def _runtest(self, num_walkers, num_cycles, dimension, debug_prints=False):
print("Random walk simulation with: ")
print("Dimension =", dimension)
print("Probability =", self.probability)
print("Number of Walkers", num_walkers)
print("Number of Cycles", num_cycles)
# set up initial state for walkers
positions = np.zeros((1, dimension))
init_state = WalkerState(positions=positions, time=0.0)
# create list of init_walkers
initial_weight = 1 / num_walkers
init_walkers = []
# init_walkers, n_cycles = get_final_state(path, num_walkers)
init_walkers = [
Walker(init_state, initial_weight) for i in range(num_walkers)
]
# set up raunner for system
runner = RandomWalkRunner(dimension=dimension,
probability=self.probability)
units = dict(UNIT_NAMES)
# instantiate a revo unbindingboudaryconditiobs
segment_length = 10
# set up the reporter
randomwalk_system_top_json = self.generate_topology()
hdf5_reporter = WepyHDF5Reporter(self.hdf5_reporter_path,
mode='w',
save_fields=SAVE_FIELDS,
topology=randomwalk_system_top_json,
resampler=self.resampler,
units=dict(UNITS),
n_dims=dimension)
# running the simulation
sim_manager = Manager(init_walkers,
runner=runner,
resampler=self.resampler,
work_mapper=Mapper(),
reporters=[hdf5_reporter])
# run a simulation with the manager for n_steps cycles of length 1000 each
steps = [segment_length for i in range(num_cycles)]
print("Start simulation")
sim_manager.run_simulation(num_cycles,
steps,
debug_prints=debug_prints)
print("Finished Simulation")
def main(n_walkers=36,
n_workers=12,
n_runs=1,
n_cycles=20,
n_steps=100,
continue_sim=False):
runner = WestpaRunner()
init_state = WestpaWalkerState.from_bstate(
struct_data_ref='$WEST_SIM_ROOT/bstates/0')
work_mapper = WorkerMapper(worker_type=Worker, num_workers=n_workers)
if continue_sim:
# init_walkers = walkers_from_disk(n_expected_walkers=n_walkers)
init_walkers, start_cycle = WalkersPickleReporter.load_most_recent_cycle(
debug_prints=True)
else:
start_cycle = 0
init_weight = 1.0 / n_walkers
init_walkers = [
Walker(deepcopy(init_state), init_weight) for i in range(n_walkers)
]
unb_distance = PairDistance()
resampler = REVOResampler(distance=unb_distance, init_state=init_state)
reporters = [WalkersPickleReporter(freq=10)]
# Instantiate a simulation manager
sim_manager = Manager(init_walkers,
runner=runner,
resampler=resampler,
boundary_conditions=NoBC(),
work_mapper=work_mapper,
reporters=reporters)
segment_lengths = [n_steps for i in range(start_cycle + n_cycles)]
### RUN the simulation
for run_idx in range(n_runs):
print("Starting run: {}".format(run_idx))
sim_manager.continue_run_simulation(
0,
n_cycles,
segment_lengths,
num_workers=n_workers,
start_cycle=start_cycle) #, debug_prints=True)
print("Finished run: {}".format(run_idx))
def gen_sim_manager(self, start_snapshot, configuration):
"""
Parameters
----------
start_snapshot :
configuration :
Returns
-------
"""
# construct the sim manager, in a wepy specific way
sim_manager = Manager(
start_snapshot.walkers,
runner=start_snapshot.apparatus.filters[0],
boundary_conditions=start_snapshot.apparatus.filters[1],
resampler=start_snapshot.apparatus.filters[2],
# configuration options
work_mapper=configuration.work_mapper,
reporters=configuration.reporters)
return sim_manager
n_walkers = int(sys.argv[3])
print("Number of steps: {}".format(n_steps))
print("Number of cycles: {}".format(n_cycles))
# create the initial walkers
init_weight = 1.0 / n_walkers
init_walkers = [
Walker(OpenMMState(init_sim_state), init_weight)
for i in range(n_walkers)
]
# initialize the simulation manager
sim_manager = Manager(init_walkers,
runner=runner,
resampler=resampler,
boundary_conditions=ubc,
work_mapper=mapper,
reporters=reporters)
# make a number of steps for each cycle. In principle it could be
# different each cycle
steps = [n_steps for i in range(n_cycles)]
# actually run the simulation
print("Starting run: {}".format(0))
sim_manager.run_simulation(n_cycles, steps)
print("Finished run: {}".format(0))
print("Finished first file")
def main(n_runs,
n_cycles,
steps,
n_walkers,
n_workers=1,
debug_prints=False,
seed=None):
## Load objects needed for various purposes
# load a json string of the topology
with open(json_top_path, mode='r') as rf:
sEH_TPPU_system_top_json = rf.read()
# an openmm.State object for setting the initial walkers up
with open(omm_state_path, mode='rb') as rf:
omm_state = pickle.load(rf)
## set up the OpenMM Runner
# load the psf which is needed for making a system in OpenMM with
# CHARMM force fields
psf = omma.CharmmPsfFile(charmm_psf_path)
# set the box size lengths and angles
lengths = [CUBE_LENGTH for i in range(3)]
angles = [CUBE_ANGLE for i in range(3)]
psf.setBox(*lengths, *angles)
# charmm forcefields parameters
params = omma.CharmmParameterSet(*charmm_param_paths)
# create a system using the topology method giving it a topology and
# the method for calculation
system = psf.createSystem(params,
nonbondedMethod=omma.CutoffPeriodic,
nonbondedCutoff=NONBONDED_CUTOFF,
constraints=omma.HBonds)
# make this a constant temperature and pressure simulation at 1.0
# atm, 300 K, with volume move attempts every 50 steps
barostat = omm.MonteCarloBarostat(PRESSURE, TEMPERATURE, VOLUME_MOVE_FREQ)
# add it as a "Force" to the system
system.addForce(barostat)
# make an integrator object that is constant temperature
integrator = omm.LangevinIntegrator(TEMPERATURE, FRICTION_COEFFICIENT,
STEP_SIZE)
# set up the OpenMMRunner with the system
runner = OpenMMRunner(system, psf.topology, integrator, platform=PLATFORM)
# the initial state, which is used as reference for many things
init_state = OpenMMState(omm_state)
## Make the distance Metric
# load the crystal structure coordinates
crystal_traj = mdj.load_pdb(pdb_path)
# get the atoms in the binding site according to the crystal structure
bs_idxs = binding_site_atoms(crystal_traj.top, LIG_RESID,
crystal_traj.xyz[0])
lig_idxs = ligand_idxs(crystal_traj.top, LIG_RESID)
prot_idxs = protein_idxs(crystal_traj.top)
# make the distance metric with the ligand and binding site
# indices for selecting atoms for the image and for doing the
# alignments to only the binding site. All images will be aligned
# to the reference initial state
unb_distance = UnbindingDistance(lig_idxs, bs_idxs, init_state)
## Make the resampler
# make a Wexplore resampler with default parameters and our
# distance metric
resampler = WExploreResampler(distance=unb_distance,
init_state=init_state,
max_n_regions=MAX_N_REGIONS,
max_region_sizes=MAX_REGION_SIZES,
pmin=PMIN,
pmax=PMAX)
## Make the Boundary Conditions
# makes ref_traj and selects lingand_atom and protein atom indices
# instantiate a revo unbindingboudaryconditiobs
ubc = UnbindingBC(cutoff_distance=CUTOFF_DISTANCE,
initial_state=init_state,
topology=crystal_traj.topology,
ligand_idxs=lig_idxs,
receptor_idxs=prot_idxs)
## make the reporters
# WepyHDF5
# make a dictionary of units for adding to the HDF5
# open it in truncate mode first, then switch after first run
hdf5_reporter = WepyHDF5Reporter(
hdf5_path,
mode='w',
# the fields of the State that will be saved in the HDF5 file
save_fields=SAVE_FIELDS,
# the topology in a JSON format
topology=sEH_TPPU_system_top_json,
# the resampler and boundary
# conditions for getting data
# types and shapes for saving
resampler=resampler,
boundary_conditions=ubc,
# the units to save the fields in
units=dict(UNITS),
# sparse (in time) fields
sparse_fields=dict(SPARSE_FIELDS),
# sparse atoms fields
main_rep_idxs=np.concatenate((lig_idxs, prot_idxs)),
all_atoms_rep_freq=ALL_ATOMS_SAVE_FREQ)
dashboard_reporter = WExploreDashboardReporter(
dashboard_path,
mode='w',
step_time=STEP_SIZE.value_in_unit(unit.second),
max_n_regions=resampler.max_n_regions,
max_region_sizes=resampler.max_region_sizes,
bc_cutoff_distance=ubc.cutoff_distance)
setup_reporter = SetupReporter(setup_state_path, mode='w')
restart_reporter = RestartReporter(restart_state_path, mode='w')
reporters = [
hdf5_reporter, dashboard_reporter, setup_reporter, restart_reporter
]
## The work mapper
# we use a mapper that uses GPUs
work_mapper = WorkerMapper(worker_type=OpenMMGPUWorker,
num_workers=n_workers)
## Combine all these parts and setup the simulation manager
# set up parameters for running the simulation
# initial weights
init_weight = 1.0 / n_walkers
# a list of the initial walkers
init_walkers = [
Walker(OpenMMState(omm_state), init_weight) for i in range(n_walkers)
]
# Instantiate a simulation manager
sim_manager = Manager(init_walkers,
runner=runner,
resampler=resampler,
boundary_conditions=ubc,
work_mapper=work_mapper,
reporters=reporters)
### RUN the simulation
for run_idx in range(n_runs):
print("Starting run: {}".format(run_idx))
sim_manager.run_simulation(n_cycles, steps, debug_prints=True)
print("Finished run: {}".format(run_idx))
n_cycles = 1
# the number of MD dynamics steps for each cycle
n_steps = 1000000
steps = [n_steps for i in range(n_cycles)]
# number of parallel simulations
n_walkers = 10
# the work mapper
# work_mapper = ThreadMapper()
work_mapper = Mapper()
# create the initial walkers with equal weights
with start_action(action_type="Init Walkers") as ctx:
init_weight = 1.0 / n_walkers
init_walkers = [Walker(copy(init_state), init_weight) for i in range(n_walkers)]
with start_action(action_type="Init Sim Manager") as ctx:
sim_manager = Manager(
init_walkers,
runner=runner,
resampler=resampler,
work_mapper=work_mapper)
# run the simulation and get the results
with start_action(action_type="Simulation") as ctx:
final_walkers, _ = sim_manager.run_simulation(n_cycles, steps)
def run_sim(init_state_path, json_top_path, forcefield_paths, n_cycles,
n_steps, n_workers, **kwargs):
#### Wepy Orchestrator
# load the wepy.OpenMMState
with open(init_state_path, 'rb') as rf:
init_state = pickle.load(rf)
### Apparatus
# Runner components
# load the JSON for the topology
with open(json_top_path) as rf:
json_top_str = rf.read()
# load it with mdtraj and then convert to openmm
mdj_top = json_to_mdtraj_topology(json_top_str)
omm_topology = mdj_top.to_openmm()
# we need to use the box vectors for setting the simulation up,
# paying mind to the units
box_vectors = init_state['box_vectors'] * init_state.box_vectors_unit
# set the box to the last box size from equilibration
omm_topology.setPeriodicBoxVectors(box_vectors)
# force field parameters
force_field = omma.ForceField(*forcefield_paths)
# create a system using the topology method giving it a topology and
# the method for calculation
runner_system = force_field.createSystem(omm_topology,
nonbondedMethod=NONBONDED_METHOD,
nonbondedCutoff=NONBONDED_CUTOFF,
constraints=MD_CONSTRAINTS,
rigidWater=RIGID_WATER,
removeCMMotion=REMOVE_CM_MOTION,
hydrogenMass=HYDROGEN_MASS)
# barostat to keep pressure constant
runner_barostat = omm.MonteCarloBarostat(PRESSURE, TEMPERATURE,
VOLUME_MOVE_FREQ)
# add it to the system
runner_system.addForce(runner_barostat)
# set up for a short simulation to runner and prepare
# instantiate an integrator
runner_integrator = omm.LangevinIntegrator(TEMPERATURE,
FRICTION_COEFFICIENT, STEP_TIME)
## Runner
runner = OpenMMRunner(runner_system,
omm_topology,
runner_integrator,
platform=PLATFORM)
## Resampler
# Distance Metric
# TODO set distance metric
distance_metric = None
# TODO set resampler
resampler = None
## Boundary Conditions
# TODO optional: set the boundary conditions
bc = None
# apparatus = WepySimApparatus(runner, resampler=resampler,
# boundary_conditions=bc)
print("created apparatus")
## CONFIGURATION
# the idxs of the main representation to save in the output files,
# it is just the protein and the ligand
# TODO optional: set the main representation atom indices
main_rep_idxs = None
# REPORTERS
# list of reporter classes and partial kwargs for using in the
# orchestrator
hdf5_reporter_kwargs = {
'main_rep_idxs': main_rep_idxs,
'topology': json_top_str,
'resampler': resampler,
'boundary_conditions': bc,
# general parameters
'save_fields': SAVE_FIELDS,
'units': dict(UNITS),
'sparse_fields': dict(SPARSE_FIELDS),
'all_atoms_rep_freq': ALL_ATOMS_SAVE_FREQ
}
# get all the reporters together. Order is important since they
# will get paired with the kwargs
reporter_classes = [
WepyHDF5Reporter,
]
# collate the kwargs in the same order
reporter_kwargs = [
hdf5_reporter_kwargs,
]
# make the configuration with all these reporters and the default number of workers
configuration = Configuration(n_workers=DEFAULT_N_WORKERS,
reporter_classes=reporter_classes,
reporter_partial_kwargs=reporter_kwargs,
config_name="no-orch")
# then instantiate them
reporters = configuration._gen_reporters()
print("created configuration")
### Initial Walkers
init_walkers = [
Walker(deepcopy(init_state), INIT_WEIGHT) for _ in range(N_WALKERS)
]
print("created init walkers")
### Orchestrator
# orchestrator = Orchestrator(apparatus,
# default_init_walkers=init_walkers,
# default_configuration=configuration)
### Work Mapper
if PLATFORM in ('OpenCL', 'CUDA'):
# we use a mapper that uses GPUs
work_mapper = WorkerMapper(worker_type=OpenMMGPUWorker,
num_workers=n_workers)
if PLATFORM in ('Reference', 'CPU'):
# we just use the standard mapper
work_mapper = Mapper
### Simulation Manager
sim_manager = Manager(init_walkers,
runner=runner,
resampler=resampler,
boundary_conditions=bc,
work_mapper=work_mapper,
reporters=reporters)
### Run the simulation
steps = [n_steps for _ in range(n_cycles)]
sim_manager.run_simulation(n_cycles, steps)
def run_sim(init_state_path,
json_top_path,
forcefield_paths,
n_cycles,
n_steps,
platform,
n_workers,
lig_ff=None,
**kwargs):
# add in the ligand force fields
assert lig_ff is not None, "must give ligand forcefield"
forcefield_paths.append(lig_ff)
#### Wepy Orchestrator
# load the wepy.OpenMMState
with open(init_state_path, 'rb') as rf:
init_state = pickle.load(rf)
### Apparatus
# Runner components
# load the JSON for the topology
with open(json_top_path) as rf:
json_top_str = rf.read()
# load it with mdtraj and then convert to openmm
mdj_top = json_to_mdtraj_topology(json_top_str)
omm_topology = mdj_top.to_openmm()
# we need to use the box vectors for setting the simulation up,
# paying mind to the units
box_vectors = init_state['box_vectors'] * init_state.box_vectors_unit
positions = init_state['positions'] * init_state.positions_unit
# set the box to the last box size from equilibration
omm_topology.setPeriodicBoxVectors(box_vectors)
# force field parameters
force_field = omma.ForceField(*forcefield_paths)
# create a system using the topology method giving it a topology and
# the method for calculation
runner_system = force_field.createSystem(omm_topology,
nonbondedMethod=NONBONDED_METHOD,
nonbondedCutoff=NONBONDED_CUTOFF,
constraints=MD_CONSTRAINTS,
rigidWater=RIGID_WATER,
removeCMMotion=REMOVE_CM_MOTION,
hydrogenMass=HYDROGEN_MASS)
# barostat to keep pressure constant
runner_barostat = omm.MonteCarloBarostat(PRESSURE, TEMPERATURE,
VOLUME_MOVE_FREQ)
# add it to the system
runner_system.addForce(runner_barostat)
# set up for a short simulation to runner and prepare
# instantiate an integrator
runner_integrator = omm.LangevinIntegrator(TEMPERATURE,
FRICTION_COEFFICIENT, STEP_TIME)
## Runner
runner = OpenMMRunner(runner_system,
omm_topology,
runner_integrator,
platform=platform)
## Resampler
# Distance Metric
lig_idxs = ligand_idxs(json_top_str)
prot_idxs = protein_idxs(json_top_str)
bs_idxs = binding_site_idxs(json_top_str, positions, box_vectors, CUTOFF)
# set distance metric
distance_metric = UnbindingDistance(lig_idxs, bs_idxs, init_state)
# set resampler
resampler = WExploreResampler(distance=distance_metric,
init_state=init_state,
max_n_regions=MAX_N_REGIONS,
max_region_sizes=MAX_REGION_SIZES,
pmin=PMIN,
pmax=PMAX)
## Boundary Conditions
# optional: set the boundary conditions
bc = None
## CONFIGURATION
# the idxs of the main representation to save in the output files,
# it is just the protein and the ligand
# optional: set the main representation atom indices, set to None
# to save all the atoms in the 'positions' field
main_rep_idxs = np.concatenate((lig_idxs, prot_idxs))
# REPORTERS
# list of reporter classes and partial kwargs for using in the
# orchestrator
hdf5_reporter_kwargs = {
'main_rep_idxs': main_rep_idxs,
'topology': json_top_str,
'resampler': resampler,
'boundary_conditions': bc,
# general parameters
'save_fields': SAVE_FIELDS,
'units': dict(UNITS),
'sparse_fields': dict(SPARSE_FIELDS),
'all_atoms_rep_freq': ALL_ATOMS_SAVE_FREQ
}
# get all the reporters together. Order is important since they
# will get paired with the kwargs
reporter_classes = [
WepyHDF5Reporter,
]
# collate the kwargs in the same order
reporter_kwargs = [
hdf5_reporter_kwargs,
]
# make the configuration with all these reporters and the default
# number of workers. Don't be thrown off by this. You don't need
# this. It is just a convenient way to dynamically name the
# outputs of the reporters and parametrize the workers and worker
# mappers. This is mainly for use in the Orchestrator framework
# but it is useful here just for batch naming everything.
configuration = Configuration(n_workers=DEFAULT_N_WORKERS,
reporter_classes=reporter_classes,
reporter_partial_kwargs=reporter_kwargs,
config_name="no-orch",
mode='w')
# then instantiate the reporters from the configuration. THis
# localizes the file paths to outputs and applies the key-word
# arguments specified above.
reporters = configuration._gen_reporters()
print("created configuration")
### Initial Walkers
init_walkers = [
Walker(deepcopy(init_state), INIT_WEIGHT) for _ in range(N_WALKERS)
]
print("created init walkers")
### Work Mapper
if platform in ('OpenCL', 'CUDA'):
# we use a mapper that uses GPUs
work_mapper = WorkerMapper(worker_type=OpenMMGPUWorker,
num_workers=n_workers)
elif platform in ('CPU', ):
# for the CPU we can choose how many threads to use per walker.
worker_attributes = {'num_threads': N_CPU_THREADS}
work_mapper = WorkerMapper(worker_type=OpenMMCPUWorker,
worker_attributes=worker_attributes,
num_workers=n_workers)
elif platform in ('Reference', ):
# we just use the standard mapper for in serial
work_mapper = Mapper
### Simulation Manager
sim_manager = Manager(init_walkers,
runner=runner,
resampler=resampler,
boundary_conditions=bc,
work_mapper=work_mapper,
reporters=reporters)
### Run the simulation
steps = [n_steps for _ in range(n_cycles)]
sim_manager.run_simulation(n_cycles, steps)
def test_lj_sim_manager_openmm_integration_run(
self,
class_tmp_path_factory,
boundary_condition_class,
resampler_class,
work_mapper_class,
platform,
lj_params,
lj_omm_sys,
lj_integrator,
lj_reporter_classes,
lj_reporter_kwargs,
lj_init_walkers,
lj_openmm_runner,
lj_unbinding_bc,
lj_wexplore_resampler,
lj_revo_resampler,
):
"""Run all combinations of components in the fixtures for the smallest
amount of time, just to make sure they all work together and don't give errors."""
logging.getLogger().setLevel(logging.DEBUG)
install_mp_handler()
logging.debug("Starting the test")
print("starting the test")
# the configuration class gives us a convenient way to
# parametrize our reporters for the locale
from wepy.orchestration.configuration import Configuration
# the runner
from wepy.runners.openmm import OpenMMRunner
# mappers
from wepy.work_mapper.mapper import Mapper
from wepy.work_mapper.worker import WorkerMapper
from wepy.work_mapper.task_mapper import TaskMapper
# the worker types for the WorkerMapper
from wepy.work_mapper.worker import Worker
from wepy.runners.openmm import OpenMMCPUWorker, OpenMMGPUWorker
# the walker task types for the TaskMapper
from wepy.work_mapper.task_mapper import WalkerTaskProcess
from wepy.runners.openmm import OpenMMCPUWalkerTaskProcess, OpenMMGPUWalkerTaskProcess
n_cycles = 1
n_steps = 2
num_workers = 2
# generate the reporters and temporary directory for this test
# combination
tmpdir_template = 'lj_fixture_{plat}-{wm}-{res}-{bc}'
tmpdir_name = tmpdir_template.format(plat=platform,
wm=work_mapper_class,
res=resampler_class,
bc=boundary_condition_class)
# make a temporary directory for this configuration to work with
tmpdir = str(class_tmp_path_factory.mktemp(tmpdir_name))
# make a config so that the reporters get parametrized properly
reporters = Configuration(
work_dir=tmpdir,
reporter_classes=lj_reporter_classes,
reporter_partial_kwargs=lj_reporter_kwargs).reporters
steps = [n_steps for _ in range(n_cycles)]
# choose the components based on the parametrization
boundary_condition = None
resampler = None
walker_fixtures = [lj_init_walkers]
runner_fixtures = [lj_openmm_runner]
boundary_condition_fixtures = [lj_unbinding_bc]
resampler_fixtures = [lj_wexplore_resampler, lj_revo_resampler]
walkers = lj_init_walkers
boundary_condition = [
boundary_condition
for boundary_condition in boundary_condition_fixtures
if type(boundary_condition).__name__ == boundary_condition_class
][0]
resampler = [
resampler for resampler in resampler_fixtures
if type(resampler).__name__ == resampler_class
][0]
assert boundary_condition is not None
assert resampler is not None
# generate the work mapper given the type and the platform
work_mapper_classes = {
mapper_class.__name__: mapper_class
for mapper_class in [Mapper, WorkerMapper, TaskMapper]
}
# # select the right one given the option
# work_mapper_type = [mapper_type for mapper_type in work_mapper_classes
# if type(mapper_type).__name__ == work_mapper_class][0]
# decide based on the platform and the work mapper which
# platform dependent components to build
if work_mapper_class == 'Mapper':
# then there is no settings
work_mapper = Mapper()
elif work_mapper_class == 'WorkerMapper':
if platform == 'CUDA' or platform == 'OpenCL':
work_mapper = WorkerMapper(num_workers=num_workers,
worker_type=OpenMMGPUWorker,
device_ids={
'0': 0,
'1': 1
},
proc_start_method='spawn')
if platform == 'OpenCL':
work_mapper = WorkerMapper(
num_workers=num_workers,
worker_type=OpenMMGPUWorker,
device_ids={
'0': 0,
'1': 1
},
)
elif platform == 'CPU':
work_mapper = WorkerMapper(
num_workers=num_workers,
worker_type=OpenMMCPUWorker,
worker_attributes={'num_threads': 1})
elif platform == 'Reference':
work_mapper = WorkerMapper(
num_workers=num_workers,
worker_type=Worker,
)
elif work_mapper_class == 'TaskMapper':
if platform == 'CUDA':
work_mapper = TaskMapper(
num_workers=num_workers,
walker_task_type=OpenMMGPUWalkerTaskProcess,
device_ids={
'0': 0,
'1': 1
},
proc_start_method='spawn')
elif platform == 'OpenCL':
work_mapper = TaskMapper(
num_workers=num_workers,
walker_task_type=OpenMMGPUWalkerTaskProcess,
device_ids={
'0': 0,
'1': 1
})
elif platform == 'CPU':
work_mapper = TaskMapper(
num_workers=num_workers,
walker_task_type=OpenMMCPUWalkerTaskProcess,
worker_attributes={'num_threads': 1})
elif platform == 'Reference':
work_mapper = TaskMapper(
num_workers=num_workers,
worker_type=WalkerTaskProcess,
)
else:
raise ValueError("Platform {} not recognized".format(platform))
# initialize the runner with the platform
runner = OpenMMRunner(lj_omm_sys.system,
lj_omm_sys.topology,
lj_integrator,
platform=platform)
logging.debug("Constructing the manager")
manager = Manager(walkers,
runner=runner,
boundary_conditions=boundary_condition,
resampler=resampler,
work_mapper=work_mapper,
reporters=reporters)
# since different work mappers need different process start
# methods for different platforms i.e. CUDA and linux fork
# vs. spawn we choose the appropriate one for each method.
logging.debug("Starting the simulation")
walkers, filters = manager.run_simulation(n_cycles,
steps,
num_workers=num_workers)
def get_char_distance(dimension, num_walkers):
"""Calculate the characteristic value.
Runs one cycle simulation and calculates the characteristic
distance value.
Parameters
----------
dimension: int
The dimension of the random walk space.
num_walkers: int
The number of walkers.
Returns
-------
characteristic distance : float
The characteristic distance value.
"""
# set up initial state for walkers
positions = np.zeros((1, dimension))
init_state = WalkerState(positions=positions, time=0.0)
# set up the distance function
rw_distance = RandomWalkDistance()
# set up the REVO Resampler with the parameters
resampler = REVOResampler(distance=rw_distance,
pmin=PMIN,
pmax=PMAX,
init_state=init_state,
char_dist=1,
merge_dist=MERGE_DIST)
# create list of init_walkers
initial_weight = 1 / num_walkers
init_walkers = [
Walker(init_state, initial_weight) for i in range(num_walkers)
]
# set up raunner for system
runner = RandomWalkRunner(probability=PROBABILITY)
n_steps = 10
mapper = Mapper()
# running the simulation
sim_manager = Manager(init_walkers,
runner=runner,
resampler=resampler,
work_mapper=mapper)
print("Running simulation")
#runs for one cycle
sim_manager.init(num_walkers)
new_walkers = sim_manager.run_segment(init_walkers, n_steps, 0)
dist_matrix, _ = resampler._all_to_all_distance(new_walkers)
return np.average(dist_matrix)
def _run(self, num_runs, num_cycles, num_walkers):
"""Runs a random walk simulation.
Parameters
----------
num_runs: int
The number independet simulations.
num_cycles: int
The number of cycles that will be run in the simulation.
num_walkers: int
The number of walkers.
"""
print("Random walk simulation with: ")
print("Dimension = {}".format(self.dimension))
print("Probability = {}".format(self.probability))
print("Number of Walkers = {}".format(num_walkers))
print("Number of Cycles ={}".format(num_cycles))
# set up initial state for walkers
positions = np.zeros((1, self.dimension))
init_state = WalkerState(positions=positions, time=0.0)
# create list of init_walkers
initial_weight = 1 / num_walkers
init_walkers = []
init_walkers = [
Walker(init_state, initial_weight) for i in range(num_walkers)
]
# set up raunner for system
runner = RandomWalkRunner(probability=self.probability)
units = dict(UNIT_NAMES)
# instantiate a revo unbindingboudaryconditiobs
segment_length = 10
# set up the reporter
randomwalk_system_top_json = self.generate_topology()
hdf5_reporter = WepyHDF5Reporter(file_path=self.hdf5_filename,
mode='w',
save_fields=SAVE_FIELDS,
topology=randomwalk_system_top_json,
resampler=self.resampler,
units=dict(UNITS),
n_dims=self.dimension)
# running the simulation
sim_manager = Manager(init_walkers,
runner=runner,
resampler=self.resampler,
work_mapper=Mapper(),
reporters=[hdf5_reporter])
# run a simulation with the manager for n_steps cycles of length 1000 each
steps = [segment_length for i in range(num_cycles)]
### RUN the simulation
for run_idx in range(num_runs):
print("Starting run: {}".format(run_idx))
sim_manager.run_simulation(num_cycles, steps)
print("Finished run: {}".format(run_idx))
print("Finished Simulation")
def new_sim_manager(self, file_report_suffix=None, reporter_base_path=None):
"""Generate a simulation manager from the objects in this restarter.
All objects are deepcopied so that this restarter object can
be used multiple times, without reading from disk again.
If a `file_report_suffix` string is given all reporters
inheriting from FileReporter will have their `file_path`
attribute modified. `file_report_suffix` will be appended to
the first clause (clauses here are taken to be portions of the
file name separated by '.'s) so that for 'file.txt' the
substring 'file' will be replaced by 'file{}'. Where the
suffix is formatted into that string.
"""
# check the arguments for correctness
if (file_report_suffix is not None) or \
(reporter_base_path is not None):
# if just the base path is given
if file_report_suffix is None:
assert type(reporter_base_path) is str, \
"'reporter_base_path' must be a string, given {}".format(type(reporter_base_path))
if reporter_base_path is None:
assert type(file_report_suffix) is str, \
"'file_report_suffix' must be a string, given {}".format(type(file_report_suffix))
# copy the reporters from this objects list of reporters, we
# also need to replace, the restart reporter in that list with
# this one.
reporters = []
for reporter in self.reporters:
# if the reporter is a restart reporter we replace it with
# a copy of this reporter, it will be mutated later
# potentially to change the path to save the pickle
if isinstance(reporter, RestartReporter):
reporters.append(deepcopy(self))
else:
# otherwise make a copy of the reporter
reporters.append(deepcopy(reporter))
# copy all of the other objects before construction
restart_walkers = deepcopy(self.restart_walkers)
runner = deepcopy(self.runner)
resampler = deepcopy(self.resampler)
boundary_conditions = deepcopy(self.boundary_conditions)
work_mapper = deepcopy(self.work_mapper)
# modify them if this was specified
# update the FileReporter paths
# iterate through the reporters and add the suffix to the
# FileReporter subclasses
for reporter in reporters:
# check if the reporter class is a FileReporter subclass
if issubclass(type(reporter), FileReporter):
# because we are making a new file with any change, we
# need to modify the access mode to a conservative
# creation mode
reporter.mode = 'x'
if file_report_suffix is not None:
filename = osp.basename(reporter.file_path)
# get the clauses
clauses = filename.split('.')
# make a template out of the first clause
template_clause = clauses[0] + "{}"
# fill in the suffix to the template clause
mod_clause = template_clause.format(file_report_suffix)
# combine them back into a filename
new_filename = '.'.join([mod_clause, *clauses[1:]])
else:
# if we don't have a suffix just return the original name
filename = osp.basename(reporter.file_path)
new_filename = filename
# if a new base path was given make that the path
# to the filename
if reporter_base_path is not None:
new_path = osp.join(reporter_base_path, new_filename)
# if it wasn't given, add the rest of the original path back
else:
new_path = osp.join(osp.dirname(reporter.file_path), new_filename)
# make a copy of the reporter and pass this to the
# sim manager instead of the one in the object
new_reporter = reporter
new_reporter.file_path = new_path
# construct the sim manager
sim_manager = Manager(restart_walkers,
runner=runner,
resampler=resampler,
boundary_conditions=boundary_conditions,
work_mapper=work_mapper,
reporters=reporters)
return sim_manager