def __init__(self, r_cut, name=None):
util.print_status_line()
# tell the base class how we operate
# initialize the base class
pair.pair.__init__(self, r_cut, name)
# update the neighbor list
neighbor_list = pair._update_global_nlist(r_cut)
neighbor_list.subscribe(lambda: self.log * self.get_max_rcut())
# create the c++ mirror class
if not globals.exec_conf.isCUDAEnabled():
self.cpp_force = _evaluators_ext_template.PotentialPairLJ2(
globals.system_definition, neighbor_list.cpp_nlist, self.name)
self.cpp_class = _evaluators_ext_template.PotentialPairLJ2
else:
neighbor_list.cpp_nlist.setStorageMode(
hoomd.NeighborList.storageMode.full)
self.cpp_force = _evaluators_ext_template.PotentialPairLJ2GPU(
globals.system_definition, neighbor_list.cpp_nlist, self.name)
self.cpp_class = _evaluators_ext_template.PotentialPairLJ2GPU
# you can play with the block size value, set it to any multiple of 32 up to 1024. Use the
# lj.benchmark() command to find out which block size performs the fastest
self.cpp_force.setBlockSize(64)
globals.system.addCompute(self.cpp_force, self.force_name)
# setup the coefficent options
self.required_coeffs = ['epsilon', 'sigma', 'alpha']
self.pair_coeff.set_default_coeff('alpha', 1.0)
def __init__(self, r_cut, name=None):
util.print_status_line();
# tell the base class how we operate
# initialize the base class
pair.pair.__init__(self, r_cut, name);
# update the neighbor list
neighbor_list = pair._update_global_nlist(r_cut);
neighbor_list.subscribe(lambda: self.log*self.get_max_rcut())
# create the c++ mirror class
if not globals.exec_conf.isCUDAEnabled():
self.cpp_force = _evaluators_ext_template.PotentialPairLJ2(globals.system_definition, neighbor_list.cpp_nlist, self.name);
self.cpp_class = _evaluators_ext_template.PotentialPairLJ2;
else:
neighbor_list.cpp_nlist.setStorageMode(hoomd.NeighborList.storageMode.full);
self.cpp_force = _evaluators_ext_template.PotentialPairLJ2GPU(globals.system_definition, neighbor_list.cpp_nlist, self.name);
self.cpp_class = _evaluators_ext_template.PotentialPairLJ2GPU;
# you can play with the block size value, set it to any multiple of 32 up to 1024. Use the
# lj.benchmark() command to find out which block size performs the fastest
self.cpp_force.setBlockSize(64);
globals.system.addCompute(self.cpp_force, self.force_name);
# setup the coefficent options
self.required_coeffs = ['epsilon', 'sigma', 'alpha'];
self.pair_coeff.set_default_coeff('alpha', 1.0);
def __init__(self, group):
util.print_status_line();
# Error out in MPI simulations
if (hoomd.is_MPI_available()):
if globals.system_definition.getParticleData().getDomainDecomposition():
globals.msg.error("charge.pppm is not supported in multi-processor simulations.\n\n")
raise RuntimeError("Error initializing PPPM.")
# initialize the base class
force._force.__init__(self);
# create the c++ mirror class
# update the neighbor list
neighbor_list = pair._update_global_nlist(0.0)
neighbor_list.subscribe(lambda: self.log*0.0)
if not globals.exec_conf.isCUDAEnabled():
self.cpp_force = hoomd.PPPMForceCompute(globals.system_definition, neighbor_list.cpp_nlist, group.cpp_group);
else:
self.cpp_force = hoomd.PPPMForceComputeGPU(globals.system_definition, neighbor_list.cpp_nlist, group.cpp_group);
globals.system.addCompute(self.cpp_force, self.force_name);
# error check flag - must be set to true by set_params in order for the run() to commence
self.params_set = False;
# initialize the short range part of electrostatics
util._disable_status_lines = True;
self.ewald = pair.ewald(r_cut = 0.0);
util._disable_status_lines = False;
def __init__(self, r_cut=1.2, name=None): util.print_status_line(); self.r_cut = r_cut; # tell the base class how we operate # initialize the base class pair.pair.__init__(self, r_cut, name); # update the neighbor list neighbor_list = pair._update_global_nlist(r_cut); neighbor_list.subscribe(lambda: self.log*self.get_max_rcut()) # create the c++ mirror class if not globals.exec_conf.isCUDAEnabled(): self.cpp_force = _martini_plugin.PotentialPairCoulombM(globals.system_definition, neighbor_list.cpp_nlist, self.name); self.cpp_class = _martini_plugin.PotentialPairCoulombM; else: neighbor_list.cpp_nlist.setStorageMode(hoomd.NeighborList.storageMode.full); self.cpp_force = _martini_plugin.PotentialPairCoulombMGPU(globals.system_definition, neighbor_list.cpp_nlist, self.name); self.cpp_class = _martini_plugin.PotentialPairCoulombMGPU; self.cpp_force.setBlockSize(128); globals.system.addCompute(self.cpp_force, self.force_name); # setup the coefficent options self.required_coeffs = ['f', 'er', 'ron'];
def __init__(self, r_cut, name=None):
util.print_status_line()
# tell the base class how we operate
# initialize the base class
pair.pair.__init__(self, r_cut, name)
# update the neighbor list
neighbor_list = pair._update_global_nlist(r_cut)
neighbor_list.subscribe(lambda: self.log * self.get_max_rcut())
# create the c++ mirror class
if not globals.exec_conf.isCUDAEnabled():
self.cpp_force = _custom_pair_potentials.PotentialPairLowe(
globals.system_definition, neighbor_list.cpp_nlist, self.name)
self.cpp_class = _custom_pair_potentials.PotentialPairLowe
else:
neighbor_list.cpp_nlist.setStorageMode(
hoomd.NeighborList.storageMode.full)
self.cpp_force = _custom_pair_potentials.PotentialPairLoweGPU(
globals.system_definition, neighbor_list.cpp_nlist, self.name)
self.cpp_class = _custom_pair_potentials.PotentialPairLoweGPU
self.cpp_force.setBlockSize(
tune._get_optimal_block_size('pair.dpd_conservative'))
self.cpp_force.setBlockSize(64)
globals.system.addCompute(self.cpp_force, self.force_name)
# setup the coefficent options
self.required_coeffs = ['A']
def __init__(self, group):
util.print_status_line();
# Error out in MPI simulations
if (hoomd.is_MPI_available()):
if globals.system_definition.getParticleData().getDomainDecomposition():
globals.msg.error("charge.pppm is not supported in multi-processor simulations.\n\n")
raise RuntimeError("Error initializing PPPM.")
# initialize the base class
force._force.__init__(self);
# create the c++ mirror class
# update the neighbor list
neighbor_list = pair._update_global_nlist(0.0)
neighbor_list.subscribe(lambda: self.log*0.0)
if not globals.exec_conf.isCUDAEnabled():
self.cpp_force = hoomd.PPPMForceCompute(globals.system_definition, neighbor_list.cpp_nlist, group.cpp_group);
else:
self.cpp_force = hoomd.PPPMForceComputeGPU(globals.system_definition, neighbor_list.cpp_nlist, group.cpp_group);
globals.system.addCompute(self.cpp_force, self.force_name);
# error check flag - must be set to true by set_params in order for the run() to commence
self.params_set = False;
# initialize the short range part of electrostatics
util._disable_status_lines = True;
self.ewald = pair.ewald(r_cut = 0.0);
util._disable_status_lines = False;
def __init__(self, T=1.0, gdt=0.1, rcut=1.0, seed=1, period=1):
util.print_status_line();
# initialize base class
_updater.__init__(self);
# setup the variant inputs
#T = variant._setup_variant_input(T);
# update the neighbor list
neighbor_list = pair._update_global_nlist(rcut);
neighbor_list.subscribe(lambda: rcut)
# initialize the reflected c++ class
self.cpp_updater = _custom_updaters.LoweAndersenUpdater(globals.system_definition,neighbor_list.cpp_nlist,T,gdt,rcut,int(seed));
#if not globals.exec_conf.isCUDAEnabled():
# self.cpp_updater = _custom_updaters.LoweAndersenUpdater(globals.system_definition,group.cpp_group,thermo.cpp_compute,tau,T.cpp_variant,deltaT);
#else:
# self.cpp_updater = _custom_updaters.LoweAndersenUpdaterGPU(globals.system_definition,group.cpp_group,thermo.cpp_compute,tau,T.cpp_variant,deltaT);
self.setupUpdater(period);
def __init__(self, r_cut, d_max=None, name=None):
util.print_status_line()
# tell the base class how we operate
# initialize the base class
pair.pair.__init__(self, r_cut, name)
# update the neighbor list
if d_max is None:
sysdef = globals.system_definition
d_max = max([
x.diameter
for x in data.particle_data(sysdef.getParticleData())
])
globals.msg.notice(2,
"Notice: sgauss set d_max=" + str(d_max) + "\n")
neighbor_list = pair._update_global_nlist(r_cut)
neighbor_list.subscribe(lambda: self.log * self.get_max_rcut())
neighbor_list.cpp_nlist.setMaximumDiameter(d_max)
# create the c++ mirror class
if not globals.exec_conf.isCUDAEnabled():
self.cpp_force = _custom_pair_potentials.PotentialPairSYukawa(
globals.system_definition, neighbor_list.cpp_nlist, self.name)
self.cpp_class = _custom_pair_potentials.PotentialPairSYukawa
else:
neighbor_list.cpp_nlist.setStorageMode(
hoomd.NeighborList.storageMode.full)
self.cpp_force = _custom_pair_potentials.PotentialPairSYukawaGPU(
globals.system_definition, neighbor_list.cpp_nlist, self.name)
self.cpp_class = _custom_pair_potentials.PotentialPairSYukawaGPU
self.cpp_force.setBlockSize(128)
globals.system.addCompute(self.cpp_force, self.force_name)
# setup the coefficent options
self.required_coeffs = ['epsilon', 'kappa']
def __init__(self, r_cut, T, seed=1, name=None):
util.print_status_line()
# tell the base class how we operate
# initialize the base class
pair.pair.__init__(self, r_cut, name)
# update the neighbor list
neighbor_list = pair._update_global_nlist(r_cut)
neighbor_list.subscribe(lambda: self.log * self.get_max_rcut())
# create the c++ mirror class
if globals.exec_conf.isCUDAEnabled():
self.cpp_force = _custom_pair_potentials.PotentialPairLoweThermoLowe(
globals.system_definition, neighbor_list.cpp_nlist, self.name)
self.cpp_class = _custom_pair_potentials.PotentialPairLoweThermoLowe
else:
neighbor_list.cpp_nlist.setStorageMode(
hoomd.NeighborList.storageMode.full)
self.cpp_force = _custom_pair_potentials.PotentialPairLoweThermoLoweGPU(
globals.system_definition, neighbor_list.cpp_nlist, self.name)
self.cpp_class = _custom_pair_potentials.PotentialPairLoweThermoLoweGPU
self.cpp_force.setBlockSize(
tune._get_optimal_block_size('pair.dpd'))
globals.system.addCompute(self.cpp_force, self.force_name)
# setup the coefficent options
self.required_coeffs = ['A', 'gamma']
# set the seed for dpd thermostat
self.cpp_force.setSeed(seed)
# set the temperature
# setup the variant inputs
T = variant._setup_variant_input(T)
self.cpp_force.setT(T.cpp_variant)
def __init__(self, group):
util.print_status_line();
# initialize the base class
force._force.__init__(self);
# create the c++ mirror class
# update the neighbor list
neighbor_list = pair._update_global_nlist(0.0)
neighbor_list.subscribe(lambda: self.log*0.0)
if not globals.exec_conf.isCUDAEnabled():
self.cpp_force = hoomd.PPPMForceCompute(globals.system_definition, neighbor_list.cpp_nlist, group.cpp_group);
else:
self.cpp_force = hoomd.PPPMForceComputeGPU(globals.system_definition, neighbor_list.cpp_nlist, group.cpp_group);
globals.system.addCompute(self.cpp_force, self.force_name);
# error check flag - must be set to true by set_params in order for the run() to commence
self.params_set = False;
# initialize the short range part of electrostatics
util._disable_status_lines = True;
self.ewald = pair.ewald(r_cut = 0.0);
util._disable_status_lines = False;
