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base.py
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base.py
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from abc import ABC, abstractmethod
import numpy as np
from parcels.interaction.neighborsearch.distanceutils import spherical_distance
class BaseNeighborSearch(ABC):
"""Base class for searching particles in the neighborhood.
The data structure of the class (and subclasses) only contain spatial
information. Additionally its input is in array format (3, n_particles),
which makes it the most efficient with the SoA (structure of arrays) data
structure.
"""
def __init__(self, inter_dist_vert, inter_dist_horiz,
max_depth=100000, periodic_domain_zonal=None):
"""Initialize neighbor search
:param inter_dist_vert: interaction distance (vertical) in m
:param inter_dist_horiz: interaction distance (horizontal in m
:param max_depth: maximum depth of the particles (i.e. 100km)
:param zperiodic_bc_domain: zonal domain if zonal periodic boundary
"""
self.inter_dist_vert = inter_dist_vert
self.inter_dist_horiz = inter_dist_horiz
self.inter_dist = np.array(
[inter_dist_vert, inter_dist_horiz, inter_dist_horiz]
).reshape(3, 1)
self.max_depth = max_depth # Maximum depth of particles.
self._values = None # Coordinates of the particles.
# Boolean array denoting active particles.
# These are particles 1) already started at the current time and
# 2) are set to a positive state (Success/Evaluate).
# Thus, this mask allows for particles do be deactivated without
# needing to completely rebuild the tree.
self._active_mask = None
self.periodic_domain_zonal = periodic_domain_zonal
@abstractmethod
def find_neighbors_by_coor(self, coor):
'''Get the neighbors around a certain location.
:param coor: Numpy array with [depth, lat, lon].
:returns List of particle indices.
'''
raise NotImplementedError
def find_neighbors_by_idx(self, particle_idx):
'''Get the neighbors around a certain particle.
Mainly useful for Structure of Array (SoA) datastructure
:param particle_idx: index of the particle (SoA).
:returns List of particle indices
'''
coor = self._values[:, particle_idx].reshape(3, 1)
return self.find_neighbors_by_coor(coor)
def update_values(self, new_values, new_active_mask=None):
'''Update the coordinates of the particles.
This is a default implementation simply rebuilds the structure.
If the rebuilding is slow, a faster implementation can be provided.
:param new_values: numpy array ([depth, lat, lon], n_particles) with
new coordinates of the particles.
:param new_active_mask: boolean array indicating active particles.
'''
self.rebuild(new_values, new_active_mask)
def rebuild(self, values, active_mask=-1):
"""Rebuild the neighbor structure from scratch.
:param values: numpy array with coordinates of particles
(same as update).
:param active_mask: boolean array indicating active particles.
"""
if values is not None:
self._values = values
if active_mask is None:
self._active_mask = np.arange(self._values.shape[1])
# If active_mask == -1, then don't update the active mask.
if not (isinstance(active_mask, int) and active_mask == -1):
self._active_mask = active_mask
self._active_idx = self.active_idx
@property
def active_idx(self):
"Indices of the currently active mask."
# See __init__ comments for a more detailed explanation.
if self._active_mask is None:
return np.arange(self._values.shape[1])
return np.where(self._active_mask)[0]
@abstractmethod
def _distance(self, coor, subset_idx):
"""Distance between a coordinate and particles
Distance depends on the mesh (spherical/flat).
:param coor: Numpy array with 3D coordinates ([depth, lat, lon]).
:param subset_idx: Indices of the particles to compute the distance to.
:returns horiz_dist: distance in the horizontal direction
:returns vert_dist: distance in the vertical direction.
"""
raise NotImplementedError
def _get_close_neighbor_dist(self, coor, subset_idx):
"""Compute distances and remove non-neighbors.
:param coor: Numpy array with 3D coordinates ([depth, lat, lon]).
:param subset_idx: Indices of the particles to compute the distance to.
:returns neighbor_idx: Indices within the interaction distance.
:returns distances: Distance between coor and the neighbor particles.
"""
vert_distance, horiz_distance = self._distance(coor, subset_idx)
rel_distances = np.sqrt((horiz_distance/self.inter_dist_horiz)**2
+ (vert_distance/self.inter_dist_vert)**2)
rel_neighbor_idx = np.where(rel_distances < 1)[0]
neighbor_idx = subset_idx[rel_neighbor_idx]
distances = np.vstack((vert_distance[rel_neighbor_idx],
horiz_distance[rel_neighbor_idx]))
return neighbor_idx, distances
class BaseFlatNeighborSearch(BaseNeighborSearch):
"Base class for neighbor searches with a flat mesh."
def _distance(self, coor, subset_idx):
coor = coor.reshape(3, 1)
horiz_distance = np.sqrt(np.sum((
self._values[1:, subset_idx] - coor[1:])**2,
axis=0))
if self.periodic_domain_zonal:
# If zonal periodic boundaries
coor[2, 0] -= self.periodic_domain_zonal
# distance through Western boundary
hd2 = np.sqrt(np.sum((
self._values[1:, subset_idx] - coor[1:])**2,
axis=0))
coor[2, 0] += 2*self.periodic_domain_zonal
# distance through Eastern boundary
hd3 = np.sqrt(np.sum((
self._values[1:, subset_idx] - coor[1:])**2,
axis=0))
coor[2, 0] -= self.periodic_domain_zonal
else:
hd2 = np.full(len(horiz_distance), np.inf)
hd3 = np.full(len(horiz_distance), np.inf)
horiz_distance = np.column_stack((horiz_distance, hd2, hd3))
horiz_distance = np.min(horiz_distance, axis=1)
vert_distance = np.abs(self._values[0, subset_idx]-coor[0])
return (vert_distance, horiz_distance)
class BaseSphericalNeighborSearch(BaseNeighborSearch):
"Base class for a neighbor search with a spherical mesh."
def _distance(self, coor, subset_idx):
vert_distances, horiz_distances = spherical_distance(
*coor,
self._values[0, subset_idx],
self._values[1, subset_idx],
self._values[2, subset_idx],
)
if self.periodic_domain_zonal:
# If zonal periodic boundaries
coor[2, 0] -= self.periodic_domain_zonal
# distance through Western boundary
hd2 = spherical_distance(
*coor,
self._values[0, subset_idx],
self._values[1, subset_idx],
self._values[2, subset_idx])[1]
coor[2, 0] += 2*self.periodic_domain_zonal
# distance through Eastern boundary
hd3 = spherical_distance(
*coor,
self._values[0, subset_idx],
self._values[1, subset_idx],
self._values[2, subset_idx])[1]
coor[2, 0] -= self.periodic_domain_zonal
else:
hd2 = np.full(len(horiz_distances), np.inf)
hd3 = np.full(len(horiz_distances), np.inf)
horiz_distances = np.column_stack((horiz_distances, hd2, hd3))
horiz_distances = np.min(horiz_distances, axis=1)
return (vert_distances, horiz_distances)