network.t.record(neuron.h._ref_t)
else:
network.t = None
############################################################################
# run simulation, gather results across all RANKs
############################################################################
# Assert that connect routines has finished across RANKS before starting
# main simulation procedure
COMM.Barrier()
if RANK == 0:
print('running simulation....')
if PSET.COMPUTE_LFP:
SPIKES, SUMMED_OUTPUT, CURRENT_DIPOLE_MOMENT = network.simulate(
electrode=electrode,
rec_current_dipole_moment=PSET.COMPUTE_P,
rec_pop_contributions=PSET.rec_pop_contributions,
**PSET.NetworkSimulateArgs)
else:
SPIKES = network.simulate()
# note: OUTPUT is a structured array if
# PSET.COMPUTE_LFP = True and PSET.COMPUTE_P is False and
# PSET.rec_pop_contributions is False.
# If PSET.COMPUTE_P == True and/or PSET.rec_pop_contributions == True,
# then output is a tuple (OUTPUT, current_dipole_moment)
if RANK == 0:
run_simulation_time = time() - tic
print('Simulations finished in {} seconds'.format(run_simulation_time))
logfile.write('simulation {}\n'.format(run_simulation_time))
tic = time()
network.t = None
############################################################################
# run simulation, gather results across all RANKs
############################################################################
# Assert that connect routines has finished across RANKS before starting
# main simulation procedure
COMM.Barrier()
if RANK == 0:
print('running simulation....')
if PSET.COMPUTE_LFP:
SPIKES, SUMMED_OUTPUT, CURRENT_DIPOLE_MOMENT = network.simulate(
electrode=electrode,
rec_current_dipole_moment=PSET.COMPUTE_P,
rec_pop_contributions=PSET.rec_pop_contributions,
**PSET.NetworkSimulateArgs)
else:
SPIKES = network.simulate()
# note: OUTPUT is a structured array if
# PSET.COMPUTE_LFP = True and PSET.COMPUTE_P is False and
# PSET.rec_pop_contributions is False.
# If PSET.COMPUTE_P == True and/or PSET.rec_pop_contributions == True,
# then output is a tuple (OUTPUT, current_dipole_moment)
if RANK == 0:
run_simulation_time = time() - tic
print('Simulations finished in {} seconds'.format(run_simulation_time))
logfile.write('simulation {}\n'.format(run_simulation_time))
tic = time()
weightargs=weightArguments[i][j],
minweight=minweight,
delayfun=delayFunction,
delayargs=delayArguments[i][j],
mindelay=mindelay,
multapsefun=multapseFunction,
multapseargs=multapseArguments[i][j],
syn_pos_args=synapsePositionArguments[i][j],
)
# set up extracellular recording device:
electrode = RecExtElectrode(**electrodeParameters)
# run simulation:
SPIKES, OUTPUT, DIPOLEMOMENT = network.simulate(
electrode=electrode,
**networkSimulationArguments
)
# collect somatic potentials across all RANKs to RANK 0:
if RANK == 0:
somavs = []
for i, name in enumerate(population_names):
somavs.append([])
somavs[i] += [cell.somav
for cell in network.populations[name].cells]
for j in range(1, SIZE):
somavs[i] += COMM.recv(source=j, tag=15)
else:
somavs = None
for name in population_names:
COMM.send([cell.somav for cell in network.populations[name].cells],
if RANK == 0:
network.t = neuron.h.Vector()
network.t.record(neuron.h._ref_t)
else:
network.t = None
##########################################################################
# run simulation, gather results across all RANKs
##########################################################################
# Assert that connect routines has finished across RANKS before starting
# main simulation procedure
COMM.Barrier()
if RANK == 0:
print('running simulation....')
SPIKES = network.simulate(probes=probes,
rec_pop_contributions=PSET.rec_pop_contributions,
**PSET.NetworkSimulateArgs)
if RANK == 0:
run_simulation_time = time() - tic
print('Simulations finished in {} seconds'.format(run_simulation_time))
logfile.write('simulation {}\n'.format(run_simulation_time))
tic = time()
##########################################################################
# save simulated output to file to allow for offline plotting
##########################################################################
if RANK == 0:
f = h5py.File(
os.path.join(PSET.OUTPUTPATH,
multapseargs=multapseArguments[i][j],
syn_pos_args=synapsePositionArguments[i][j],
save_connections=False,
)
# set up extracellular recording device.
# Here `cell` is set to None as handles to cell geometry is handled
# internally
electrode = RecExtElectrode(cell=None, **electrodeParameters)
# set up recording of current dipole moments. Ditto with regards to
# `cell` being set to None
current_dipole_moment = CurrentDipoleMoment(cell=None)
# run simulation:
SPIKES = network.simulate(probes=[electrode, current_dipole_moment],
**networkSimulationArguments)
# collect somatic potentials across all RANKs to RANK 0:
if RANK == 0:
somavs = []
for i, name in enumerate(population_names):
somavs_pop = None # avoid undeclared variable
for j, cell in enumerate(network.populations[name].cells):
if j == 0:
somavs_pop = cell.somav
else:
somavs_pop = np.vstack((somavs_pop, cell.somav))
if RANK == 0:
for j in range(1, SIZE):
recv = COMM.recv(source=j, tag=15)
if somavs_pop is None:
delayfun=delayFunction,
delayargs=delayArguments[i][j],
mindelay=mindelay,
multapsefun=multapseFunction,
multapseargs=multapseArguments[i][j],
syn_pos_args=synapsePositionArguments[i][j],
save_connections=False, # Creates synapse_positions.h5
)
electrode = RecExtElectrode(**electrodeParameters)
EEG_electrode_params = dict(x=0, y=0, z=90000., method="soma_as_point")
EEG_electrode = RecExtElectrode(**EEG_electrode_params)
# run simulation:
SPIKES2, OUTPUT2, DIPOLEMOMENT2 = network.simulate(
electrode=electrode,
# electrode = EEG_electrode,
**networkSimulationArguments,
)
# collect somatic potentials across all RANKs to RANK 0:
if RANK == 0:
somavs = []
for i, name in enumerate(population_names):
somavs.append([])
somavs[i] += [
cell.somav for cell in network.populations[name].cells
]
for j in range(1, SIZE):
somavs[i] += COMM.recv(source=j, tag=15)
else:
somavs = None
weightargs=weightArguments[i][j],
minweight=minweight,
delayfun=delayFunction,
delayargs=delayArguments[i][j],
mindelay=mindelay,
multapsefun=multapseFunction,
multapseargs=multapseArguments[i][j],
syn_pos_args=synapsePositionArguments[i][j],
)
# set up extracellular recording device:
electrode = RecExtElectrode(**electrodeParameters)
# run simulation:
SPIKES, OUTPUT, DIPOLEMOMENT = network.simulate(
electrode=electrode,
**networkSimulationArguments
)
# collect somatic potentials across all RANKs to RANK 0:
if RANK == 0:
somavs = []
for i, name in enumerate(population_names):
somavs.append([])
somavs[i] += [cell.somav
for cell in network.populations[name].cells]
for j in range(1, SIZE):
somavs[i] += COMM.recv(source=j, tag=15)
else:
somavs = None
for name in population_names:
COMM.send([cell.somav for cell in network.populations[name].cells],