Module nnps: Nearest Neighbor Particle Search¶
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class
pysph.base.nnps_base.
CPUDomainManager
(double xmin=-1000, double xmax=1000, double ymin=0, double ymax=0, double zmin=0, double zmax=0, periodic_in_x=False, periodic_in_y=False, periodic_in_z=False, double n_layers=2.0, backend=None, props=None, mirror_in_x=False, mirror_in_y=False, mirror_in_z=False)¶ Bases:
pysph.base.nnps_base.DomainManagerBase
This class determines the limits of the solution domain.
We expect all simulations to have well defined domain limits beyond which we are either not interested or the solution is invalid to begin with. Thus, if a particle leaves the domain, the solution should be considered invalid (at least locally).
The initial domain limits could be given explicitly or asked to be computed from the particle arrays. The domain could be periodic.
Constructor
The n_layers argument specifies the number of ghost layers as multiples of hmax*radius_scale.
- props: list/dict: properties to copy.
- Provide a list or dict with the keys as particle array names. Only the specified properties are copied. If not specified, all props are copied.
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dtype
¶ object
Type: dtype
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dtype_max
¶ ‘double’
Type: dtype_max
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ghosts
¶ list
Type: ghosts
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update
(self)¶ General method that is called before NNPS can bin particles.
This method is responsible for the computation of cell sizes and creation of any ghost particles for periodic or wall boundary conditions.
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use_double
¶ ‘bool’
Type: use_double
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class
pysph.base.nnps_base.
Cell
(IntPoint cid, double cell_size, int narrays, int layers=2)¶ Bases:
object
Basic indexing structure for the box-sort NNPS.
For a spatial indexing based on the box-sort algorithm, this class defines the spatial data structure used to hold particle indices (local and global) that are within this cell.
Constructor
Parameters: - cid (IntPoint) – Spatial index (unflattened) for the cell
- cell_size (double) – Spatial extent of the cell in each dimension
- narrays (int) – Number of arrays being binned
- layers (int) – Factor to compute the bounding box
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centroid
¶ ‘cPoint’
Type: centroid
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get_bounding_box
(self, Point boxmin, Point boxmax, int layers=1, cell_size=None)¶ Compute the bounding box for the cell.
Parameters: - boxmin (Point (output)) – The bounding box min coordinates are stored here
- boxmax (Point (output)) – The bounding box max coordinates are stored here
- layers (int (input) default (1)) – Number of offset layers to define the bounding box
- cell_size (double (input) default (None)) – Optional cell size to use to compute the bounding box. If not provided, the cell’s size will be used.
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get_centroid
(self, Point pnt)¶ Utility function to get the centroid of the cell.
Parameters: - pnt (Point (input/output)) – The centroid is cmoputed and stored in this object.
- centroid is defined as the origin plus half the cell size (The) –
- each dimension. (in) –
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gindices
¶ list
Type: gindices
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is_boundary
¶ ‘bool’
Type: is_boundary
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lindices
¶ list
Type: lindices
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set_indices
(self, int index, UIntArray lindices, UIntArray gindices)¶ Set the global and local indices for the cell
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size
¶ ‘double’
Type: size
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class
pysph.base.nnps_base.
DomainManager
(double xmin=-1000, double xmax=1000, double ymin=0, double ymax=0, double zmin=0, double zmax=0, periodic_in_x=False, periodic_in_y=False, periodic_in_z=False, double n_layers=2.0, backend=None, props=None, mirror_in_x=False, mirror_in_y=False, mirror_in_z=False)¶ Bases:
object
Constructor
Parameters: - xmax, ymin, ymax, zmin, zmax (xmin,) –
- periodic_in_y, periodic_in_z (periodic_in_x,) –
- mirror_in_y, mirror_in_z (mirror_in_x,) –
- n_layers (double: number of ghost layers as a multiple of) – h_max*radius_scale
- props (list/dict: properties to copy.) – Provide a list or dict with the keys as particle array names. Only the specified properties are copied. If not specified, all props are copied.
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backend
¶ object
Type: backend
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compute_cell_size_for_binning
(self)¶ Compute the cell size for the binning.
The cell size is chosen as the kernel radius scale times the maximum smoothing length in the local processor. For parallel runs, we would need to communicate the maximum ‘h’ on all processors to decide on the appropriate binning size.
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manager
¶ object
Type: manager
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set_cell_size
(self, cell_size)¶
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set_in_parallel
(self, in_parallel)¶
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set_pa_wrappers
(self, wrappers)¶
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set_radius_scale
(self, radius_scale)¶
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update
(self)¶
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class
pysph.base.nnps_base.
DomainManagerBase
(double xmin=-1000, double xmax=1000, double ymin=0, double ymax=0, double zmin=0, double zmax=0, periodic_in_x=False, periodic_in_y=False, periodic_in_z=False, double n_layers=2.0, props=None, mirror_in_x=False, mirror_in_y=False, mirror_in_z=False)¶ Bases:
object
Constructor
The n_layers argument specifies the number of ghost layers as multiples of hmax*radius_scale.
- props: list/dict: properties to copy.
- Provide a list or dict with the keys as particle array names. Only the specified properties are copied. If not specified, all props are copied.
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cell_size
¶ ‘double’
Type: cell_size
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compute_cell_size_for_binning
(self)¶
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copy_props
¶ list
Type: copy_props
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dim
¶ ‘int’
Type: dim
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hmin
¶ ‘double’
Type: hmin
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in_parallel
¶ ‘bool’
Type: in_parallel
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is_mirror
¶ ‘bool’
Type: is_mirror
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is_periodic
¶ ‘bool’
Type: is_periodic
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mirror_in_x
¶ ‘bool’
Type: mirror_in_x
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mirror_in_y
¶ ‘bool’
Type: mirror_in_y
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mirror_in_z
¶ ‘bool’
Type: mirror_in_z
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n_layers
¶ ‘double’
Type: n_layers
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narrays
¶ ‘int’
Type: narrays
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pa_wrappers
¶ list
Type: pa_wrappers
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periodic_in_x
¶ ‘bool’
Type: periodic_in_x
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periodic_in_y
¶ ‘bool’
Type: periodic_in_y
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periodic_in_z
¶ ‘bool’
Type: periodic_in_z
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props
¶ object
Type: props
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radius_scale
¶ ‘double’
Type: radius_scale
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set_cell_size
(self, cell_size)¶
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set_in_parallel
(self, bool in_parallel)¶
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set_pa_wrappers
(self, wrappers)¶
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set_radius_scale
(self, double radius_scale)¶
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xmax
¶ ‘double’
Type: xmax
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xmin
¶ ‘double’
Type: xmin
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xtranslate
¶ ‘double’
Type: xtranslate
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ymax
¶ ‘double’
Type: ymax
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ymin
¶ ‘double’
Type: ymin
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ytranslate
¶ ‘double’
Type: ytranslate
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zmax
¶ ‘double’
Type: zmax
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zmin
¶ ‘double’
Type: zmin
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ztranslate
¶ ‘double’
Type: ztranslate
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class
pysph.base.nnps_base.
NNPS
(int dim, list particles, double radius_scale=2.0, int ghost_layers=1, domain=None, bool cache=False, bool sort_gids=False)¶ Bases:
pysph.base.nnps_base.NNPSBase
Nearest neighbor query class using the box-sort algorithm.
NNPS bins all local particles using the box sort algorithm in Cells. The cells are stored in a dictionary ‘cells’ which is keyed on the spatial index (IntPoint) of the cell.
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current_cache
¶ pysph.base.nnps_base.NeighborCache
Type: current_cache
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get_spatially_ordered_indices
(self, int pa_index, LongArray indices)¶
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set_in_parallel
(self, bool in_parallel)¶
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set_use_cache
(self, bool use_cache)¶
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sort_gids
¶ ‘bool’
Type: sort_gids
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spatially_order_particles
(self, int pa_index)¶ Spatially order particles such that nearby particles have indices nearer each other. This may improve pre-fetching on the CPU.
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update
(self)¶ Update the local data after particles have moved.
For parallel runs, we want the NNPS to be independent of the ParallelManager which is solely responsible for distributing particles across available processors. We assume therefore that after a parallel update, each processor has all the local particle information it needs and this operation is carried out locally.
For serial runs, this method should be called when the particles have moved.
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update_domain
(self)¶
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xmax
¶ cyarray.carray.DoubleArray
Type: xmax
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xmin
¶ cyarray.carray.DoubleArray
Type: xmin
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class
pysph.base.nnps_base.
NNPSBase
(int dim, list particles, double radius_scale=2.0, int ghost_layers=1, domain=None, bool cache=False, bool sort_gids=False)¶ Bases:
object
Constructor for NNPS
Parameters: - dim (int) – Dimension (fixme: Not sure if this is really needed)
- particles (list) – The list of particle arrays we are working on.
- radius_scale (double, default (2)) – Optional kernel radius scale. Defaults to 2
- domain (DomainManager, default (None)) – Optional limits for the domain
- cache (bint) – Flag to set if we want to cache neighbor calls. This costs storage but speeds up neighbor calculations.
- sort_gids (bint, default (False)) – Flag to sort neighbors using gids (if they are available). This is useful when comparing parallel results with those from a serial run.
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brute_force_neighbors
(self, int src_index, int dst_index, size_t d_idx, UIntArray nbrs)¶
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cache
¶ list
Type: cache
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cell_size
¶ ‘double’
Type: cell_size
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dim
¶ ‘int’
Type: dim
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domain
¶ pysph.base.nnps_base.DomainManager
Type: domain
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get_nearest_particles
(self, int src_index, int dst_index, size_t d_idx, UIntArray nbrs)¶
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get_nearest_particles_no_cache
(self, int src_index, int dst_index, size_t d_idx, UIntArray nbrs, bool prealloc)¶ Find nearest neighbors for particle id ‘d_idx’ without cache
Parameters: - src_index (int) – Index in the list of particle arrays to which the neighbors belong
- dst_index (int) – Index in the list of particle arrays to which the query point belongs
- d_idx (size_t) – Index of the query point in the destination particle array
- nbrs (UIntArray) – Array to be populated by nearest neighbors of ‘d_idx’
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hmin
¶ ‘double’
Type: hmin
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is_periodic
¶ ‘bool’
Type: is_periodic
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n_cells
¶ ‘int’
Type: n_cells
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narrays
¶ ‘int’
Type: narrays
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pa_wrappers
¶ list
Type: pa_wrappers
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particles
¶ list
Type: particles
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radius_scale
¶ ‘double’
Type: radius_scale
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set_context
(self, int src_index, int dst_index)¶ Setup the context before asking for neighbors. The dst_index represents the particles for whom the neighbors are to be determined from the particle array with index src_index.
Parameters: - src_index (int: the source index of the particle array.) –
- dst_index (int: the destination index of the particle array.) –
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class
pysph.base.nnps_base.
NNPSParticleArrayWrapper
(ParticleArray pa)¶ Bases:
object
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gid
¶ cyarray.carray.UIntArray
Type: gid
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h
¶ cyarray.carray.DoubleArray
Type: h
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name
¶ unicode
Type: name
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np
¶ ‘int’
Type: np
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pa
¶ pysph.base.particle_array.ParticleArray
Type: pa
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remove_tagged_particles
(self, int tag)¶
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tag
¶ cyarray.carray.IntArray
Type: tag
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x
¶ cyarray.carray.DoubleArray
Type: x
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y
¶ cyarray.carray.DoubleArray
Type: y
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z
¶ cyarray.carray.DoubleArray
Type: z
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class
pysph.base.nnps_base.
NeighborCache
(NNPS nnps, int dst_index, int src_index)¶ Bases:
object
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find_all_neighbors
(self)¶
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get_neighbors
(self, int src_index, size_t d_idx, UIntArray nbrs)¶
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update
(self)¶
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pysph.base.nnps_base.
arange_uint
(int start, int stop=-1) → UIntArray¶ Utility function to return a numpy.arange for a UIntArray
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pysph.base.nnps_base.
get_centroid
(double cell_size, IntPoint cid)¶ Get the centroid of the cell.
Parameters: - cell_size (double (input)) – Cell size used for binning
- cid (IntPoint (input)) – Spatial index for a cell
Returns: centroid
Return type: Point
Notes
The centroid in any coordinate direction is defined to be the origin plus half the cell size in that direction
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pysph.base.nnps_base.
get_number_of_threads
() → int¶
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pysph.base.nnps_base.
py_flatten
(IntPoint cid, IntArray ncells_per_dim, int dim)¶ Python wrapper
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pysph.base.nnps_base.
py_get_valid_cell_index
(IntPoint cid, IntArray ncells_per_dim, int dim, int n_cells)¶ Return the flattened cell index for a valid cell
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pysph.base.nnps_base.
py_unflatten
(long cell_index, IntArray ncells_per_dim, int dim)¶ Python wrapper
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pysph.base.nnps_base.
set_number_of_threads
(int n)¶
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class
pysph.base.linked_list_nnps.
LinkedListNNPS
(int dim, list particles, double radius_scale=2.0, int ghost_layers=1, domain=None, bool fixed_h=False, bool cache=False, bool sort_gids=False)¶ Bases:
pysph.base.nnps_base.NNPS
Nearest neighbor query class using the linked list method.
Constructor for NNPS
Parameters: - dim (int) – Number of dimension.
- particles (list) – The list of particle arrays we are working on
- radius_scale (double, default (2)) – Optional kernel radius scale. Defaults to 2
- ghost_layers (int) – Optional number of layers to share in parallel
- domain (DomainManager, default (None)) – Optional limits for the domain
- fixed_h (bint) – Optional flag to use constant cell sizes throughout.
- cache (bint) – Flag to set if we want to cache neighbor calls. This costs storage but speeds up neighbor calculations.
- sort_gids (bint, default (False)) – Flag to sort neighbors using gids (if they are available). This is useful when comparing parallel results with those from a serial run.
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fixed_h
¶ ‘bool’
Type: fixed_h
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get_spatially_ordered_indices
(self, int pa_index, LongArray indices)¶
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heads
¶ list
Type: heads
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ncells_per_dim
¶ cyarray.carray.IntArray
Type: ncells_per_dim
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ncells_tot
¶ ‘int’
Type: ncells_tot
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nexts
¶ list
Type: nexts
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set_context
(self, int src_index, int dst_index)¶ Setup the context before asking for neighbors. The dst_index represents the particles for whom the neighbors are to be determined from the particle array with index src_index.
Parameters: - src_index (int: the source index of the particle array.) –
- dst_index (int: the destination index of the particle array.) –
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class
pysph.base.box_sort_nnps.
BoxSortNNPS
¶ Bases:
pysph.base.linked_list_nnps.LinkedListNNPS
Nearest neighbor query class using the box sort method but which uses the LinkedList algorithm. This makes this very fast but perhaps not as safe as the DictBoxSortNNPS. All this class does is to use a std::map to obtain a linear cell index from the actual flattened cell index.
Constructor for NNPS
Parameters: - dim (int) – Number of dimension.
- particles (list) – The list of particle arrays we are working on
- radius_scale (double, default (2)) – Optional kernel radius scale. Defaults to 2
- ghost_layers (int) – Optional number of layers to share in parallel
- domain (DomainManager, default (None)) – Optional limits for the domain
- fixed_h (bint) – Optional flag to use constant cell sizes throughout.
- cache (bint) – Flag to set if we want to cache neighbor calls. This costs storage but speeds up neighbor calculations.
- sort_gids (bint, default (False)) – Flag to sort neighbors using gids (if they are available). This is useful when comparing parallel results with those from a serial run.
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cell_to_index
¶ ‘map[long,int]’
Type: cell_to_index
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class
pysph.base.box_sort_nnps.
DictBoxSortNNPS
(int dim, list particles, double radius_scale=2.0, int ghost_layers=1, domain=None, cache=False, sort_gids=False)¶ Bases:
pysph.base.nnps_base.NNPS
Nearest neighbor query class using the box-sort algorithm using a dictionary.
NNPS bins all local particles using the box sort algorithm in Cells. The cells are stored in a dictionary ‘cells’ which is keyed on the spatial index (IntPoint) of the cell.
Constructor for NNPS
Parameters: - dim (int) – Number of dimensions.
- particles (list) – The list of particle arrays we are working on.
- radius_scale (double, default (2)) – Optional kernel radius scale. Defaults to 2
- domain (DomainManager, default (None)) – Optional limits for the domain
- cache (bint) – Flag to set if we want to cache neighbor calls. This costs storage but speeds up neighbor calculations.
- sort_gids (bint, default (False)) – Flag to sort neighbors using gids (if they are available). This is useful when comparing parallel results with those from a serial run.
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cells
¶ dict
Type: cells
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get_nearest_particles_no_cache
(self, int src_index, int dst_index, size_t d_idx, UIntArray nbrs, bool prealloc)¶ Utility function to get near-neighbors for a particle.
Parameters: - src_index (int) – Index of the source particle array in the particles list
- dst_index (int) – Index of the destination particle array in the particles list
- d_idx (int (input)) – Destination particle for which neighbors are sought.
- nbrs (UIntArray (output)) – Neighbors for the requested particle are stored here.
- prealloc (bool) – Specifies if the neighbor array already has pre-allocated space for the neighbor list. In this case the neighbors are directly set in the given array without resetting or appending to the array. This improves performance when the neighbors are cached.
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class
pysph.base.spatial_hash_nnps.
ExtendedSpatialHashNNPS
(int dim, list particles, double radius_scale=2.0, int H=3, int ghost_layers=1, domain=None, bool fixed_h=False, bool cache=False, bool sort_gids=False, long long table_size=131072, bool approximate=False)¶ Bases:
pysph.base.nnps_base.NNPS
Finds nearest neighbors using Extended Spatial Hashing algorithm
Sub-divides each cell into smaller ones. Useful when particles cluster in a cell.
For approximate Extended Spatial Hash, if the distance between a cell and the cell of the query particle is greater than search radius, the entire cell is ignored.
Ref. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.105.6732&rep=rep1&type=pdf
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set_context
(self, int src_index, int dst_index)¶ Set context for nearest neighbor searches.
Parameters: - src_index (int) – Index in the list of particle arrays to which the neighbors belong
- dst_index (int) – Index in the list of particle arrays to which the query point belongs
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class
pysph.base.spatial_hash_nnps.
SpatialHashNNPS
(int dim, list particles, double radius_scale=2.0, int ghost_layers=1, domain=None, bool fixed_h=False, bool cache=False, bool sort_gids=False, long long table_size=131072)¶ Bases:
pysph.base.nnps_base.NNPS
Nearest neighbor particle search using Spatial Hashing algorithm
Uses a hashtable to store particles according to cell it belongs to.
Ref. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.105.6732&rep=rep1&type=pdf
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set_context
(self, int src_index, int dst_index)¶ Set context for nearest neighbor searches.
Parameters: - src_index (int) – Index in the list of particle arrays to which the neighbors belong
- dst_index (int) – Index in the list of particle arrays to which the query point belongs
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