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 test_mapper(self):
mapper = Mapper(segment_func=task_fail)
mapper.init()
with pytest.raises(TaskException) as task_exc_info:
results = mapper.map(gen_walkers())
mapper.cleanup()
def test_mapper(self):
mapper = Mapper(segment_func=task_pass)
mapper.init()
results = mapper.map(gen_walkers())
assert all([res.state['num'] == TASK_PASS_ANSWER[i]
for i, res in enumerate(results)])
mapper.cleanup()
def test_Mapper(self, n_walkers, benchmark):
mapper = Mapper(segment_func=task_pass)
mapper.init()
def thunk():
return mapper.map(gen_walkers(n_walkers))
result = benchmark(thunk)
mapper.cleanup()
units=units)
dashboard_reporter = WExploreDashboardReporter(
file_path='./outputs/wepy.dash.txt',
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)
reporters = [hdf5_reporter, dashboard_reporter]
## Work Mapper
# a simple work mapper
mapper = Mapper()
## Run the simulation
if __name__ == "__main__":
if sys.argv[1] == "-h" or sys.argv[1] == "--help":
print("arguments: n_cycles, n_steps, n_walkers")
else:
n_cycles = int(sys.argv[1])
n_steps = int(sys.argv[2])
n_walkers = int(sys.argv[3])
print("Number of steps: {}".format(n_steps))
print("Number of cycles: {}".format(n_cycles))
# Run the simulation
# number of cycles of WE to perform
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:
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 __init__(self, init_walkers,
runner = None,
work_mapper = None,
resampler = None,
boundary_conditions = None,
reporters = None):
"""Constructor for Manager.
Arguments
---------
init_walkers : list of walkers
The list of the initial walkers that will be run.
runner : object implementing the Runner interface
The runner to be used for propagating sampling segments of walkers.
work_mapper : object implementing the WorkMapper interface
The object that will be used to perform a set of runner
segments in a cycle.
resampler : object implementing the Resampler interface
The resampler to be used in the simulation
boundary_conditions : object implementing BoundaryCondition interface, optional
The boundary conditions to apply to walkers
reporters : list of objects implenting the Reporter interface, optional
Reporters to be used. You should provide these if you want to keep data.
Warnings
--------
While reporters are strictly optional, you probably want to
provide some because the simulation manager provides no
utilities for saving data from the simulations except for the
walkers at the end of a cycle or simulation.
See Also
--------
wepy.reporter.hdf5 : The standard reporter for molecular simulations in wepy.
wepy.orchestration.orchestrator.Orchestrator : for running simulations with
checkpointing, restarting, reporter localization, and configuration hotswapping
with command line interface.
"""
self.init_walkers = init_walkers
self.n_init_walkers = len(init_walkers)
# the runner is the object that runs dynamics
self.runner = runner
# the resampler
self.resampler = resampler
# object for boundary conditions
self.boundary_conditions = boundary_conditions
# the method for writing output
if reporters is None:
self.reporters = []
else:
self.reporters = reporters
if work_mapper is None:
self.work_mapper = Mapper()
else:
self.work_mapper = work_mapper
class Manager(object):
"""The class that coordinates wepy simulations.
The Manager class is the lynchpin of wepy simulations and is where
all the different components are composed.
Strictly speaking the Manager defines the interfaces each
component must provide to function.
Developers can call `run_cycle` directly but the following
convenience functions are provided to run many cycles in
succession as a single 'run' with consecutive cycle idxs:
- run_simulation_by_time
- run_simulation
The corresponding 'continue' run methods will simply pass a run
index to reporters indicating that the run continues another.
For these run methods the `init` method is called followed by
iterative calls to `run_cycle` and finally with a call to
`cleanup`.
The order of application of wepy components are:
- runner
- boundary_conditions
- resampler
- reporters
"""
REPORT_ITEM_KEYS = ('cycle_idx', 'n_segment_steps',
'new_walkers', 'resampled_walkers',
'warp_data', 'bc_data', 'progress_data',
'resampling_data', 'resampler_data',
'worker_segment_times', 'cycle_runner_time',
'cycle_bc_time', 'cycle_resampling_time',)
"""Keys of values that will be passed to reporters.
This indicates the values that the reporters will have access to.
"""
def __init__(self, init_walkers,
runner = None,
work_mapper = None,
resampler = None,
boundary_conditions = None,
reporters = None):
"""Constructor for Manager.
Arguments
---------
init_walkers : list of walkers
The list of the initial walkers that will be run.
runner : object implementing the Runner interface
The runner to be used for propagating sampling segments of walkers.
work_mapper : object implementing the WorkMapper interface
The object that will be used to perform a set of runner
segments in a cycle.
resampler : object implementing the Resampler interface
The resampler to be used in the simulation
boundary_conditions : object implementing BoundaryCondition interface, optional
The boundary conditions to apply to walkers
reporters : list of objects implenting the Reporter interface, optional
Reporters to be used. You should provide these if you want to keep data.
Warnings
--------
While reporters are strictly optional, you probably want to
provide some because the simulation manager provides no
utilities for saving data from the simulations except for the
walkers at the end of a cycle or simulation.
See Also
--------
wepy.reporter.hdf5 : The standard reporter for molecular simulations in wepy.
wepy.orchestration.orchestrator.Orchestrator : for running simulations with
checkpointing, restarting, reporter localization, and configuration hotswapping
with command line interface.
"""
self.init_walkers = init_walkers
self.n_init_walkers = len(init_walkers)
# the runner is the object that runs dynamics
self.runner = runner
# the resampler
self.resampler = resampler
# object for boundary conditions
self.boundary_conditions = boundary_conditions
# the method for writing output
if reporters is None:
self.reporters = []
else:
self.reporters = reporters
if work_mapper is None:
self.work_mapper = Mapper()
else:
self.work_mapper = work_mapper
def run_segment(self, walkers, segment_length):
"""Run a time segment for all walkers using the available workers.
Maps the work for running each segment for each walker using
the work mapper.
Walkers will have the same weights but different states.
Parameters
----------
walkers : list of walkers
segment_length : int
Number of steps to run in each segment.
Returns
-------
new_walkers : list of walkers
The walkers after the segment of sampling simulation.
"""
num_walkers = len(walkers)
logging.info("Starting segment")
new_walkers = list(self.work_mapper.map(walkers,
(segment_length for i in range(num_walkers)),
)
)
logging.info("Ending segment")
return new_walkers
def run_cycle(self, walkers, n_segment_steps, cycle_idx):
"""Run a full cycle of weighted ensemble simulation using each
component.
The order of application of wepy components are:
- runner
- boundary_conditions
- resampler
- reporters
The `init` method should have been called before this or
components may fail.
This method is not idempotent and will alter the state of wepy
components.
The cycle is not kept as a state variable of the simulation
manager and so myst be provided here. This motivation for this
is that a cycle index is really a property of a run and runs
can be composed in many ways and is then handled by
higher-level methods calling run_cycle.
Each component should implement its respective interface to be
called in this method, the order and names of the methods
called are as follows:
1. runner.pre_cycle
2. run_segment -> work_mapper.map(runner.run_segment)
3. runner.post_cycle
4. boundary_conditions.warp_walkers (if present)
5. resampler.resample
6. reporter.report for all reporters
The pre and post cycle calls to the runner allow for a parity
of one call per cycle to the runner.
The boundary_conditions component is optional, as are the
reporters (although it won't be very useful to run this
without any).
Parameters
----------
walkers : list of walkers
n_segment_steps : int
Number of steps to run in each segment.
cycle_idx : int
The index of this cycle.
Returns
-------
new_walkers : list of walkers
The resulting walkers of the cycle
sim_components : list
The runner, resampler, and boundary conditions
objects at the end of the cycle.
See Also
--------
run_simulation : To run a simulation by the number of cycles
run_simulation_by_time
"""
# this one is called to just easily be able to catch all the
# errors from it so we can cleanup if an error is caught
try:
return self._run_cycle(walkers, n_segment_steps, cycle_idx)
except Exception as err:
# if we catch any error we want to make sure that run the
# cleanup for everything. By policy this should make sure
# all running processes are killed (i.e. does not actually
# kill processes and the modules should implement this
# themselves in their cleanup method)
self.cleanup()
# then reraise the error
raise err
def _run_cycle(self, walkers, n_segment_steps, cycle_idx):
"""See run_cycle."""
logging.info("Begin cycle {}".format(cycle_idx))
# run the pre-cycle hook
self.runner.pre_cycle(walkers=walkers,
n_segment_steps=n_segment_steps,
cycle_idx=cycle_idx)
# run the segment
start = time.time()
new_walkers = self.run_segment(walkers, n_segment_steps)
end = time.time()
runner_time = end - start
# run post-cycle hook
self.runner.post_cycle()
logging.info("End cycle {}".format(cycle_idx))
# boundary conditions should be optional;
# initialize the warped walkers to the new_walkers and
# change them later if need be
warped_walkers = new_walkers
warp_data = []
bc_data = []
progress_data = {}
bc_time = 0.0
if self.boundary_conditions is not None:
# apply rules of boundary conditions and warp walkers through space
start = time.time()
bc_results = self.boundary_conditions.warp_walkers(new_walkers,
cycle_idx)
end = time.time()
bc_time = end - start
# warping results
warped_walkers = bc_results[0]
warp_data = bc_results[1]
bc_data = bc_results[2]
progress_data = bc_results[3]
if len(warp_data) > 0:
logging.info("Returned warp record in cycle {}".format(cycle_idx))
# resample walkers
start = time.time()
resampling_results = self.resampler.resample(warped_walkers)
end = time.time()
resampling_time = end - start
resampled_walkers = resampling_results[0]
resampling_data = resampling_results[1]
resampler_data = resampling_results[2]
# log the weights of the walkers after resampling
result_template_str = "|".join(["{:^5}" for i in range(self.n_init_walkers + 1)])
walker_weight_str = result_template_str.format("weight",
*[round(walker.weight, 3) for walker in resampled_walkers])
logging.info(walker_weight_str)
# make a dictionary of all the results that will be reported
report = {'cycle_idx' : cycle_idx,
'new_walkers' : new_walkers,
'warp_data' : warp_data,
'bc_data' : bc_data,
'progress_data' : progress_data,
'resampling_data' : resampling_data,
'resampler_data' : resampler_data,
'n_segment_steps' : n_segment_steps,
'worker_segment_times' : self.work_mapper.worker_segment_times,
'cycle_runner_time' : runner_time,
'cycle_bc_time' : bc_time,
'cycle_resampling_time' : resampling_time,
'resampled_walkers' : resampled_walkers}
# check that all of the keys that are specified for this sim
# manager are present
assert all([True if rep_key in report else False
for rep_key in self.REPORT_ITEM_KEYS])
# report results to the reporters
for reporter in self.reporters:
reporter.report(**report)
# prepare resampled walkers for running new state changes
walkers = resampled_walkers
# we also return a list of the "filters" which are the
# classes that are run on the initial walkers to produce
# the final walkers. THis is to satisfy a future looking
# interface in which the order and components of these
# filters are completely parametrizable. This may or may
# not be implemented in a future release of wepy but this
# interface is assumed by the orchestration classes for
# making snapshots of the simulations. The receiver of
# these should perform the copy to make sure they aren't
# mutated. We don't do this here for efficiency.
filters = [self.runner, self.boundary_conditions, self.resampler]
return walkers, filters
def init(self, num_workers=None, continue_run=None):
"""Initialize wepy configuration components for use at runtime.
This `init` method is different than the constructor
`__init__` method and instead calls the special `init` method
on all wepy components (runner, resampler, boundary
conditions, and reporters) at runtime.
This allows for a things that need to be done at runtime before a
simulation begins, e.g. opening files, that you don't want done at
construction time.
It calls the `init` methods on:
- work_mapper
- reporters
Passes the segment_func of the runner and the number of
workers to the work_mapper.
Passes the following things to each reporter `init` method:
- init_walkers
- runner
- resampler
- boundary_conditions
- work_mapper
- reporters
- continue_run
Parameters
----------
num_workers : int
The number of workers to use in the work mapper.
(Default value = None)
continue_run : int
Index of a run this one is continuing.
(Default value = None)
"""
logging.info("Starting simulation")
# initialize the work_mapper with the function it will be
# mapping and the number of workers, this may include things like starting processes
# etc.
self.work_mapper.init(segment_func=self.runner.run_segment,
num_workers=num_workers)
# init the reporter
for reporter in self.reporters:
reporter.init(init_walkers=self.init_walkers,
runner=self.runner,
resampler=self.resampler,
boundary_conditions=self.boundary_conditions,
work_mapper=self.work_mapper,
reporters=self.reporters,
continue_run=continue_run)
def cleanup(self):
"""Perform cleanup actions for wepy configuration components.
Allow components to perform actions before ending the main
simulation manager process.
Calls the `cleanup` method on:
- work_mapper
- reporters
Passes nothing to the work mapper.
Passes the following to each reporter:
- runner
- work_mapper
- resampler
- boundary_conditions
- reporters
"""
# cleanup the mapper
self.work_mapper.cleanup()
# cleanup things associated with the reporter
for reporter in self.reporters:
reporter.cleanup(runner=self.runner,
work_mapper=self.work_mapper,
resampler=self.resampler,
boundary_conditions=self.boundary_conditions,
reporters=self.reporters)
def run_simulation_by_time(self, run_time, segments_length, num_workers=None):
"""Run a simulation for a certain amount of time.
This starts timing as soon as this is called. If the time
before running a new cycle is greater than the runtime the run
will exit after cleaning up. Once a cycle is started it may
also run over the wall time.
All this does is provide a run idx to the reporters, which is
the run that is intended to be continued. This simulation
manager knows no details and is left up to the reporters to
handle this appropriately.
Parameters
----------
run_time : float
The time to run in seconds.
segments_length : int
The number of steps for each runner segment.
num_workers : int
The number of workers to use for the work mapper.
(Default value = None)
Returns
-------
new_walkers : list of walkers
The resulting walkers of the cycle
sim_components : list
Deep copies of the runner, resampler, and boundary
conditions objects at the end of the cycle.
See Also
--------
wepy.orchestration.orchestrator.Orchestrator : for running simulations with
checkpointing, restarting, reporter localization, and configuration hotswapping
with command line interface.
"""
start_time = time.time()
self.init(num_workers=num_workers)
cycle_idx = 0
walkers = self.init_walkers
while time.time() - start_time < run_time:
logging.info("starting cycle {} at time {}".format(cycle_idx, time.time() - start_time))
walkers, filters = self.run_cycle(walkers, segments_length, cycle_idx)
logging.info("ending cycle {} at time {}".format(cycle_idx, time.time() - start_time))
cycle_idx += 1
self.cleanup()
return walkers, deepcopy(filters)
def run_simulation(self, n_cycles, segment_lengths, num_workers=None):
"""Run a simulation for an explicit number of cycles.
Parameters
----------
n_cycles : int
Number of cycles to perform.
segment_lengths : int
The number of steps for each runner segment.
num_workers : int
The number of workers to use for the work mapper.
(Default value = None)
Returns
-------
new_walkers : list of walkers
The resulting walkers of the cycle
sim_components : list
Deep copies of the runner, resampler, and boundary
conditions objects at the end of the cycle.
See Also
--------
wepy.orchestration.orchestrator.Orchestrator : for running simulations with
checkpointing, restarting, reporter localization, and configuration hotswapping
with command line interface.
"""
self.init(num_workers=num_workers)
walkers = self.init_walkers
# the main cycle loop
for cycle_idx in range(n_cycles):
walkers, filters = self.run_cycle(walkers, segment_lengths[cycle_idx], cycle_idx)
self.cleanup()
return walkers, deepcopy(filters)
def continue_run_simulation(self, run_idx, n_cycles, segment_lengths, num_workers=None):
"""Continue a simulation. All this does is provide a run idx to the
reporters, which is the run that is intended to be
continued. This simulation manager knows no details and is
left up to the reporters to handle this appropriately.
Parameters
----------
run_idx : int
Index of the run you are continuing.
n_cycles : int
Number of cycles to perform.
segment_lengths : int
The number of steps for each runner segment.
num_workers : int
The number of workers to use for the work mapper.
(Default value = None)
Returns
-------
new_walkers : list of walkers
The resulting walkers of the cycle
sim_components : list
Deep copies of the runner, resampler, and boundary
conditions objects at the end of the cycle.
See Also
--------
wepy.orchestration.orchestrator.Orchestrator : for running simulations with
checkpointing, restarting, reporter localization, and configuration hotswapping
with command line interface.
"""
self.init(num_workers=num_workers,
continue_run=run_idx)
walkers = self.init_walkers
# the main cycle loop
for cycle_idx in range(n_cycles):
walkers, filters = self.run_cycle(walkers, segment_lengths[cycle_idx], cycle_idx)
self.cleanup()
return walkers, filters
def continue_run_simulation_by_time(self, run_idx, run_time, segments_length, num_workers=None):
"""Continue a simulation with a separate run by time.
This starts timing as soon as this is called. If the time
before running a new cycle is greater than the runtime the run
will exit after cleaning up. Once a cycle is started it may
also run over the wall time.
All this does is provide a run idx to the reporters, which is
the run that is intended to be continued. This simulation
manager knows no details and is left up to the reporters to
handle this appropriately.
Parameters
----------
run_idx : int
Deep copies of the runner, resampler, and boundary
conditions objects at the end of the cycle.
See Also
--------
wepy.orchestration.orchestrator.Orchestrator : for running simulations with
checkpointing, restarting, reporter localization, and configuration hotswapping
with command line interface.
"""
start_time = time.time()
self.init(num_workers=num_workers,
continue_run=run_idx)
cycle_idx = 0
walkers = self.init_walkers
while time.time() - start_time < run_time:
logging.info("starting cycle {} at time {}".format(cycle_idx, time.time() - start_time))
walkers, filters = self.run_cycle(walkers, segments_length, cycle_idx)
logging.info("ending cycle {} at time {}".format(cycle_idx, time.time() - start_time))
cycle_idx += 1
self.cleanup()
return walkers, filters
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")
