Source code for pytential.qbx

# -*- coding: utf-8 -*-
from __future__ import division, absolute_import

__copyright__ = "Copyright (C) 2013 Andreas Kloeckner"

__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""

import six

import numpy as np
from pytools import memoize_method
from pytential.qbx.target_assoc import QBXTargetAssociationFailedException
from pytential.source import LayerPotentialSourceBase

import pyopencl as cl

import logging
logger = logging.getLogger(__name__)


__doc__ = """
.. autoclass:: QBXLayerPotentialSource

.. autoclass:: QBXTargetAssociationFailedException
"""


# {{{ QBX layer potential source

class _not_provided:  # noqa: N801
    pass


[docs]class QBXLayerPotentialSource(LayerPotentialSourceBase): """A source discretization for a QBX layer potential. .. attribute :: qbx_order .. attribute :: fmm_order .. automethod :: __init__ .. automethod :: copy See :ref:`qbxguts` for some information on the inner workings of this. """ # {{{ constructor / copy
[docs] def __init__(self, density_discr, fine_order, qbx_order=None, fmm_order=None, fmm_level_to_order=None, expansion_factory=None, target_association_tolerance=_not_provided, # begin experimental arguments # FIXME default debug=False once everything has matured debug=True, _disable_refinement=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", _use_target_specific_qbx=None, geometry_data_inspector=None, cost_model=None, fmm_backend="sumpy", target_stick_out_factor=_not_provided): """ :arg fine_order: The total degree to which the (upsampled) underlying quadrature is exact. :arg fmm_order: `False` for direct calculation. May not be given if *fmm_level_to_order* is given. :arg 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 :class:`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. Experimental arguments without a promise of forward compatibility: :arg _use_target_specific_qbx: Whether to use target-specific acceleration by default if possible. *None* means "use if possible". :arg cost_model: Either *None* or instance of :class:`~pytential.qbx.cost.CostModel`, used for gathering modeled costs (experimental) """ # {{{ argument processing if fine_order is None: raise ValueError("fine_order must be provided.") if qbx_order is None: raise ValueError("qbx_order must be provided.") if target_stick_out_factor is not _not_provided: from warnings import warn warn("target_stick_out_factor has been renamed to " "target_association_tolerance. " "Using target_stick_out_factor is deprecated " "and will stop working in 2018.", DeprecationWarning, stacklevel=2) if target_association_tolerance is not _not_provided: raise TypeError("May not pass both target_association_tolerance and " "target_stick_out_factor.") target_association_tolerance = target_stick_out_factor del target_stick_out_factor if target_association_tolerance is _not_provided: target_association_tolerance = float( np.finfo(density_discr.real_dtype).eps) * 1e3 if fmm_order is not None and fmm_level_to_order is not None: raise TypeError("may not specify both fmm_order and fmm_level_to_order") if _box_extent_norm is None: _box_extent_norm = "l2" if _from_sep_smaller_crit is None: # This seems to win no matter what the box extent norm is # https://gitlab.tiker.net/papers/2017-qbx-fmm-3d/issues/10 _from_sep_smaller_crit = "precise_linf" if fmm_level_to_order is None: if fmm_order is False: fmm_level_to_order = False else: def fmm_level_to_order(kernel, kernel_args, tree, level): # noqa pylint:disable=function-redefined return fmm_order if _max_leaf_refine_weight is None: if density_discr.ambient_dim == 2: # FIXME: This should be verified now that l^2 is the default. _max_leaf_refine_weight = 64 elif density_discr.ambient_dim == 3: # For static_linf/linf: https://gitlab.tiker.net/papers/2017-qbx-fmm-3d/issues/8#note_25009 # noqa # For static_l2/l2: https://gitlab.tiker.net/papers/2017-qbx-fmm-3d/issues/12 # noqa _max_leaf_refine_weight = 512 else: # Just guessing... _max_leaf_refine_weight = 64 if _from_sep_smaller_min_nsources_cumul is None: # See here for the comment thread that led to these defaults: # https://gitlab.tiker.net/inducer/boxtree/merge_requests/28#note_18661 if density_discr.dim == 1: _from_sep_smaller_min_nsources_cumul = 15 else: _from_sep_smaller_min_nsources_cumul = 30 # }}} LayerPotentialSourceBase.__init__(self, density_discr) self.fine_order = fine_order self.qbx_order = qbx_order self.fmm_level_to_order = fmm_level_to_order assert target_association_tolerance is not None self.target_association_tolerance = target_association_tolerance self.fmm_backend = fmm_backend if expansion_factory is None: from sumpy.expansion import DefaultExpansionFactory expansion_factory = DefaultExpansionFactory() self.expansion_factory = expansion_factory self.debug = debug self._disable_refinement = _disable_refinement self._expansions_in_tree_have_extent = \ _expansions_in_tree_have_extent self._expansion_stick_out_factor = _expansion_stick_out_factor self._well_sep_is_n_away = _well_sep_is_n_away self._max_leaf_refine_weight = _max_leaf_refine_weight self._box_extent_norm = _box_extent_norm self._from_sep_smaller_crit = _from_sep_smaller_crit self._from_sep_smaller_min_nsources_cumul = \ _from_sep_smaller_min_nsources_cumul self._tree_kind = _tree_kind self._use_target_specific_qbx = _use_target_specific_qbx self.geometry_data_inspector = geometry_data_inspector if cost_model is None: from pytential.qbx.cost import CostModel cost_model = CostModel() self.cost_model = cost_model
# /!\ *All* parameters set here must also be set by copy() below, # otherwise they will be reset to their default values behind your # back if the layer potential source is ever copied. (such as # during refinement)
[docs] def copy( self, density_discr=None, fine_order=None, qbx_order=None, fmm_order=_not_provided, fmm_level_to_order=_not_provided, expansion_factory=None, target_association_tolerance=_not_provided, _expansions_in_tree_have_extent=_not_provided, _expansion_stick_out_factor=_not_provided, _max_leaf_refine_weight=None, _box_extent_norm=None, _from_sep_smaller_crit=None, _tree_kind=None, _use_target_specific_qbx=_not_provided, geometry_data_inspector=None, cost_model=_not_provided, fmm_backend=None, debug=_not_provided, _disable_refinement=_not_provided, target_stick_out_factor=_not_provided, ): # {{{ argument processing if target_stick_out_factor is not _not_provided: from warnings import warn warn("target_stick_out_factor has been renamed to " "target_association_tolerance. " "Using target_stick_out_factor is deprecated " "and will stop working in 2018.", DeprecationWarning, stacklevel=2) if target_association_tolerance is not _not_provided: raise TypeError("May not pass both target_association_tolerance and " "target_stick_out_factor.") target_association_tolerance = target_stick_out_factor elif target_association_tolerance is _not_provided: target_association_tolerance = self.target_association_tolerance del target_stick_out_factor # }}} kwargs = {} if (fmm_order is not _not_provided and fmm_level_to_order is not _not_provided): raise TypeError("may not specify both fmm_order and fmm_level_to_order") elif fmm_order is not _not_provided: kwargs["fmm_order"] = fmm_order elif fmm_level_to_order is not _not_provided: kwargs["fmm_level_to_order"] = fmm_level_to_order else: kwargs["fmm_level_to_order"] = self.fmm_level_to_order # FIXME Could/should share wrangler and geometry kernels # if no relevant changes have been made. return QBXLayerPotentialSource( density_discr=density_discr or self.density_discr, fine_order=( fine_order if fine_order is not None else self.fine_order), qbx_order=qbx_order if qbx_order is not None else self.qbx_order, target_association_tolerance=target_association_tolerance, expansion_factory=( expansion_factory or self.expansion_factory), debug=( # False is a valid value here debug if debug is not _not_provided else self.debug), _disable_refinement=( # False is a valid value here _disable_refinement if _disable_refinement is not _not_provided else self._disable_refinement), _expansions_in_tree_have_extent=( # False is a valid value here _expansions_in_tree_have_extent if _expansions_in_tree_have_extent is not _not_provided else self._expansions_in_tree_have_extent), _expansion_stick_out_factor=( # 0 is a valid value here _expansion_stick_out_factor if _expansion_stick_out_factor is not _not_provided else self._expansion_stick_out_factor), _well_sep_is_n_away=self._well_sep_is_n_away, _max_leaf_refine_weight=( _max_leaf_refine_weight or self._max_leaf_refine_weight), _box_extent_norm=(_box_extent_norm or self._box_extent_norm), _from_sep_smaller_crit=( _from_sep_smaller_crit or self._from_sep_smaller_crit), _from_sep_smaller_min_nsources_cumul=( self._from_sep_smaller_min_nsources_cumul), _tree_kind=_tree_kind or self._tree_kind, _use_target_specific_qbx=(_use_target_specific_qbx if _use_target_specific_qbx is not _not_provided else self._use_target_specific_qbx), geometry_data_inspector=( geometry_data_inspector or self.geometry_data_inspector), cost_model=( # None is a valid value here cost_model if cost_model is not _not_provided else self.cost_model), fmm_backend=fmm_backend or self.fmm_backend, **kwargs)
# }}} @property @memoize_method def tree_code_container(self): from pytential.qbx.utils import TreeCodeContainer return TreeCodeContainer(self.cl_context) @property @memoize_method def refiner_code_container(self): from pytential.qbx.refinement import RefinerCodeContainer return RefinerCodeContainer(self.cl_context, self.tree_code_container) @property @memoize_method def target_association_code_container(self): from pytential.qbx.target_assoc import TargetAssociationCodeContainer return TargetAssociationCodeContainer( self.cl_context, self.tree_code_container) # {{{ internal API @memoize_method def qbx_fmm_geometry_data(self, places, name, target_discrs_and_qbx_sides): """ :arg target_discrs_and_qbx_sides: a tuple of *(discr, qbx_forced_limit)* tuples, where *discr* is a :class:`meshmode.discretization.Discretization` or :class:`pytential.target.TargetBase` instance """ from pytential.qbx.geometry import QBXFMMGeometryData return QBXFMMGeometryData(places, name, self.qbx_fmm_code_getter, target_discrs_and_qbx_sides, target_association_tolerance=self.target_association_tolerance, tree_kind=self._tree_kind, debug=self.debug) # }}} # {{{ helpers for symbolic operator processing def preprocess_optemplate(self, name, discretizations, expr): """ :arg name: The symbolic name for *self*, which the preprocessor should use to find which expressions it is allowed to modify. """ from pytential.symbolic.mappers import QBXPreprocessor return QBXPreprocessor(name, discretizations)(expr) def op_group_features(self, expr): from sumpy.kernel import AxisTargetDerivativeRemover result = ( expr.source, expr.density, AxisTargetDerivativeRemover()(expr.kernel), ) return result # }}} # {{{ internal functionality for execution def exec_compute_potential_insn(self, queue, insn, bound_expr, evaluate, return_timing_data): extra_args = {} if self.fmm_level_to_order is False: func = self.exec_compute_potential_insn_direct extra_args["return_timing_data"] = return_timing_data else: func = self.exec_compute_potential_insn_fmm def drive_fmm(wrangler, strengths, geo_data, kernel, kernel_arguments): del geo_data, kernel, kernel_arguments from pytential.qbx.fmm import drive_fmm if return_timing_data: timing_data = {} else: timing_data = None return drive_fmm(wrangler, strengths, timing_data), timing_data extra_args["fmm_driver"] = drive_fmm return self._dispatch_compute_potential_insn( queue, insn, bound_expr, evaluate, func, extra_args) def cost_model_compute_potential_insn(self, queue, insn, bound_expr, evaluate): """Using :attr:`cost_model`, evaluate the cost of executing *insn*. Cost model results are gathered in :attr:`pytential.symbolic.execution.BoundExpression.modeled_cost` along the way. :returns: whatever :meth:`exec_compute_potential_insn_fmm` returns. """ if self.fmm_level_to_order is False: raise NotImplementedError("perf modeling direct evaluations") def drive_cost_model( wrangler, strengths, geo_data, kernel, kernel_arguments): del strengths cost_model_result = ( self.cost_model(wrangler, geo_data, kernel, kernel_arguments)) from pytools.obj_array import with_object_array_or_scalar output_placeholder = with_object_array_or_scalar( wrangler.finalize_potentials, wrangler.full_output_zeros() ) return output_placeholder, cost_model_result return self._dispatch_compute_potential_insn( queue, insn, bound_expr, evaluate, self.exec_compute_potential_insn_fmm, extra_args={"fmm_driver": drive_cost_model}) def _dispatch_compute_potential_insn(self, queue, insn, bound_expr, evaluate, func, extra_args=None): if self._disable_refinement: from warnings import warn warn( "Executing global QBX without refinement. " "This is unlikely to work.") if extra_args is None: extra_args = {} return func(queue, insn, bound_expr, evaluate, **extra_args) @property @memoize_method def qbx_fmm_code_getter(self): from pytential.qbx.geometry import QBXFMMGeometryCodeGetter return QBXFMMGeometryCodeGetter(self.cl_context, self.ambient_dim, self.tree_code_container, debug=self.debug, _well_sep_is_n_away=self._well_sep_is_n_away, _from_sep_smaller_crit=self._from_sep_smaller_crit) # {{{ fmm-based execution @memoize_method def expansion_wrangler_code_container(self, fmm_kernel, out_kernels): mpole_expn_class = \ self.expansion_factory.get_multipole_expansion_class(fmm_kernel) local_expn_class = \ self.expansion_factory.get_local_expansion_class(fmm_kernel) from functools import partial fmm_mpole_factory = partial(mpole_expn_class, fmm_kernel) fmm_local_factory = partial(local_expn_class, fmm_kernel) qbx_local_factory = partial(local_expn_class, fmm_kernel) if self.fmm_backend == "sumpy": from pytential.qbx.fmm import \ QBXSumpyExpansionWranglerCodeContainer return QBXSumpyExpansionWranglerCodeContainer( self.cl_context, fmm_mpole_factory, fmm_local_factory, qbx_local_factory, out_kernels) elif self.fmm_backend == "fmmlib": from pytential.qbx.fmmlib import \ QBXFMMLibExpansionWranglerCodeContainer return QBXFMMLibExpansionWranglerCodeContainer( self.cl_context, fmm_mpole_factory, fmm_local_factory, qbx_local_factory, out_kernels) else: raise ValueError("invalid FMM backend: %s" % self.fmm_backend) def get_target_discrs_and_qbx_sides(self, insn, bound_expr): """Build the list of unique target discretizations used by the provided instruction. """ # map (name, qbx_side) to number in list target_name_and_side_to_number = {} # list of tuples (discr, qbx_side) target_discrs_and_qbx_sides = [] for o in insn.outputs: key = (o.target_name, o.qbx_forced_limit) if key not in target_name_and_side_to_number: target_name_and_side_to_number[key] = \ len(target_discrs_and_qbx_sides) target_discr = bound_expr.places.get_discretization( o.target_name.geometry, o.target_name.discr_stage) if isinstance(target_discr, LayerPotentialSourceBase): target_discr = target_discr.density_discr qbx_forced_limit = o.qbx_forced_limit if qbx_forced_limit is None: qbx_forced_limit = 0 target_discrs_and_qbx_sides.append( (target_discr, qbx_forced_limit)) return target_name_and_side_to_number, tuple(target_discrs_and_qbx_sides) def exec_compute_potential_insn_fmm(self, queue, insn, bound_expr, evaluate, fmm_driver): """ :arg fmm_driver: A function that accepts four arguments: *wrangler*, *strength*, *geo_data*, *kernel*, *kernel_arguments* :returns: a tuple ``(assignments, extra_outputs)``, where *assignments* is a list of tuples containing pairs ``(name, value)`` representing assignments to be performed in the evaluation context. *extra_outputs* is data that *fmm_driver* may return (such as timing data), passed through unmodified. """ target_name_and_side_to_number, target_discrs_and_qbx_sides = ( self.get_target_discrs_and_qbx_sides(insn, bound_expr)) geo_data = self.qbx_fmm_geometry_data( bound_expr.places, insn.source.geometry, target_discrs_and_qbx_sides) # FIXME Exert more positive control over geo_data attribute lifetimes using # geo_data.<method>.clear_cache(geo_data). # FIXME Synthesize "bad centers" around corners and edges that have # inadequate QBX coverage. # FIXME don't compute *all* output kernels on all targets--respect that # some target discretizations may only be asking for derivatives (e.g.) from pytential import bind, sym waa = bind(bound_expr.places, sym.weights_and_area_elements( self.ambient_dim, dofdesc=insn.source))(queue) strengths = waa * evaluate(insn.density).with_queue(queue) out_kernels = tuple(knl for knl in insn.kernels) fmm_kernel = self.get_fmm_kernel(out_kernels) output_and_expansion_dtype = ( self.get_fmm_output_and_expansion_dtype(fmm_kernel, strengths)) kernel_extra_kwargs, source_extra_kwargs = ( self.get_fmm_expansion_wrangler_extra_kwargs( queue, out_kernels, geo_data.tree().user_source_ids, insn.kernel_arguments, evaluate)) wrangler = self.expansion_wrangler_code_container( fmm_kernel, out_kernels).get_wrangler( queue, geo_data, output_and_expansion_dtype, self.qbx_order, self.fmm_level_to_order, source_extra_kwargs=source_extra_kwargs, kernel_extra_kwargs=kernel_extra_kwargs, _use_target_specific_qbx=self._use_target_specific_qbx) from pytential.qbx.geometry import target_state if (geo_data.user_target_to_center().with_queue(queue) == target_state.FAILED).any().get(): raise RuntimeError("geometry has failed targets") # {{{ geometry data inspection hook if self.geometry_data_inspector is not None: perform_fmm = self.geometry_data_inspector(insn, bound_expr, geo_data) if not perform_fmm: return [(o.name, 0) for o in insn.outputs] # }}} # Execute global QBX. all_potentials_on_every_target, extra_outputs = ( fmm_driver( wrangler, strengths, geo_data, fmm_kernel, kernel_extra_kwargs)) result = [] for o in insn.outputs: target_side_number = target_name_and_side_to_number[ o.target_name, o.qbx_forced_limit] target_slice = slice(*geo_data.target_info().target_discr_starts[ target_side_number:target_side_number+2]) result.append((o.name, all_potentials_on_every_target[o.kernel_index][target_slice])) return result, extra_outputs # }}} # {{{ direct execution @memoize_method def get_lpot_applier(self, kernels): # needs to be separate method for caching from pytools import any if any(knl.is_complex_valued for knl in kernels): value_dtype = self.density_discr.complex_dtype else: value_dtype = self.density_discr.real_dtype from sumpy.qbx import LayerPotential from sumpy.expansion.local import LineTaylorLocalExpansion return LayerPotential(self.cl_context, [LineTaylorLocalExpansion(knl, self.qbx_order) for knl in kernels], value_dtypes=value_dtype) @memoize_method def get_lpot_applier_on_tgt_subset(self, kernels): # needs to be separate method for caching from pytools import any if any(knl.is_complex_valued for knl in kernels): value_dtype = self.density_discr.complex_dtype else: value_dtype = self.density_discr.real_dtype from pytential.qbx.direct import LayerPotentialOnTargetAndCenterSubset from sumpy.expansion.local import VolumeTaylorLocalExpansion return LayerPotentialOnTargetAndCenterSubset( self.cl_context, [VolumeTaylorLocalExpansion(knl, self.qbx_order) for knl in kernels], value_dtypes=value_dtype) @memoize_method def get_qbx_target_numberer(self, dtype): assert dtype == np.int32 from pyopencl.scan import GenericScanKernel return GenericScanKernel( self.cl_context, np.int32, arguments="int *tgt_to_qbx_center, int *qbx_tgt_number, int *count", input_expr="tgt_to_qbx_center[i] >= 0 ? 1 : 0", scan_expr="a+b", neutral="0", output_statement=""" if (item != prev_item) qbx_tgt_number[item-1] = i; if (i+1 == N) *count = item; """) def exec_compute_potential_insn_direct(self, queue, insn, bound_expr, evaluate, return_timing_data): from pytential import bind, sym if return_timing_data: from pytential.source import UnableToCollectTimingData from warnings import warn warn( "Timing data collection not supported.", category=UnableToCollectTimingData) lpot_applier = self.get_lpot_applier(insn.kernels) p2p = None lpot_applier_on_tgt_subset = None kernel_args = {} for arg_name, arg_expr in six.iteritems(insn.kernel_arguments): kernel_args[arg_name] = evaluate(arg_expr) waa = bind(bound_expr.places, sym.weights_and_area_elements( self.ambient_dim, dofdesc=insn.source))(queue) strengths = waa * evaluate(insn.density).with_queue(queue) source_discr = bound_expr.places.get_discretization( insn.source.geometry, insn.source.discr_stage) # FIXME: Do this all at once result = [] for o in insn.outputs: source_dd = insn.source.copy(discr_stage=o.target_name.discr_stage) target_discr = bound_expr.places.get_discretization( o.target_name.geometry, o.target_name.discr_stage) density_discr = bound_expr.places.get_discretization( source_dd.geometry, source_dd.discr_stage) is_self = density_discr is target_discr if is_self: # QBXPreprocessor is supposed to have taken care of this assert o.qbx_forced_limit is not None assert abs(o.qbx_forced_limit) > 0 expansion_radii = bind(bound_expr.places, sym.expansion_radii( self.ambient_dim, dofdesc=o.target_name))(queue) centers = bind(bound_expr.places, sym.expansion_centers( self.ambient_dim, o.qbx_forced_limit, dofdesc=o.target_name))(queue) evt, output_for_each_kernel = lpot_applier( queue, target_discr.nodes(), source_discr.nodes(), centers, [strengths], expansion_radii=expansion_radii, **kernel_args) result.append((o.name, output_for_each_kernel[o.kernel_index])) else: # no on-disk kernel caching if p2p is None: p2p = self.get_p2p(insn.kernels) if lpot_applier_on_tgt_subset is None: lpot_applier_on_tgt_subset = self.get_lpot_applier_on_tgt_subset( insn.kernels) evt, output_for_each_kernel = p2p(queue, target_discr.nodes(), source_discr.nodes(), [strengths], **kernel_args) qbx_forced_limit = o.qbx_forced_limit if qbx_forced_limit is None: qbx_forced_limit = 0 target_discrs_and_qbx_sides = ((target_discr, qbx_forced_limit),) geo_data = self.qbx_fmm_geometry_data( bound_expr.places, insn.source.geometry, target_discrs_and_qbx_sides=target_discrs_and_qbx_sides) # center-related info is independent of targets # First ncenters targets are the centers tgt_to_qbx_center = ( geo_data.user_target_to_center()[geo_data.ncenters:] .copy(queue=queue) .with_queue(queue)) qbx_tgt_numberer = self.get_qbx_target_numberer( tgt_to_qbx_center.dtype) qbx_tgt_count = cl.array.empty(queue, (), np.int32) qbx_tgt_numbers = cl.array.empty_like(tgt_to_qbx_center) qbx_tgt_numberer( tgt_to_qbx_center, qbx_tgt_numbers, qbx_tgt_count, queue=queue) qbx_tgt_count = int(qbx_tgt_count.get()) if (o.qbx_forced_limit is not None and abs(o.qbx_forced_limit) == 1 and qbx_tgt_count < target_discr.nnodes): raise RuntimeError("Did not find a matching QBX center " "for some targets") qbx_tgt_numbers = qbx_tgt_numbers[:qbx_tgt_count] qbx_center_numbers = tgt_to_qbx_center[qbx_tgt_numbers] qbx_center_numbers.finish() tgt_subset_kwargs = kernel_args.copy() for i, res_i in enumerate(output_for_each_kernel): tgt_subset_kwargs["result_%d" % i] = res_i if qbx_tgt_count: lpot_applier_on_tgt_subset( queue, targets=target_discr.nodes(), sources=source_discr.nodes(), centers=geo_data.centers(), expansion_radii=geo_data.expansion_radii(), strengths=[strengths], qbx_tgt_numbers=qbx_tgt_numbers, qbx_center_numbers=qbx_center_numbers, **tgt_subset_kwargs) result.append((o.name, output_for_each_kernel[o.kernel_index])) timing_data = {} return result, timing_data
# }}} # }}} # }}} __all__ = ( "QBXLayerPotentialSource", "QBXTargetAssociationFailedException", ) # vim: fdm=marker