def make_snapshot(particle_types=['A'], a=1, n=7, r=0):
"""Make a snapshot with particles in a fcc structure.
Args:
particle_types: List of particle type names
a: Lattice constant
n: Number of unit cells along each box edge
r: Amount to randomly perturb particles in x,y,z
Place particles in a fcc structure. The box is cubic with a side length
of ``n * a``. There will be ``4 * n**3`` particles in the snapshot.
"""
s = Snapshot(device.communicator)
if s.communicator.rank == 0:
# make one unit cell
s.configuration.box = [a, a, a, 0, 0, 0]
s.particles.N = 4
s.particles.types = particle_types
s.particles.position[:] = [
[0, 0, 0],
[0, a / 2, a / 2],
[a / 2, 0, a / 2],
[a / 2, a / 2, 0],
]
# and replicate it
s.replicate(n, n, n)
# perturb the positions
if r > 0:
shift = numpy.random.uniform(-r, r, size=(s.particles.N, 3))
s.particles.position[:] += shift
return s
def create_state_from_gsd(self, filename, frame=-1):
"""Create the simulation state from a GSD file.
Args:
filename (str): GSD file to read
frame (int): Index of the frame to read from the file. Negative
values index back from the last frame in the file.
"""
if self.state is not None:
raise RuntimeError("Cannot initialize more than once\n")
filename = _hoomd.mpi_bcast_str(filename, self.device._cpp_exec_conf)
# Grab snapshot and timestep
reader = _hoomd.GSDReader(self.device._cpp_exec_conf, filename,
abs(frame), frame < 0)
snapshot = Snapshot._from_cpp_snapshot(reader.getSnapshot(),
self.device.communicator)
step = reader.getTimeStep() if self.timestep is None else self.timestep
self._state = State(self, snapshot)
reader.clearSnapshot()
# Store System and Reader for Operations
self._cpp_sys = _hoomd.System(self.state._cpp_sys_def, step)
self._init_communicator()
self.operations._store_reader(reader)
def snapshot(self):
r"""hoomd.Snapshot: All data of a simulation's current microstate.
`State.snapshot` should be used when all of a simulation's state
information is desired in a single object. When accessed, data across
all MPI ranks and from GPUs is gathered on the root MPI rank's memory.
When accessing data in MPI simulations, it is recommended to use a
``if snapshot.exists:`` conditional to prevent attempting to access data
on a non-root rank.
This property can be set to replace the system state with the given
`hoomd.Snapshot` object. Example use cases in which a simulation's
state may be reset from a snapshot include Monte Carlo schemes
implemented at the Python script level, where the current snapshot is
passed to the Monte Carlo simulation before being passed back after
running some Monte Carlo steps.
Warning:
Using `State.snapshot` multiple times will gather data across MPI
ranks and GPUs every time. If the snapshot is needed for more than
one use, it is recommended to store it in a variable.
Note:
Setting or getting a snapshot is an order :math:`O(N_{particles}
+ N_{bonds} + \ldots)` operation.
"""
cpp_snapshot = self._cpp_sys_def.takeSnapshot_double()
return Snapshot._from_cpp_snapshot(cpp_snapshot,
self._simulation.device.communicator)
def test_from_gsd_snapshot_populated(s, device):
if s.exists:
s.configuration.box = [10, 12, 7, 0.1, 0.4, 0.2]
for section in ('particles', 'bonds', 'angles', 'dihedrals',
'impropers', 'pairs'):
setattr(getattr(s, section), 'N', 5)
setattr(getattr(s, section), 'types', ['A', 'B'])
for prop in ('angmom', 'body', 'charge', 'diameter', 'image', 'mass',
'moment_inertia', 'orientation', 'position', 'typeid',
'velocity'):
attr = getattr(s.particles, prop)
if attr.dtype == numpy.float64:
attr[:] = numpy.random.rand(*attr.shape)
else:
attr[:] = numpy.random.randint(3, size=attr.shape)
for section in ('bonds', 'angles', 'dihedrals', 'impropers', 'pairs'):
for prop in ('group', 'typeid'):
attr = getattr(getattr(s, section), prop)
attr[:] = numpy.random.randint(3, size=attr.shape)
s.constraints.N = 3
for prop in ('group', 'value'):
attr = getattr(s.constraints, prop)
if attr.dtype == numpy.float64:
attr[:] = numpy.random.rand(*attr.shape)
else:
attr[:] = numpy.random.randint(3, size=attr.shape)
gsd_snap = make_gsd_snapshot(s)
hoomd_snap = Snapshot.from_gsd_snapshot(gsd_snap, device.communicator)
assert_equivalent_snapshots(gsd_snap, hoomd_snap)
def get_snapshot(self):
"""Make a copy of the simulation current state.
`State.get_snapshot` makes a copy of the simulation state and
makes it available in a single object. `State.set_snapshot` resets
the internal state to that in the given snapshot. Use these methods
to implement techniques like hybrid MD/MC or umbrella sampling where
entire system configurations need to be reset to a previous one after a
rejected move.
Note:
Data across all MPI ranks and from GPUs is gathered on the root MPI
rank's memory. When accessing data in MPI simulations, use a ``if
snapshot.communicator.rank == 0:`` conditional to access data arrays
only on the root rank.
Note:
`State.get_snapshot` is an order :math:`O(N_{particles} + N_{bonds}
+ \\ldots)` operation.
See Also:
`set_snapshot`
Returns:
hoomd.Snapshot: The current simulation state
"""
cpp_snapshot = self._cpp_sys_def.takeSnapshot_double()
return Snapshot._from_cpp_snapshot(
cpp_snapshot, self._simulation.device.communicator)
def make_snapshot(particle_types=['A'], dimensions=3, a=1, n=7, r=0):
s = Snapshot(device.communicator)
if s.exists:
box = [n * a, n * a, n * a, 0, 0, 0]
if dimensions == 2:
box[2] = 0
s.configuration.box = box
s.particles.N = n**dimensions
s.particles.types = particle_types
# create the lattice
range_ = numpy.arange(-n / 2, n / 2)
if dimensions == 2:
pos = list(itertools.product(range_, range_, [0]))
else:
pos = list(itertools.product(range_, repeat=3))
pos = numpy.array(pos) * a
pos[:, 0] += a / 2
pos[:, 1] += a / 2
if dimensions == 3:
pos[:, 2] += a / 2
# perturb the positions
if r > 0:
shift = numpy.random.uniform(-r, r, size=(s.particles.N, 3))
if dimensions == 2:
shift[:, 2] = 0
pos += shift
s.particles.position[:] = pos
return s
def make_snapshot(particle_types=['A'], dimensions=3, d=1, L=20):
"""Make the snapshot.
Args:
particle_types: List of particle type names
dimensions: Number of dimensions (2 or 3)
d: Distance apart to place particles
L: Box length
The two particles are placed at (-d/2, 0, 0) and (d/2,0,0). When,
dimensions==3, the box is L by L by L. When dimensions==2, the box is
L by L by 0.
"""
s = Snapshot(device.communicator)
N = 2
if s.communicator.rank == 0:
box = [L, L, L, 0, 0, 0]
if dimensions == 2:
box[2] = 0
s.configuration.box = box
s.particles.N = N
# shift particle positions slightly in z so MPI tests pass
s.particles.position[:] = [[-d / 2, 0, .1], [d / 2, 0, .1]]
s.particles.types = particle_types
if dimensions == 2:
box[2] = 0
s.particles.position[:] = [[-d / 2, 0.1, 0], [d / 2, 0.1, 0]]
return s
def create_state_from_snapshot(self, snapshot):
"""Create the simulations state from a `Snapshot`.
Args:
snapshot (Snapshot or gsd.hoomd.Snapshot): Snapshot to initialize
the state from. A `gsd.hoomd.Snapshot` will first be
converted to a `hoomd.Snapshot`.
When `timestep` is `None` before calling, `create_state_from_snapshot`
sets `timestep` to 0.
"""
if self.state is not None:
raise RuntimeError("Cannot initialize more than once\n")
if isinstance(snapshot, Snapshot):
# snapshot is hoomd.Snapshot
self._state = State(self, snapshot)
elif _match_class_path(snapshot, 'gsd.hoomd.Snapshot'):
# snapshot is gsd.hoomd.Snapshot
snapshot = Snapshot.from_gsd_snapshot(snapshot,
self._device.communicator)
self._state = State(self, snapshot)
else:
raise TypeError(
"Snapshot must be a hoomd.Snapshot or gsd.hoomd.Snapshot.")
step = 0
if self.timestep is not None:
step = self.timestep
self._init_system(step)
def filter_snapshot(n=10, particle_types=['A']):
s = Snapshot(device.communicator)
if s.communicator.rank == 0:
s.configuration.box = [20, 20, 20, 0, 0, 0]
s.particles.N = n
s.particles.position[:] = np.random.uniform(-10, 10, size=(n, 3))
s.particles.types = particle_types
return s
def create_state_from_snapshot(self,
snapshot,
domain_decomposition=(None, None, None)):
"""Create the simulation state from a `Snapshot`.
Args:
snapshot (Snapshot or gsd.hoomd.Snapshot): Snapshot to initialize
the state from. A `gsd.hoomd.Snapshot` will first be
converted to a `hoomd.Snapshot`.
domain_decomposition (tuple): Choose how to distribute the state
across MPI ranks with domain decomposition. Provide a tuple
of 3 integers indicating the number of evenly spaced domains in
the x, y, and z directions (e.g. ``(8,4,2)``). Provide a tuple
of 3 lists of floats to set the fraction of the simulation box
to include in each domain. The sum of each list of floats must
be 1.0 (e.g. ``([0.25, 0.75], [0.2, 0.8], [1.0])``).
When `timestep` is `None` before calling, `create_state_from_snapshot`
sets `timestep` to 0.
Note:
Set any or all of the ``domain_decomposition`` tuple elements to
`None` and `create_state_from_gsd` will select a value that
minimizes the surface area between the domains (e.g.
``(2,None,None)``). The domains are spaced evenly along each
automatically selected direction. The default value of ``(None,
None, None)`` will automatically select the number of domains in all
directions.
See Also:
`State.get_snapshot`
`State.set_snapshot`
"""
if self._state is not None:
raise RuntimeError("Cannot initialize more than once\n")
if isinstance(snapshot, Snapshot):
# snapshot is hoomd.Snapshot
self._state = State(self, snapshot, domain_decomposition)
elif _match_class_path(snapshot, 'gsd.hoomd.Snapshot'):
# snapshot is gsd.hoomd.Snapshot
snapshot = Snapshot.from_gsd_snapshot(snapshot,
self._device.communicator)
self._state = State(self, snapshot, domain_decomposition)
else:
raise TypeError(
"Snapshot must be a hoomd.Snapshot or gsd.hoomd.Snapshot.")
step = 0
if self.timestep is not None:
step = self.timestep
self._init_system(step)
def make_snapshot(particle_types=['A'], dimensions=3, a=1, n=7, r=0):
"""Make the snapshot.
Args:
particle_types: List of particle type names
dimensions: Number of dimensions (2 or 3)
a: Lattice constant
n: Number of particles along each box edge. Pass a tuple for
different lengths in each dimension.
r: Fraction of `a` to randomly perturb particles
Place particles on a simple cubic (dimensions==3) or square
(dimensions==2) lattice. The box is cubic (or square) with a side length
of `n * a`.
Set `r` to randomly perturb particles a small amount off their lattice
positions. This is useful in MD simulation testing so that forces do not
cancel out by symmetry.
"""
if isinstance(n, int):
n = (n, ) * dimensions
if dimensions == 2:
n += (1, )
s = Snapshot(device.communicator)
if s.communicator.rank == 0:
box = [n[0] * a, n[1] * a, n[2] * a, 0, 0, 0]
if dimensions == 2:
box[2] = 0
s.configuration.box = box
s.particles.N = numpy.product(n)
s.particles.types = particle_types
if any(nx == 0 for nx in n):
return s
# create the lattice
ranges = [numpy.arange(-nx / 2, nx / 2) for nx in n]
x, y, z = numpy.meshgrid(*ranges)
lattice_position = numpy.vstack(
(x.flatten(), y.flatten(), z.flatten())).T
pos = (lattice_position + 0.5) * a
if dimensions == 2:
pos[:, 2] = 0
# perturb the positions
if r > 0:
shift = numpy.random.uniform(-r, r, size=(s.particles.N, 3))
if dimensions == 2:
shift[:, 2] = 0
pos += shift
s.particles.position[:] = pos
return s
def make_snapshot(particle_types=['A'], dimensions=3, a=1, n=7, r=0):
"""Make the snapshot.
Args:
particle_types: List of particle type names
dimensions: Number of dimensions (2 or 3)
a: Lattice constant
n: Number of particles along each box edge
r: Fraction of `a` to randomly perturb particles
Place particles on a simple cubic (dimensions==3) or square
(dimensions==2) lattice. The box is cubic (or square) with a side length
of `n * a`.
Set `r` to randomly perturb particles a small amount off their lattice
positions. This is useful in MD simulation testing so that forces do not
cancel out by symmetry.
"""
s = Snapshot(device.communicator)
if s.communicator.rank == 0:
box = [n * a, n * a, n * a, 0, 0, 0]
if dimensions == 2:
box[2] = 0
s.configuration.box = box
s.particles.N = n**dimensions
s.particles.types = particle_types
# create the lattice
if n > 0:
range_ = numpy.arange(-n / 2, n / 2)
if dimensions == 2:
pos = list(itertools.product(range_, range_, [0]))
else:
pos = list(itertools.product(range_, repeat=3))
pos = numpy.array(pos) * a
pos[:, 0] += a / 2
pos[:, 1] += a / 2
if dimensions == 3:
pos[:, 2] += a / 2
# perturb the positions
if r > 0:
shift = numpy.random.uniform(-r,
r,
size=(s.particles.N, 3))
if dimensions == 2:
shift[:, 2] = 0
pos += shift
s.particles.position[:] = pos
return s
def make_snapshot(particle_types=['A'], dimensions=3, d=1, L=20):
s = Snapshot(device.communicator)
N = 2
if s.exists:
box = [L, L, L, 0, 0, 0]
if dimensions == 2:
box[2] = 1
s.configuration.box = box
s.configuration.dimensions = dimensions
s.particles.N = N
# shift particle positions slightly in z so MPI tests pass
s.particles.position[:] = [[-d / 2, 0, .1], [d / 2, 0, .1]]
s.particles.types = particle_types
return s
def create_state_from_gsd(self,
filename,
frame=-1,
domain_decomposition=(None, None, None)):
"""Create the simulation state from a GSD file.
Args:
filename (str): GSD file to read
frame (int): Index of the frame to read from the file. Negative
values index back from the last frame in the file.
domain_decomposition (tuple): Choose how to distribute the state
across MPI ranks with domain decomposition. Provide a tuple
of 3 integers indicating the number of evenly spaced domains in
the x, y, and z directions (e.g. ``(8,4,2)``). Provide a tuple
of 3 lists of floats to set the fraction of the simulation box
to include in each domain. The sum of each list of floats must
be 1.0 (e.g. ``([0.25, 0.75], [0.2, 0.8], [1.0])``).
When `timestep` is `None` before calling, `create_state_from_gsd`
sets `timestep` to the value in the selected GSD frame in the file.
Note:
Set any or all of the ``domain_decomposition`` tuple elements to
`None` and `create_state_from_gsd` will select a value that
minimizes the surface area between the domains (e.g.
``(2,None,None)``). The domains are spaced evenly along each
automatically selected direction. The default value of ``(None,
None, None)`` will automatically select the number of domains in all
directions.
"""
if self._state is not None:
raise RuntimeError("Cannot initialize more than once\n")
filename = _hoomd.mpi_bcast_str(filename, self.device._cpp_exec_conf)
# Grab snapshot and timestep
reader = _hoomd.GSDReader(self.device._cpp_exec_conf, filename,
abs(frame), frame < 0)
snapshot = Snapshot._from_cpp_snapshot(reader.getSnapshot(),
self.device.communicator)
step = reader.getTimeStep() if self.timestep is None else self.timestep
self._state = State(self, snapshot, domain_decomposition)
reader.clearSnapshot()
self._init_system(step)
def make_snapshot(particle_types=['A'],
dimensions=3,
position=(0, 0, 0),
orientation=(1, 0, 0, 0),
L=20):
"""Make the snapshot.
Args:
particle_types: List of particle type names
dimensions: Number of dimensions (2 or 3)
position: Position to place the particle
orientation: Orientation quaternion to assign to the particle
L: Box length
The arguments position and orientation define the position and
orientation of the particle. When dimensions==3, the box is a cubic box
with dimensions L by L by L. When dimensions==2, the box is a square box
with dimensions L by L by 0.
"""
s = Snapshot(device.communicator)
N = 1
if dimensions == 2 and position[2] != 0:
raise ValueError(
'z component of position must be zero for 2D simulation.')
if s.communicator.rank == 0:
box = [L, L, L, 0, 0, 0]
if dimensions == 2:
box[2] = 0
s.configuration.box = box
s.particles.N = N
# shift particle positions slightly in z so MPI tests pass
s.particles.position[0] = position
s.particles.orientation[0] = orientation
s.particles.types = particle_types
return s
def test_from_gsd_snapshot_empty(s, device):
gsd_snap = make_gsd_snapshot(s)
hoomd_snap = Snapshot.from_gsd_snapshot(gsd_snap, device.communicator)
assert_equivalent_snapshots(gsd_snap, hoomd_snap)
def snap(device):
s = Snapshot(device.communicator)
N = 1000
if s.exists:
s.configuration.box = [20, 20, 20, 0, 0, 0]
s.particles.N = N
s.particles.position[:] = numpy.random.uniform(-10, 10, size=(N, 3))
s.particles.velocity[:] = numpy.random.uniform(-1, 1, size=(N, 3))
s.particles.typeid[:] = numpy.random.randint(0, 3, size=N)
s.particles.mass[:] = numpy.random.uniform(1, 2, size=N)
s.particles.charge[:] = numpy.random.uniform(-2, 2, size=N)
s.particles.image[:] = numpy.random.randint(-8, 8, size=(N, 3))
s.particles.orientation[:] = numpy.random.uniform(-1, 1, size=(N, 4))
s.particles.moment_inertia[:] = numpy.random.uniform(1, 5, size=(N, 3))
s.particles.angmom[:] = numpy.random.uniform(-1, 1, size=(N, 4))
s.particles.types = ['A', 'B', 'C', 'D']
s.bonds.N = N - 1
for i in range(s.bonds.N):
s.bonds.group[i, :] = [i, i + 1]
s.bonds.typeid[:] = numpy.random.randint(0, 3, size=s.bonds.N)
s.bonds.types = ['bondA', 'bondB', 'bondC', 'bondD']
s.angles.N = N - 2
for i in range(s.angles.N):
s.angles.group[i, :] = [i, i + 1, i + 2]
s.angles.typeid[:] = numpy.random.randint(0, 3, size=s.angles.N)
s.angles.types = ['angleA', 'angleB', 'angleC', 'angleD']
s.dihedrals.N = N - 3
for i in range(s.dihedrals.N):
s.dihedrals.group[i, :] = [i, i + 1, i + 2, i + 3]
s.dihedrals.typeid[:] = numpy.random.randint(0, 3, size=s.dihedrals.N)
s.dihedrals.types = [
'dihedralA', 'dihedralB', 'dihedralC', 'dihedralD'
]
s.impropers.N = N - 3
for i in range(s.impropers.N):
s.impropers.group[i, :] = [i, i + 1, i + 2, i + 3]
s.impropers.typeid[:] = numpy.random.randint(0, 3, size=s.impropers.N)
s.impropers.types = [
'improperA', 'improperB', 'improperC', 'improperD'
]
s.pairs.N = N - 1
for i in range(s.pairs.N):
s.pairs.group[i, :] = [i, i + 1]
s.pairs.typeid[:] = numpy.random.randint(0, 3, size=s.pairs.N)
s.pairs.types = ['pairA', 'pairB', 'pairC', 'pairD']
s.constraints.N = N - 1
for i in range(s.constraints.N):
s.constraints.group[i, :] = [i, i + 1]
s.constraints.value[:] = numpy.random.uniform(1,
10,
size=s.constraints.N)
return s
def s():
return Snapshot()
