FmmTree

ferreus_bbfmm.FmmTree(source_points, interpolation_order, kernel_params, adaptive_tree, sparse, extents, params)

A Fast Multipole Method (FMM) tree that organises source points into a hierarchical spatial structure to accelerate kernel summation tasks.

The tree supports both adaptive and uniform refinement, with optional sparse leaf pruning. It efficiently precomputes all operators (M2M and M2L) required for far-field approximation.

Parameters:

Name	Type	Description	Default
source_points	NDArray[float64]	Source point locations used to build the tree. Expected to be a numpy array with shape (N, D), where N is the number of points and D is the dimensionality.	required
interpolation_order	int	Number of Chebyshev interpolation nodes per dimension.	required
kernel_params	FmmParams	KernelParams that define the kernel function used for interaction computations.	required
adaptive_tree	bool	Whether the tree uses adaptive or uniform subdivision.	required
sparse	bool	If True, constructs a sparse tree that omits empty leaves.	required
extents	Optional[NDArray[float64]]	Optional bounding box [xmin, xmax, ymin, ymax, ...]; if None, computed from data.	required
params	Optional[FmmParams]	Optional parameters for tuning the FMM performance.	required

set_weights(weights)

Performs an upward pass of the tree to set the multipole coefficients.

Parameters:

Name	Type	Description	Default
weights	NDArray[float64]	Numpy array of shape (N, K), where N is the number of source points and K is the number of right-hand sides to evaluate, containing source point weights (values)	required

evaluate(weights, target_points)

Performs a downward pass of the tree to set the local coefficients and then performs a leaf evaluation pass to evaluate the values at the target locations.

Parameters:

Name	Type	Description	Default
weights	NDArray[float64]	Numpy array of shape (N, K), where N is the number of source points and K is the number of right-hand sides to evaluate, containing source point weights (values)	required
target_points	NDArray[float64]	Numpy array of shape (N, D), where N is the number of target points and D is the dimensionality.	required

set_local_coefficients(weights)

Performs a downward pass of the tree to set the local coefficients. Intended to be used before calling ['FmmTree.evaluate_leaves`].

Parameters:

Name	Type	Description	Default
weights	NDArray[float64]	Numpy array of shape (N, K), where N is the number of source points and K is the number of right-hand sides to evaluate, containing source point weights (values)	required

evaluate_leaves(weights, target_points)

Performs a leaf evaluation pass to calculate the values at the target locations. Intended to be used after set_local_coefficients, for when repeated calls to this function are desired, such as when using 'surface following' isosurface generation algorithms.

Parameters:

Name	Type	Description	Default
weights	NDArray[float64]	Numpy array of shape (N, K), where N is the number of source points and K is the number of right-hand sides to evaluate, containing source point weights (values)	required
target_points	NDArray[float64]	Numpy array of shape (N, D), where N is the number of target points and D is the dimensionality.	required

target_values()

Values at target locations after evaluate or evaluate_leaves is called.

Returns:

Type	Description
NDArray[float64]	Returns a numpy array with shape (N, K), where N is the number of target points and K is the number of right-hand-sides evaluated.