.
find_neighbors_by_coor(coor)[source]#

Get the neighbors around a certain location.

Parameters:

coor – Numpy array with [depth, lat, lon].

Returns:

List of particle indices.

Return type:

type

class parcels.interaction.neighborsearch.BruteSphericalNeighborSearch(inter_dist_vert, inter_dist_horiz, max_depth=100000, periodic_domain_zonal=None)[source]#

Bases: BaseSphericalNeighborSearch

Brute force implementation to find the neighbors.

Attributes:
active_idx

Indices of the currently active mask.

Methods

find_neighbors_by_coor(coor)

Get the neighbors around a certain location.

find_neighbors_by_idx(particle_idx)

Get the neighbors around a certain particle.

rebuild(values[, active_mask])

Rebuild the neighbor structure from scratch.

update_values(new_values[, new_active_mask])

Update the coordinates of the particles.
find_neighbors_by_coor(coor)[source]#

Get the neighbors around a certain location.

Parameters:

coor – Numpy array with [depth, lat, lon].

Returns:

List of particle indices.

Return type:

type

class parcels.interaction.neighborsearch.HashFlatNeighborSearch(inter_dist_vert, inter_dist_horiz, max_depth=100000, periodic_domain_zonal=None)[source]#

Bases: BaseHashNeighborSearch, BaseFlatNeighborSearch

Neighbor search using a hashtable (similar to octtrees).

Attributes:
active_idx

Indices of the currently active mask.

Methods

consistency_check()

See if all values are in their proper place.

find_neighbors_by_coor(coor)

Get the neighbors around a certain location.

find_neighbors_by_idx(particle_idx)

Get the neighbors around a certain particle.

rebuild(values[, active_mask])

Rebuild the neighbor structure from scratch.

update_values(new_values[, new_active_mask])

Update the locations of (some) of the particles.
rebuild(values, active_mask=-1)[source]#

Rebuild the neighbor structure from scratch.

Parameters:

  • values – numpy array with coordinates of particles (same as update).

  • active_mask – boolean array indicating active particles. (Default value = -1)

update_values(new_values, new_active_mask=None)[source]#

Update the locations of (some) of the particles.

Particles that stay in the same location are computationally cheap. The order and number of the particles is assumed to remain the same.

Parameters:

  • new_values – new (depth, lat, lon) values for particles.

  • new_active_mask – (Default value = None)

class parcels.interaction.neighborsearch.HashSphericalNeighborSearch(inter_dist_vert, inter_dist_horiz, max_depth=100000)[source]#

Bases: BaseHashNeighborSearch, BaseSphericalNeighborSearch

Neighbor search using a hashtable (similar to octtrees).

Parameters:

  • inter_dist_vert (float) – Interaction distance (vertical) in m.

  • inter_dist_horiz (float) – interaction distance (horizontal) in m

  • max_depth (float, optional) – Maximum depth of the particles (default is 100000m).

Attributes:
active_idx

Indices of the currently active mask.

Methods

consistency_check()

See if all values are in their proper place.

find_neighbors_by_coor(coor)

Get the neighbors around a certain location.

find_neighbors_by_idx(particle_idx)

Get the neighbors around a certain particle.

rebuild(values[, active_mask])

Recreate the tree with new values.

update_values(new_values[, new_active_mask])

Update the locations of (some) of the particles.
rebuild(values, active_mask=-1)[source]#

Recreate the tree with new values.

Parameters:

  • values – positions of the particles.

  • active_mask – (Default value = -1)

class parcels.interaction.neighborsearch.KDTreeFlatNeighborSearch(inter_dist_vert, inter_dist_horiz, max_depth=100000, periodic_domain_zonal=None)[source]#

Bases: BaseFlatNeighborSearch

Attributes:
active_idx

Indices of the currently active mask.

Methods

find_neighbors_by_coor(coor)

Get the neighbors around a certain location.

find_neighbors_by_idx(particle_idx)

Get the neighbors around a certain particle.

rebuild([values, active_mask])

Rebuild the neighbor structure from scratch.

update_values(new_values[, new_active_mask])

Update the coordinates of the particles.
find_neighbors_by_coor(coor)[source]#

Get the neighbors around a certain location.

Parameters:

coor – Numpy array with [depth, lat, lon].

Returns:

List of particle indices.

Return type:

type

rebuild(values=None, active_mask=-1)[source]#

Rebuild the neighbor structure from scratch.

Parameters:

  • values – numpy array with coordinates of particles (same as update).

  • active_mask – boolean array indicating active particles. (Default value = -1)