def init_network():
"""
"""
np.seterr(all='raise')
context.env = Env(comm=context.comm, results_file_id=context.results_file_id, **context.kwargs)
network.init(context.env)
def main(arena_id, cell_selection_path, config_file, template_paths, hoc_lib_path, dataset_prefix, config_prefix,
results_path, results_id, node_rank_file, io_size, recording_fraction, recording_profile, coredat, trajectory_id, tstop, v_init,
stimulus_onset, max_walltime_hours, checkpoint_clear_data, checkpoint_interval, results_write_time,
spike_input_path, spike_input_namespace, spike_input_attr, dt, ldbal, lptbal, cleanup, profile_memory, write_selection,
verbose, debug, dry_run):
profile_time = False
comm = MPI.COMM_WORLD
np.seterr(all='raise')
params = dict(locals())
env = Env(**params)
if profile_time:
from dentate.network import init, run
import cProfile
cProfile.runctx('init(env)', None, locals(), filename='dentate_profile_init')
if not dry_run:
cProfile.runctx('run(env)', None, locals(), filename='dentate_profile_run')
else:
network.init(env)
if not dry_run:
network.run(env)
def init_network():
"""
"""
np.seterr(all='raise')
context.comm.barrier()
context.env = Env(comm=context.comm,
results_file_id=context.results_file_id,
**context.kwargs)
network.init(context.env)
if context.debug:
raise RuntimeError('config_worker: after network.init')
context.comm.barrier()
def main(config_file, template_paths, hoc_lib_path, dataset_prefix,
config_prefix, results_path, results_id, input_path, input_namespace,
target_cell, tstop, v_init, stimulus_onset, max_walltime_hours,
results_write_time, dt, ldbal, lptbal, verbose, dry_run):
"""
:param config_file: str; model configuration file name
:param template_paths: str; colon-separated list of paths to directories containing hoc cell templates
:param hoc_lib_path: str; path to directory containing required hoc libraries
:param dataset_prefix: str; path to directory containing required neuroh5 data files
:param config_prefix: str; path to directory containing network and cell mechanism config files
:param results_path: str; path to directory to export output files
:param results_id: str; label for neuroh5 namespaces to write spike and voltage trace data
:param tstop: int; physical time to simulate (ms)
:param v_init: float; initialization membrane potential (mV)
:param stimulus_onset: float; starting time of stimulus (ms)
:param max_walltime_hours: float; maximum wall time (hours)
:param results_write_time: float; time to write out results at end of simulation
:param dt: float; simulation time step
:param verbose: bool; print verbose diagnostic messages while constructing the network
:param dry_run: bool; whether to actually execute simulation after building network
"""
comm = MPI.COMM_WORLD
np.seterr(all='raise')
vrecord_fraction = 1.0
env = Env(comm,
config_file,
template_paths,
hoc_lib_path,
dataset_prefix,
config_prefix,
results_path,
results_id,
vrecord_fraction,
target_cell,
tstop,
v_init,
stimulus_onset,
max_walltime_hours,
results_write_time,
dt,
cell_selection=target_cell,
spike_input_path=input_path,
spike_input_ns=input_namespace,
verbose=verbose)
network.init(env)
if not dry_run:
network.run(env)
def main(cell_selection_path, config_file, template_paths, hoc_lib_path,
dataset_prefix, config_prefix, results_path, results_id,
node_rank_file, io_size, vrecord_fraction, coredat, tstop, v_init,
stimulus_onset, max_walltime_hours, results_write_time,
spike_input_path, spike_input_namespace, dt, ldbal, lptbal, cleanup,
verbose, run_test):
"""
:param cell_selection_path: str; name of file specifying subset of cells gids to be instantiated
:param config_file: str; model configuration file name
:param template_paths: str; colon-separated list of paths to directories containing hoc cell templates
:param hoc_lib_path: str; path to directory containing required hoc libraries
:param dataset_prefix: str; path to directory containing required neuroh5 data files
:param config_prefix: str; path to directory containing network and cell mechanism config files
:param results_path: str; path to directory to export output files
:param results_id: str; label for neuroh5 namespaces to write spike and voltage trace data
:param node_rank_file: str; name of file specifying assignment of node gids to MPI ranks
:param io_size: int; the number of MPI ranks to be used for I/O operations
:param vrecord_fraction: float; fraction of cells to record intracellular voltage from
:param coredat: bool; Save CoreNEURON data
:param tstop: int; physical time to simulate (ms)
:param v_init: float; initialization membrane potential (mV)
:param stimulus_onset: float; starting time of stimulus (ms)
:param max_walltime_hours: float; maximum wall time (hours)
:param results_write_time: float; time to write out results at end of simulation
:param spike_input_path: str; path to file for input spikes when cell selection is specified
:param spike_input_namespace: str;
:param dt: float; simulation time step
:param ldbal: bool; estimate load balance based on cell complexity
:param lptbal: bool; calculate load balance with LPT algorithm
:param cleanup: bool; whether to delete from memory the synapse attributes metadata after specifying connections
:param verbose: bool; print verbose diagnostic messages while constructing the network
:param run_test: bool; whether to actually execute simulation after building network
"""
comm = MPI.COMM_WORLD
np.seterr(all='raise')
params = dict(locals())
env = Env(**params)
network.init(env)
if run_test:
network.run(env, output=False)
def init_network(comm, kwargs):
np.seterr(all='raise')
env = Env(comm=comm, **kwargs)
network.init(env)
return env