def __init__(self,
r_buff=0.4,
check_period=1,
d_max=None,
dist_check=True,
name=None):
nlist.__init__(self)
# create the C++ mirror class
if not hoomd.context.current.device.cpp_exec_conf.isCUDAEnabled():
self.cpp_nlist = _md.NeighborListTree(
hoomd.context.current.system_definition, 0.0, r_buff)
else:
self.cpp_nlist = _md.NeighborListGPUTree(
hoomd.context.current.system_definition, 0.0, r_buff)
self.cpp_nlist.setEvery(check_period, dist_check)
if name is None:
self.name = "tree_nlist_%d" % tree.cur_id
tree.cur_id += 1
else:
self.name = name
hoomd.context.current.system.addCompute(self.cpp_nlist, self.name)
# register this neighbor list with the context
hoomd.context.current.neighbor_lists += [self]
# save the user defined parameters
self.set_params(r_buff, check_period, d_max, dist_check)
def __init__(self,
r_buff=0.4,
check_period=1,
d_max=None,
dist_check=True,
name=None):
hoomd.util.print_status_line()
# register the citation
c = hoomd.cite.article(
cite_key='howard2016',
author=[
'M P Howard', 'J A Anderson', 'A Nikoubashman', 'S C Glotzer',
'A Z Panagiotopoulos'
],
title=
'Efficient neighbor list calculation for molecular simulation of colloidal systems using graphics processing units',
journal='Computer Physics Communications',
volume=203,
pages='45--52',
month='Mar',
year='2016',
doi='10.1016/j.cpc.2016.02.003',
feature='tree neighbor lists')
hoomd.cite._ensure_global_bib().add(c)
nlist.__init__(self)
# create the C++ mirror class
if not hoomd.context.exec_conf.isCUDAEnabled():
self.cpp_nlist = _md.NeighborListTree(
hoomd.context.current.system_definition, 0.0, r_buff)
else:
self.cpp_nlist = _md.NeighborListGPUTree(
hoomd.context.current.system_definition, 0.0, r_buff)
self.cpp_nlist.setEvery(check_period, dist_check)
if name is None:
self.name = "tree_nlist_%d" % tree.cur_id
tree.cur_id += 1
else:
self.name = name
hoomd.context.current.system.addCompute(self.cpp_nlist, self.name)
# register this neighbor list with the context
hoomd.context.current.neighbor_lists += [self]
# save the user defined parameters
hoomd.util.quiet_status()
self.set_params(r_buff, check_period, d_max, dist_check)
hoomd.util.unquiet_status()
def __init__(self, r_buff=0.4, check_period=1, d_max=None, dist_check=True, name=None):
# register the citation
c1 = hoomd.cite.article(cite_key='howard2016',
author=['M P Howard', 'J A Anderson', 'A Nikoubashman', 'S C Glotzer', 'A Z Panagiotopoulos'],
title='Efficient neighbor list calculation for molecular simulation of colloidal systems using graphics processing units',
journal='Computer Physics Communications',
volume=203,
pages='45--52',
month='Mar',
year='2016',
doi='10.1016/j.cpc.2016.02.003',
feature='tree neighbor lists')
c2 = hoomd.cite.article(cite_key='howard2019',
author=['M P Howard', 'A Statt', 'F Madutsa', 'T M Truskett', 'A Z Panagiotopoulos'],
title='Quantized bounding volume hierarchies for neighbor search in molecular simulations on graphics processing units',
journal='Computational Materials Science',
volume=164,
pages='139--146',
month='Jun',
year='2019',
doi='10.1016/j.commatsci.2019.04.004',
feature='tree neighbor lists')
hoomd.cite._ensure_global_bib().add((c1,c2))
nlist.__init__(self)
# create the C++ mirror class
if not hoomd.context.current.device.cpp_exec_conf.isCUDAEnabled():
self.cpp_nlist = _md.NeighborListTree(hoomd.context.current.system_definition, 0.0, r_buff)
else:
self.cpp_nlist = _md.NeighborListGPUTree(hoomd.context.current.system_definition, 0.0, r_buff)
self.cpp_nlist.setEvery(check_period, dist_check)
if name is None:
self.name = "tree_nlist_%d" % tree.cur_id
tree.cur_id += 1
else:
self.name = name
hoomd.context.current.system.addCompute(self.cpp_nlist, self.name)
# register this neighbor list with the context
hoomd.context.current.neighbor_lists += [self]
# save the user defined parameters
self.set_params(r_buff, check_period, d_max, dist_check)
def __init__(self, group, T, seed=0, xi = 0.5, error = 0.001, function_form = None, max_strain = 0.5, nlist_type = "cell" ):
# Print the status of the initialization
hoomd.util.print_status_line();
# initialize base class
hoomd.md.integrate._integration_method.__init__(self);
# setup the variant inputs
T = hoomd.variant._setup_variant_input(T);
# create the compute thermo
compute._get_unique_thermo(group=group);
# Real space neighborlist cutoff based on error estimate for spectral sums
self.rcut = math.sqrt( - math.log( error ) ) / xi;
# If this line is changed, remember to change in C++ code as well!!
# initialize the reflected c++ class
if not hoomd.context.exec_conf.isCUDAEnabled():
hoomd.context.msg.error("Sorry, we have not written CPU code for PSE RPY simulation. \n");
raise RuntimeError('Error creating Stokes');
else:
# Create a neighborlist exclusively for real space interactions. Use cell lists by
# default, but also allow the user to specify
if ( nlist_type.upper() == "CELL" ):
cl_stokes = _hoomd.CellListGPU(hoomd.context.current.system_definition);
hoomd.context.current.system.addCompute(cl_stokes, "stokes_cl")
self.neighbor_list = _md.NeighborListGPUBinned(hoomd.context.current.system_definition, self.rcut, 0.4, cl_stokes);
elif ( nlist_type.upper() == "TREE" ):
self.neighbor_list = _md.NeighborListGPUTree(hoomd.context.current.system_definition, self.rcut, 0.4)
elif ( nlist_type.upper() == "STENCIL" ):
cl_stokes = _hoomd.CellListGPU(hoomd.context.current.system_definition)
hoomd.context.current.system.addCompute(cl_stokes, "stokes_cl")
cls_stokes = _hoomd.CellListStencil( hoomd.context.current.system_definition, cl_stokes )
hoomd.context.current.system.addCompute( cls_stokes, "stokes_cls")
self.neighbor_list = _md.NeighborListGPUStencil(hoomd.context.current.system_definition, self.rcut, 0.4, cl_stokes, cls_stokes)
else:
hoomd.context.msg.error("Invalid neighborlist method specified. Valid options are: cell, tree, stencil. \n");
raise RuntimeError('Error constructing neighborlist');
# Set neighborlist properties
self.neighbor_list.setEvery(1, True);
hoomd.context.current.system.addCompute(self.neighbor_list, "stokes_nlist")
self.neighbor_list.countExclusions();
# Call the stokes integrator
self.cpp_method = _PSEv1.Stokes(hoomd.context.current.system_definition, group.cpp_group, T.cpp_variant, seed, self.neighbor_list, xi, error);
self.cpp_method.validateGroup()
if function_form is not None:
self.cpp_method.setShear(function_form.cpp_function, max_strain)
else:
no_shear_function = shear_function.steady(dt = 0)
self.cpp_method.setShear(no_shear_function.cpp_function, max_strain)
self.cpp_method.setParams()