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-rw-r--r--Docs/source/running_cpp/parameters.rst25
-rwxr-xr-xExamples/Modules/resampling/analysis_leveling_thinning.py138
-rw-r--r--Examples/Modules/resampling/inputs_leveling_thinning65
-rw-r--r--Regression/Checksum/benchmarks_json/leveling_thinning.json33
-rw-r--r--Regression/WarpX-tests.ini16
-rw-r--r--Source/Particles/MultiParticleContainer.H2
-rw-r--r--Source/Particles/MultiParticleContainer.cpp2
-rw-r--r--Source/Particles/PhysicalParticleContainer.H4
-rw-r--r--Source/Particles/PhysicalParticleContainer.cpp7
-rw-r--r--Source/Particles/Resampling/LevelingThinning.H10
-rw-r--r--Source/Particles/Resampling/LevelingThinning.cpp6
-rw-r--r--Source/Particles/Resampling/Resampling.H9
-rw-r--r--Source/Particles/Resampling/Resampling.cpp10
-rw-r--r--Source/Particles/Resampling/ResamplingTrigger.H7
-rw-r--r--Source/Particles/Resampling/ResamplingTrigger.cpp8
-rw-r--r--Source/Particles/WarpXParticleContainer.H2
16 files changed, 322 insertions, 22 deletions
diff --git a/Docs/source/running_cpp/parameters.rst b/Docs/source/running_cpp/parameters.rst
index 05e9808eb..e646d2b6b 100644
--- a/Docs/source/running_cpp/parameters.rst
+++ b/Docs/source/running_cpp/parameters.rst
@@ -594,6 +594,31 @@ Particle initialization
(the name of an existing positron species must be provided).
**This feature requires to compile with QED=TRUE**
+* ``<species>.do_resampling`` (`0` or `1`) optional (default `0`)
+ If `1` resampling is performed for this species. This means that the number of macroparticles
+ will be reduced at specific timesteps while preserving the distribution function as much as
+ possible (in particular the weight of the remaining particles will be increased on average).
+ This can be useful in situations with continuous creation of particles (e.g. with ionization
+ or with QED effects). At least one resampling trigger (see below) must be specified to actually
+ perform resampling.
+
+* ``<species>.resampling_algorithm`` (`string`) optional (default `leveling_thinning`)
+ The algorithm used for resampling. Currently there is only one option, which is already set by
+ default:
+
+ * ``leveling_thinning`` This algorithm is defined in `Muraviev et al., arXiv:2006.08593 (2020) <https://arxiv.org/abs/2006.08593>`_.
+ The main parameter for this algorithm can be set with ``<species>.resampling_algorithm_target_ratio``.
+ It **roughly** corresponds to the ratio between the number of particles before and after
+ resampling. The default value for this parameter is 1.5.
+
+* ``<species>.resampling_trigger_intervals`` (`string`) optional (default `0`)
+ Using the `Intervals parser`_ syntax, this string defines timesteps at which resampling is
+ performed.
+
+* ``<species>.resampling_trigger_max_avg_ppc`` (`float`) optional (default `infinity`)
+ Resampling is performed everytime the number of macroparticles per cell of the species
+ averaged over the whole simulation domain exceeds this parameter.
+
.. _running-cpp-parameters-laser:
Laser initialization
diff --git a/Examples/Modules/resampling/analysis_leveling_thinning.py b/Examples/Modules/resampling/analysis_leveling_thinning.py
new file mode 100755
index 000000000..dc96b0138
--- /dev/null
+++ b/Examples/Modules/resampling/analysis_leveling_thinning.py
@@ -0,0 +1,138 @@
+#! /usr/bin/env python
+
+# Copyright 2020 Neil Zaim
+#
+# This file is part of WarpX.
+#
+# License: BSD-3-Clause-LBNL
+
+## In this test, we check that leveling thinning works as expected on two simple cases. Each case
+## corresponds to a different particle species.
+
+import yt
+import numpy as np
+import sys
+from scipy.special import erf
+sys.path.insert(1, '../../../../warpx/Regression/Checksum/')
+import checksumAPI
+
+fn_final = sys.argv[1]
+fn0 = fn_final[:-4] + '0000'
+
+ds0 = yt.load(fn0)
+ds = yt.load(fn_final)
+
+ad0 = ds0.all_data()
+ad = ds.all_data()
+
+numcells = 16*16
+t_r = 1.3 # target ratio
+relative_tol = 1.e-13 # tolerance for machine precision errors
+
+
+#### Tests for first species ####
+# Particles are present in all simulation cells and all have the same weight
+
+ppc = 400.
+numparts_init = numcells*ppc
+
+w0 = ad0['resampled_part1','particle_weight'].to_ndarray() # weights before resampling
+w = ad['resampled_part1','particle_weight'].to_ndarray() # weights after resampling
+
+# Renormalize weights for easier calculations
+w0 = w0*ppc
+w = w*ppc
+
+# Check that initial number of particles is indeed as expected
+assert(w0.shape[0] == numparts_init)
+# Check that all initial particles have the same weight
+assert(np.unique(w0).shape[0] == 1)
+# Check that this weight is 1 (to machine precision)
+assert(abs(w0[0] - 1) < relative_tol)
+
+# Check that the number of particles after resampling is as expected
+numparts_final = w.shape[0]
+expected_numparts_final = numparts_init/t_r**2
+error = np.abs(numparts_final - expected_numparts_final)
+std_numparts_final = np.sqrt(numparts_init/t_r**2*(1.-1./t_r**2))
+# 5 sigma test that has an intrisic probability to fail of 1 over ~2 millions
+print("difference between expected and actual final number of particles (1st species): " + str(error))
+print("tolerance: " + str(5*std_numparts_final))
+assert(error<5*std_numparts_final)
+
+# Check that the final weight is the same for all particles (to machine precision) and is as
+# expected
+final_weight = t_r**2
+assert(np.amax(np.abs(w-final_weight)) < relative_tol*final_weight )
+
+
+#### Tests for second species ####
+# Particles are only present in a single cell and have a gaussian weight distribution
+# Using a single cell makes the analysis easier because leveling thinning is done separately in
+# each cell
+
+ppc = 100000.
+numparts_init = ppc
+
+w0 = ad0['resampled_part2','particle_weight'].to_ndarray() # weights before resampling
+w = ad['resampled_part2','particle_weight'].to_ndarray() # weights after resampling
+
+# Renormalize and sort weights for easier calculations
+w0 = np.sort(w0)*ppc
+w = np.sort(w)*ppc
+
+## First we verify that the initial distribution is as expected
+
+# Check that the mean initial weight is as expected
+mean_initial_weight = np.average(w0)
+expected_mean_initial_weight = 2.*np.sqrt(2.)
+error = np.abs(mean_initial_weight - expected_mean_initial_weight)
+expected_std_initial_weight = 1./np.sqrt(2.)
+std_mean_initial_weight = expected_std_initial_weight/np.sqrt(numparts_init)
+# 5 sigma test that has an intrisic probability to fail of 1 over ~2 millions
+print("difference between expected and actual mean initial weight (2nd species): " + str(error))
+print("tolerance: " + str(5*std_mean_initial_weight))
+assert(error<5*std_mean_initial_weight)
+
+# Check that the initial weight variance is as expected
+variance_initial_weight = np.var(w0)
+expected_variance_initial_weight = 0.5
+error = np.abs(variance_initial_weight - expected_variance_initial_weight)
+std_variance_initial_weight = expected_variance_initial_weight*np.sqrt(2./numparts_init)
+# 5 sigma test that has an intrisic probability to fail of 1 over ~2 millions
+print("difference between expected and actual variance of initial weight (2nd species): " + str(error))
+print("tolerance: " + str(5*std_variance_initial_weight))
+
+## Next we verify that the resampling worked as expected
+
+# Check that the level weight value is as expected from the initial distribution
+level_weight = w[0]
+assert(np.abs(level_weight - mean_initial_weight*t_r) < level_weight*relative_tol)
+
+# Check that the number of particles at the level weight is the same as predicted from analytic
+# calculations
+numparts_leveled = np.argmax(w > level_weight) # This returns the first index for which
+# w > level_weight, which thus corresponds to the number of particles at the level weight
+expected_numparts_leveled = numparts_init/(2.*t_r)*(1+erf(expected_mean_initial_weight*(t_r-1.)) \
+ -1./(np.sqrt(np.pi)*expected_mean_initial_weight)* \
+ np.exp(-(expected_mean_initial_weight*(t_r-1.))**2))
+error = np.abs(numparts_leveled - expected_numparts_leveled)
+std_numparts_leveled = np.sqrt(expected_numparts_leveled - numparts_init/np.sqrt(np.pi)/(t_r* \
+ expected_mean_initial_weight)**2*(np.sqrt(np.pi)/4.* \
+ (2.*expected_mean_initial_weight**2+1.)*(1.-erf(expected_mean_initial_weight* \
+ (t_r-1.)))-0.5*np.exp(-(expected_mean_initial_weight*(t_r-1.))**2* \
+ (expected_mean_initial_weight*(t_r+1.)))))
+# 5 sigma test that has an intrisic probability to fail of 1 over ~2 millions
+print("difference between expected and actual number of leveled particles (2nd species): " + str(error))
+print("tolerance: " + str(5*std_numparts_leveled))
+
+numparts_unaffected = w.shape[0] - numparts_leveled
+numparts_unaffected_anticipated = w0.shape[0] - np.argmax(w0 > level_weight)
+# Check that number of particles with weight higher than level weight is the same before and after
+# resampling
+assert(numparts_unaffected == numparts_unaffected_anticipated)
+# Check that particles with weight higher than level weight are unaffected by resampling.
+assert(np.all(w[-numparts_unaffected:] == w0[-numparts_unaffected:]))
+
+test_name = fn_final[:-9] # Could also be os.path.split(os.getcwd())[1]
+checksumAPI.evaluate_checksum(test_name, fn_final)
diff --git a/Examples/Modules/resampling/inputs_leveling_thinning b/Examples/Modules/resampling/inputs_leveling_thinning
new file mode 100644
index 000000000..c761a75dd
--- /dev/null
+++ b/Examples/Modules/resampling/inputs_leveling_thinning
@@ -0,0 +1,65 @@
+max_step = 8
+amr.n_cell = 16 16
+amr.blocking_factor = 8
+amr.max_grid_size = 8
+geometry.is_periodic = 1 1
+geometry.prob_lo = 0. 0.
+geometry.prob_hi = 16. 16.
+amr.max_level = 0
+
+warpx.do_pml = 0
+
+particles.species_names = resampled_part1 resampled_part2
+
+my_constants.pi = 3.141592653589793
+
+# First particle species. The particles are distributed throughout the simulation box and all have
+# the same weight.
+resampled_part1.species_type = electron
+resampled_part1.injection_style = NRandomPerCell
+resampled_part1.num_particles_per_cell = 400
+resampled_part1.profile = constant
+resampled_part1.density = 1.
+resampled_part1.momentum_distribution_type = constant
+resampled_part1.do_not_deposit = 1
+resampled_part1.do_not_gather = 1
+resampled_part1.do_not_push = 1
+resampled_part1.do_resampling = 1
+resampled_part1.resampling_algorithm = leveling_thinning
+resampled_part1.resampling_algorithm_target_ratio = 1.3
+# This should trigger resampling at timestep 5 only in this case
+resampled_part1.resampling_trigger_intervals = 5
+# This should trigger resampling at first timestep
+resampled_part1.resampling_trigger_max_avg_ppc = 395
+
+# Second particle species. The particles are only present in one cell and have a Gaussian weight
+# distribution.
+resampled_part2.species_type = electron
+resampled_part2.injection_style = NRandomPerCell
+resampled_part2.num_particles_per_cell = 100000
+resampled_part2.zmin = 0.
+resampled_part2.zmax = 1.
+resampled_part2.xmin = 0.
+resampled_part2.xmax = 1.
+resampled_part2.profile = parse_density_function
+# Trick to get a Gaussian weight distribution is to do a Box-Muller transform using the position
+# within the cell as the two random variables. Here, we have a distribution with standard deviation
+# sigma = 1/sqrt(2) and mean 4*sigma.
+resampled_part2.density_function(x,y,z) = 2.*sqrt(2.)+sqrt(-log(x))*cos(2*pi*z)
+resampled_part2.momentum_distribution_type = constant
+resampled_part2.do_not_deposit = 1
+resampled_part2.do_not_gather = 1
+resampled_part2.do_not_push = 1
+resampled_part2.do_resampling = 1
+resampled_part2.resampling_algorithm = leveling_thinning
+resampled_part2.resampling_algorithm_target_ratio = 1.3
+# This should trigger resampling at timestep 7 only in this case. The rest is here to test the
+# intervals parser syntax.
+resampled_part2.resampling_trigger_intervals = 100 :120:3, 7:7:7
+# This should not trigger resampling: this species only has ~391 ppc on average.
+resampled_part2.resampling_trigger_max_avg_ppc = 395
+
+# Diagnostics
+diagnostics.diags_names = diag1
+diag1.period = 8
+diag1.diag_type = Full
diff --git a/Regression/Checksum/benchmarks_json/leveling_thinning.json b/Regression/Checksum/benchmarks_json/leveling_thinning.json
new file mode 100644
index 000000000..30cb28b20
--- /dev/null
+++ b/Regression/Checksum/benchmarks_json/leveling_thinning.json
@@ -0,0 +1,33 @@
+{
+ "resampled_part1": {
+ "particle_cpu": 91119.0,
+ "particle_id": 773368403.0,
+ "particle_momentum_x": 0.0,
+ "particle_momentum_y": 0.0,
+ "particle_momentum_z": 0.0,
+ "particle_position_x": 483987.107681320863775908946990966796875,
+ "particle_position_y": 484901.85673470445908606052398681640625,
+ "particle_weight": 255.3040749999979084350343327969312667846680
+ },
+ "resampled_part2": {
+ "particle_cpu": 0.0,
+ "particle_id": 5754560102.0,
+ "particle_momentum_x": 0.0,
+ "particle_momentum_y": 0.0,
+ "particle_momentum_z": 0.0,
+ "particle_position_x": 38483.4033272069573285989463329315185546875,
+ "particle_position_y": 38200.1600630077518871985375881195068359375,
+ "particle_weight": 2.8420376578928117083933102549053728580475
+ },
+ "lev=0": {
+ "Bx": 0.0,
+ "By": 0.0,
+ "Bz": 0.0,
+ "Ex": 0.0,
+ "Ey": 0.0,
+ "Ez": 0.0,
+ "jx": 0.0,
+ "jy": 0.0,
+ "jz": 0.0
+ }
+}
diff --git a/Regression/WarpX-tests.ini b/Regression/WarpX-tests.ini
index 5161589b2..2d3c697fd 100644
--- a/Regression/WarpX-tests.ini
+++ b/Regression/WarpX-tests.ini
@@ -1736,3 +1736,19 @@ doVis = 0
compareParticles = 0
analysisRoutine = Examples/Tests/initial_distribution/analysis_distribution.py
aux1File = Tools/PostProcessing/read_raw_data.py
+
+[leveling_thinning]
+buildDir = .
+inputFile = Examples/Modules/resampling/inputs_leveling_thinning
+runtime_params =
+dim = 2
+addToCompileString =
+restartTest = 0
+useMPI = 1
+numprocs = 4
+useOMP = 1
+numthreads = 1
+compileTest = 0
+doVis = 0
+compareParticles = 0
+analysisRoutine = Examples/Modules/resampling/analysis_leveling_thinning.py
diff --git a/Source/Particles/MultiParticleContainer.H b/Source/Particles/MultiParticleContainer.H
index 6d11d53f9..73a8207bd 100644
--- a/Source/Particles/MultiParticleContainer.H
+++ b/Source/Particles/MultiParticleContainer.H
@@ -277,8 +277,6 @@ protected:
std::vector<PCTypes> species_types;
- Resampling m_resampler;
-
/** Whether to absorb particles exiting the domain */
ParticleBC m_boundary_conditions = ParticleBC::none;
diff --git a/Source/Particles/MultiParticleContainer.cpp b/Source/Particles/MultiParticleContainer.cpp
index a740330d7..9bcb19b2e 100644
--- a/Source/Particles/MultiParticleContainer.cpp
+++ b/Source/Particles/MultiParticleContainer.cpp
@@ -722,7 +722,7 @@ void MultiParticleContainer::doResampling (const int timestep)
// do_resampling can only be true for PhysicalParticleContainers
if (!pc->do_resampling){ continue; }
- pc->resample(m_resampler, timestep);
+ pc->resample(timestep);
}
}
diff --git a/Source/Particles/PhysicalParticleContainer.H b/Source/Particles/PhysicalParticleContainer.H
index 30105ddcb..1e1307072 100644
--- a/Source/Particles/PhysicalParticleContainer.H
+++ b/Source/Particles/PhysicalParticleContainer.H
@@ -287,7 +287,7 @@ public:
* @param[in] resampler the Resampling object used for resampling.
* @param[in] timestep the current timestep.
*/
- void resample (const Resampling& resampler, const int timestep) override final;
+ void resample (const int timestep) override final;
#ifdef WARPX_QED
//Functions decleared in WarpXParticleContainer.H
@@ -338,6 +338,8 @@ protected:
bool boost_adjust_transverse_positions = false;
bool do_backward_propagation = false;
+ Resampling m_resampler;
+
// Inject particles during the whole simulation
void ContinuousInjection (const amrex::RealBox& injection_box) override;
diff --git a/Source/Particles/PhysicalParticleContainer.cpp b/Source/Particles/PhysicalParticleContainer.cpp
index b71ff2920..0f0ac96f5 100644
--- a/Source/Particles/PhysicalParticleContainer.cpp
+++ b/Source/Particles/PhysicalParticleContainer.cpp
@@ -123,6 +123,7 @@ PhysicalParticleContainer::PhysicalParticleContainer (AmrCore* amr_core, int isp
pp.query("do_field_ionization", do_field_ionization);
pp.query("do_resampling", do_resampling);
+ if (do_resampling) m_resampler = Resampling(species_name);
//check if Radiation Reaction is enabled and do consistency checks
pp.query("do_classical_radiation_reaction", do_classical_radiation_reaction);
@@ -1996,18 +1997,18 @@ PhysicalParticleContainer::getIonizationFunc (const WarpXParIter& pti,
ion_atomic_number);
}
-void PhysicalParticleContainer::resample (const Resampling& resampler, const int timestep)
+void PhysicalParticleContainer::resample (const int timestep)
{
const amrex::Real global_numparts = TotalNumberOfParticles();
- if (resampler.triggered(timestep, global_numparts))
+ if (m_resampler.triggered(timestep, global_numparts))
{
amrex::Print() << "Resampling " << species_name << ".\n";
for (int lev = 0; lev <= maxLevel(); lev++)
{
for (WarpXParIter pti(*this, lev); pti.isValid(); ++pti)
{
- resampler(pti, lev, this);
+ m_resampler(pti, lev, this);
}
}
}
diff --git a/Source/Particles/Resampling/LevelingThinning.H b/Source/Particles/Resampling/LevelingThinning.H
index 920fa07c9..14d0f8e6a 100644
--- a/Source/Particles/Resampling/LevelingThinning.H
+++ b/Source/Particles/Resampling/LevelingThinning.H
@@ -19,10 +19,18 @@
*/
class LevelingThinning: public ResamplingAlgorithm {
public:
+
+ /**
+ * \brief Default constructor of the LevelingThinning class.
+ */
+ LevelingThinning () = default;
+
/**
* \brief Constructor of the LevelingThinning class
+ *
+ * @param[in] species_name the name of the resampled species
*/
- LevelingThinning ();
+ LevelingThinning (const std::string species_name);
/**
* \brief A method that performs leveling thinning for the considered species.
diff --git a/Source/Particles/Resampling/LevelingThinning.cpp b/Source/Particles/Resampling/LevelingThinning.cpp
index 0fc347241..7ac28932e 100644
--- a/Source/Particles/Resampling/LevelingThinning.cpp
+++ b/Source/Particles/Resampling/LevelingThinning.cpp
@@ -9,12 +9,12 @@
#include <AMReX_Particles.H>
-LevelingThinning::LevelingThinning ()
+LevelingThinning::LevelingThinning (const std::string species_name)
{
using namespace amrex::literals;
- amrex::ParmParse pp("resampling_algorithm");
- pp.query("target_ratio", m_target_ratio);
+ amrex::ParmParse pp(species_name);
+ pp.query("resampling_algorithm_target_ratio", m_target_ratio);
AMREX_ALWAYS_ASSERT_WITH_MESSAGE( m_target_ratio > 0._rt,
"Resampling target ratio should be strictly greater than 0");
if (m_target_ratio <= 1._rt)
diff --git a/Source/Particles/Resampling/Resampling.H b/Source/Particles/Resampling/Resampling.H
index de919bfcb..b381771f8 100644
--- a/Source/Particles/Resampling/Resampling.H
+++ b/Source/Particles/Resampling/Resampling.H
@@ -39,10 +39,17 @@ class Resampling
public:
/**
+ * \brief Default constructor of the Resampling class.
+ */
+ Resampling () = default;
+
+ /**
* \brief Constructor of the Resampling class. Reads the chosen resampling algorithm from the
* input file.
+ *
+ * @param[in] species_name the name of the resampled species
*/
- Resampling ();
+ Resampling (const std::string species_name);
/**
* \brief A method that returns true if resampling should be done for the considered species
diff --git a/Source/Particles/Resampling/Resampling.cpp b/Source/Particles/Resampling/Resampling.cpp
index 65332d191..b5c2d8b4f 100644
--- a/Source/Particles/Resampling/Resampling.cpp
+++ b/Source/Particles/Resampling/Resampling.cpp
@@ -7,18 +7,20 @@
#include "Resampling.H"
#include "LevelingThinning.H"
-Resampling::Resampling ()
+Resampling::Resampling (const std::string species_name)
{
- amrex::ParmParse pp("resampling_algorithm");
+ amrex::ParmParse pp(species_name);
std::string resampling_algorithm_string = "leveling_thinning"; // default resampling algorithm
- pp.query("type", resampling_algorithm_string);
+ pp.query("resampling_algorithm", resampling_algorithm_string);
if (resampling_algorithm_string.compare("leveling_thinning") == 0)
{
- m_resampling_algorithm = std::make_unique<LevelingThinning>();
+ m_resampling_algorithm = std::make_unique<LevelingThinning>(species_name);
}
else
{ amrex::Abort("Unknown resampling algorithm."); }
+
+ m_resampling_trigger = ResamplingTrigger(species_name);
}
bool Resampling::triggered (const int timestep, const amrex::Real global_numparts) const
diff --git a/Source/Particles/Resampling/ResamplingTrigger.H b/Source/Particles/Resampling/ResamplingTrigger.H
index 7e373289a..830b511f2 100644
--- a/Source/Particles/Resampling/ResamplingTrigger.H
+++ b/Source/Particles/Resampling/ResamplingTrigger.H
@@ -22,10 +22,15 @@ class ResamplingTrigger
public:
/**
+ * \brief Default constructor of the ResamplingTrigger class.
+ */
+ ResamplingTrigger () = default;
+
+ /**
* \brief Constructor of the ResamplingTrigger class. Reads the resampling trigger parameters
* from the input file.
*/
- ResamplingTrigger ();
+ ResamplingTrigger (const std::string species_name);
/**
* \brief A method that returns true if resampling should be done for the considered species
diff --git a/Source/Particles/Resampling/ResamplingTrigger.cpp b/Source/Particles/Resampling/ResamplingTrigger.cpp
index c89f01848..97407d51a 100644
--- a/Source/Particles/Resampling/ResamplingTrigger.cpp
+++ b/Source/Particles/Resampling/ResamplingTrigger.cpp
@@ -7,15 +7,15 @@
#include "ResamplingTrigger.H"
#include "WarpX.H"
-ResamplingTrigger::ResamplingTrigger ()
+ResamplingTrigger::ResamplingTrigger (const std::string species_name)
{
- amrex::ParmParse pprt("resampling_trigger");
+ amrex::ParmParse pprt(species_name);
std::vector<std::string> resampling_trigger_int_string_vec = {"0"};
- pprt.queryarr("intervals", resampling_trigger_int_string_vec);
+ pprt.queryarr("resampling_trigger_intervals", resampling_trigger_int_string_vec);
m_resampling_intervals = IntervalsParser(resampling_trigger_int_string_vec);
- pprt.query("max_avg_ppc", m_max_avg_ppc);
+ pprt.query("resampling_trigger_max_avg_ppc", m_max_avg_ppc);
}
bool ResamplingTrigger::triggered (const int timestep, const amrex::Real global_numparts) const
diff --git a/Source/Particles/WarpXParticleContainer.H b/Source/Particles/WarpXParticleContainer.H
index 8f1cab717..12dbd1289 100644
--- a/Source/Particles/WarpXParticleContainer.H
+++ b/Source/Particles/WarpXParticleContainer.H
@@ -343,7 +343,7 @@ public:
* override the method for every derived class. Note that in practice this function is never
* called because resample() is only called for PhysicalParticleContainers.
*/
- virtual void resample (const Resampling& /*resampler*/, const int /*timestep*/) {}
+ virtual void resample (const int /*timestep*/) {}
protected:
amrex::Array<amrex::Real,3> m_v_galilean = {{0}};
generated by cgit v1.2.3 (git 2.46.0) at 2025-08-11 13:39:59 +0000