Discretizations

QBX discretization

To compute a layer potential as an an end user, create a meshmode.discretization.Discretization with a InterpolatoryQuadratureSimplexGroupFactory as a discretization for the density.

Then create pytential.qbx.QBXLayerPotentialSource, pytential.bind() a layer potential operator to it, and you can start computing.

class pytential.qbx.QBXLayerPotentialSource(density_discr, fine_order, qbx_order=None, fmm_order=None, fmm_level_to_order=None, to_refined_connection=None, expansion_factory=None, target_association_tolerance=<class 'pytential.qbx._not_provided'>, debug=True, _refined_for_global_qbx=False, _expansions_in_tree_have_extent=True, _expansion_stick_out_factor=0.5, _well_sep_is_n_away=2, _max_leaf_refine_weight=None, _box_extent_norm=None, _from_sep_smaller_crit=None, _from_sep_smaller_min_nsources_cumul=None, _tree_kind='adaptive', geometry_data_inspector=None, fmm_backend='sumpy', target_stick_out_factor=<class 'pytential.qbx._not_provided'>)[source]

A source discretization for a QBX layer potential.

qbx_order
fmm_order

See QBX internals for some information on the inner workings of this.

Parameters:
  • fine_order – The total degree to which the (upsampled) underlying quadrature is exact.
  • to_refined_connection – A connection used for resampling from density_discr the fine density discretization. It is assumed that the fine density discretization given by to_refined_connection.to_discr is not already upsampled. May be None.
  • fmm_orderFalse for direct calculation. May not be given if fmm_level_to_order is given.
  • fmm_level_to_order – A function that takes arguments of (kernel, kernel_args, tree, level) and returns the expansion order to be used on a given level of tree with kernel, where kernel is the sumpy.kernel.Kernel being evaluated, and kernel_args is a set of (key, value) tuples with evaluated kernel arguments. May not be given if fmm_order is given.
class pytential.qbx.QBXTargetAssociationFailedException(refine_flags, failed_target_flags, message)[source]
refine_flags
failed_target_flags

Unregularized discretization

class pytential.unregularized.UnregularizedLayerPotentialSource(density_discr, fmm_order=False, fmm_level_to_order=None, expansion_factory=None, debug=True)[source]

A source discretization for a layer potential discretized with a Nyström method that uses panel-based quadrature and does not modify the kernel.

fmm_level_to_order
Parameters:fmm_orderFalse for direct calculation.

Sources

class pytential.source.PotentialSource[source]
preprocess_optemplate(name, discretizations, expr)[source]
op_group_features(expr)

Return a characteristic tuple by which operators that can be executed together can be grouped.

expr is a subclass of pytential.symbolic.primitives.IntG.

class pytential.source.PointPotentialSource(cl_context, nodes)[source]
nodes[source]

An pyopencl.array.Array of shape [ambient_dim, nnodes].

nnodes
class pytential.source.LayerPotentialSourceBase[source]

A discretization of a layer potential using panel-based geometry, with support for refinement and upsampling.

Discretizations

density_discr
stage2_density_discr
quad_stage2_density_discr
resampler
with_refinement()

Discretization data

cl_context
ambient_dim
dim
real_dtype
complex_dtype
h_max

Execution

weights_and_area_elements()
exec_compute_potential_insn()

Targets

Target discretizations are a simpler version of the full pytential.discretization.Discretization interface. They do not provide any evaluation of integrals, norms, or layer potentials, but are instead only geared towards being used as evaluation targets.

class pytential.target.TargetBase[source]
ambient_dim
nodes()

Shape: [ambient_dim, nnodes]

nnodes
class pytential.target.PointsTarget(nodes, normals=None)[source]

The point of this class is to act as a container for some target points while presenting enough of the meshmode.discretization.Discretization interface to not necessitate a lot of special cases in that code path.