def ACTIONS(self):
LOGGER.debug("%s starting", self.name)
if self._rate_encoder.last_update_time != get_current_time():
self._rate_encoder.update_rates(get_current_time())
if self.last_schedulable_time > self.corrected_time:
# At least one more event has to be scheduled.
next_period = self.corrected_time + self._rate_encoder.update_period
next_time = min(next_period, self.last_schedulable_time)
_schedule_output_rate_encoder(self._rate_encoder,
start_t=next_time,
end_t=self._end_t)
elif self._end_t == None or self._end_t > get_current_time():
# The rate encoder should go on encoding if the simulation is restarted.
# We use a special structure for those.
start = get_current_time() + self._rate_encoder.update_period
if self._end_t != None:
start = min(start, self._end_t)
RATE_ENC_RESPAWN_DICT[self._rate_encoder] = (start, self._end_t)
yield sim.hold, self, 0
def ACTIONS(self):
LOGGER.debug("%s starting", self.name)
if self._rate_encoder.last_update_time != get_current_time():
self._rate_encoder.update_rates(get_current_time())
if self.last_schedulable_time > self.corrected_time:
# At least one more event has to be scheduled.
next_period = self.corrected_time + self._rate_encoder.update_period
next_time = min(next_period, self.last_schedulable_time)
_schedule_output_rate_encoder(self._rate_encoder,
start_t=next_time,
end_t=self._end_t)
elif self._end_t == None or self._end_t > get_current_time():
# The rate encoder should go on encoding if the simulation is restarted.
# We use a special structure for those.
start = get_current_time() + self._rate_encoder.update_period
if self._end_t != None:
start = min(start, self._end_t)
RATE_ENC_RESPAWN_DICT[self._rate_encoder] = (start, self._end_t)
yield sim.hold, self, 0
def run_simulation(end_time=None):
"""Runs the simulation while keeping SimPy and PyNN synchronized at
event times. Runs until no event is scheduled unless end_time is
provided. if end_time is given, runs until end_time."""
global SIMULATION_END_T
def run_pynn(end_t):
pynn_now = pynnn.get_current_time()
pynn_now_round = round(pynn_now, PYNN_TIME_ROUNDING)
delta_t = round(end_t - pynn_now_round, PYNN_TIME_ROUNDING)
if pynn_now <= pynn_now_round and delta_t > PYNN_TIME_STEP:
delta_t = round(delta_t - PYNN_TIME_STEP, PYNN_TIME_ROUNDING)
if delta_t > 0: # necessary because run(0) may run PyNN by timestep
pynnn.run(delta_t) # neuralensemble.org/trac/PyNN/ticket/200
is_not_end = None
if end_time == None:
# Would testing len(sim.Globals.allEventTimes()) be faster?
is_not_end = lambda t: not isinstance(t, sim.Infinity)
else:
DummyProcess().start(at=end_time)
e_t = end_time
is_not_end = lambda t: t <= e_t
for e in RATE_ENC_RESPAWN_DICT.iteritems():
if e[1] == None:
continue
renc, start_time, end_time = e[0], e[1][0], e[1][1]
_schedule_output_rate_encoder(renc,
start_t=start_time,
end_t=end_time)
if SIMULATION_END_T < end_time:
SIMULATION_END_T = end_time
RATE_ENC_RESPAWN_DICT.clear() # unnecessary as _schedule_output_rate_encoder performs cleanup
t_event_start = sim.peek()
while is_not_end(t_event_start):
LOGGER.debug("Progressing to SimPy event at time %s",
t_event_start)
run_pynn(t_event_start) # run until event start
sim.step() # process the event
run_pynn(get_current_time()) # run PyNN until event end
t_event_start = sim.peek()
if SIMULATION_END_T < get_current_time():
SIMULATION_END_T = get_current_time()
def run_simulation(end_time=None):
"""Runs the simulation while keeping SimPy and PyNN synchronized at
event times. Runs until no event is scheduled unless end_time is
provided. if end_time is given, runs until end_time."""
global SIMULATION_END_T
def run_pynn(end_t):
pynn_now = pynnn.get_current_time()
pynn_now_round = round(pynn_now, PYNN_TIME_ROUNDING)
delta_t = round(end_t - pynn_now_round, PYNN_TIME_ROUNDING)
if pynn_now <= pynn_now_round and delta_t > PYNN_TIME_STEP:
delta_t = round(delta_t - PYNN_TIME_STEP, PYNN_TIME_ROUNDING)
if delta_t > 0: # necessary because run(0) may run PyNN by timestep
pynnn.run(delta_t) # neuralensemble.org/trac/PyNN/ticket/200
is_not_end = None
if end_time == None:
# Would testing len(sim.Globals.allEventTimes()) be faster?
is_not_end = lambda t: not isinstance(t, sim.Infinity)
else:
DummyProcess().start(at=end_time)
e_t = end_time
is_not_end = lambda t: t <= e_t
for e in RATE_ENC_RESPAWN_DICT.iteritems():
if e[1] == None:
continue
renc, start_time, end_time = e[0], e[1][0], e[1][1]
_schedule_output_rate_encoder(renc, start_t=start_time, end_t=end_time)
if SIMULATION_END_T < end_time:
SIMULATION_END_T = end_time
RATE_ENC_RESPAWN_DICT.clear(
) # unnecessary as _schedule_output_rate_encoder performs cleanup
t_event_start = sim.peek()
while is_not_end(t_event_start):
LOGGER.debug("Progressing to SimPy event at time %s", t_event_start)
run_pynn(t_event_start) # run until event start
sim.step() # process the event
run_pynn(get_current_time()) # run PyNN until event end
t_event_start = sim.peek()
if SIMULATION_END_T < get_current_time():
SIMULATION_END_T = get_current_time()
def main():
## Uninteresting setup, start up the visu process,...
logfile = make_logfile_name()
ensure_dir(logfile)
f_h = logging.FileHandler(logfile)
f_h.setLevel(SUBDEBUG)
d_h = logging.StreamHandler()
d_h.setLevel(INFO)
utils.configure_loggers(debug_handler=d_h, file_handler=f_h)
parent_conn, child_conn = multiprocessing.Pipe()
p = multiprocessing.Process(
target=visualisation.visualisation_process_f,
name="display_process", args=(child_conn, LOGGER))
p.start()
pynnn.setup(timestep=SIMU_TIMESTEP)
init_logging("logfile", debug=True)
LOGGER.info("Simulation started with command: %s", sys.argv)
## Network setup
# First population
p1 = pynnn.Population(100, pynnn.IF_curr_alpha,
structure=pynnn.space.Grid2D())
p1.set({'tau_m':20, 'v_rest':-65})
# Second population
p2 = pynnn.Population(20, pynnn.IF_curr_alpha,
cellparams={'tau_m': 15.0, 'cm': 0.9})
# Projection 1 -> 2
prj1_2 = pynnn.Projection(
p1, p2, pynnn.AllToAllConnector(allow_self_connections=False),
target='excitatory')
# I may need to make own PyNN Connector class. Otherwise, this is
# neat: exponentially decaying probability of connections depends
# on distance. Distance is only calculated using x and y, which
# are on a toroidal topo with boundaries at 0 and 500.
connector = pynnn.DistanceDependentProbabilityConnector(
"exp(-abs(d))",
space=pynnn.Space(
axes='xy', periodic_boundaries=((0,500), (0,500), None)))
# Alternately, the powerful connection set algebra (python CSA
# module) can be used.
weight_distr = pynnn.RandomDistribution(distribution='gamma',
parameters=[1,0.1])
prj1_2.randomizeWeights(weight_distr)
# This one is in NEST but not in Brian:
# source = pynnn.NoisyCurrentSource(
# mean=100, stdev=50, dt=SIMU_TIMESTEP,
# start=10.0, stop=SIMU_DURATION, rng=pynnn.NativeRNG(seed=100))
source = pynnn.DCSource(
start=10.0, stop=SIMU_DURATION, amplitude=100)
source.inject_into(list(p1.sample(50).all()))
p1.record(to_file=False)
p2.record(to_file=False)
## Build and send the visualizable network structure
adapter = pynn_to_visu.PynnToVisuAdapter(LOGGER)
adapter.add_pynn_population(p1)
adapter.add_pynn_population(p2)
adapter.add_pynn_projection(p1, p2, prj1_2.connection_manager)
adapter.commit_structure()
parent_conn.send(adapter.output_struct)
# Number of chunks to run the simulation:
n_chunks = SIMU_DURATION // SIMU_TO_VISU_MESSAGE_PERIOD
last_chunk_duration = SIMU_DURATION % SIMU_TO_VISU_MESSAGE_PERIOD
# Run the simulator
for visu_i in xrange(n_chunks):
pynnn.run(SIMU_TO_VISU_MESSAGE_PERIOD)
parent_conn.send(adapter.make_activity_update_message())
LOGGER.debug("real current p1 spike counts: %s",
p1.get_spike_counts().values())
if last_chunk_duration > 0:
pynnn.run(last_chunk_duration)
parent_conn.send(adapter.make_activity_update_message())
# Cleanup
pynnn.end()
# Wait for the visualisation process to terminate
p.join(VISU_PROCESS_JOIN_TIMEOUT)
def ACTIONS(self):
LOGGER.debug("%s starting", self.name)
self.input_layer.apply_input(self.input_sample, get_current_time(),
self.duration)
yield sim.hold, self, 0
def ACTIONS(self):
LOGGER.debug("%s starting", self.name)
self.input_layer.apply_input(self.input_sample, get_current_time(),
self.duration)
yield sim.hold, self, 0
def main():
## Uninteresting setup, start up the visu process,...
logfile = make_logfile_name()
ensure_dir(logfile)
f_h = logging.FileHandler(logfile)
f_h.setLevel(SUBDEBUG)
d_h = logging.StreamHandler()
d_h.setLevel(INFO)
utils.configure_loggers(debug_handler=d_h, file_handler=f_h)
parent_conn, child_conn = multiprocessing.Pipe()
p = multiprocessing.Process(target=visualisation.visualisation_process_f,
name="display_process",
args=(child_conn, LOGGER))
p.start()
pynnn.setup(timestep=SIMU_TIMESTEP)
init_logging("logfile", debug=True)
LOGGER.info("Simulation started with command: %s", sys.argv)
## Network setup
# First population
p1 = pynnn.Population(100,
pynnn.IF_curr_alpha,
structure=pynnn.space.Grid2D())
p1.set({'tau_m': 20, 'v_rest': -65})
# Second population
p2 = pynnn.Population(20,
pynnn.IF_curr_alpha,
cellparams={
'tau_m': 15.0,
'cm': 0.9
})
# Projection 1 -> 2
prj1_2 = pynnn.Projection(
p1,
p2,
pynnn.AllToAllConnector(allow_self_connections=False),
target='excitatory')
# I may need to make own PyNN Connector class. Otherwise, this is
# neat: exponentially decaying probability of connections depends
# on distance. Distance is only calculated using x and y, which
# are on a toroidal topo with boundaries at 0 and 500.
connector = pynnn.DistanceDependentProbabilityConnector(
"exp(-abs(d))",
space=pynnn.Space(axes='xy',
periodic_boundaries=((0, 500), (0, 500), None)))
# Alternately, the powerful connection set algebra (python CSA
# module) can be used.
weight_distr = pynnn.RandomDistribution(distribution='gamma',
parameters=[1, 0.1])
prj1_2.randomizeWeights(weight_distr)
# This one is in NEST but not in Brian:
# source = pynnn.NoisyCurrentSource(
# mean=100, stdev=50, dt=SIMU_TIMESTEP,
# start=10.0, stop=SIMU_DURATION, rng=pynnn.NativeRNG(seed=100))
source = pynnn.DCSource(start=10.0, stop=SIMU_DURATION, amplitude=100)
source.inject_into(list(p1.sample(50).all()))
p1.record(to_file=False)
p2.record(to_file=False)
## Build and send the visualizable network structure
adapter = pynn_to_visu.PynnToVisuAdapter(LOGGER)
adapter.add_pynn_population(p1)
adapter.add_pynn_population(p2)
adapter.add_pynn_projection(p1, p2, prj1_2.connection_manager)
adapter.commit_structure()
parent_conn.send(adapter.output_struct)
# Number of chunks to run the simulation:
n_chunks = SIMU_DURATION // SIMU_TO_VISU_MESSAGE_PERIOD
last_chunk_duration = SIMU_DURATION % SIMU_TO_VISU_MESSAGE_PERIOD
# Run the simulator
for visu_i in xrange(n_chunks):
pynnn.run(SIMU_TO_VISU_MESSAGE_PERIOD)
parent_conn.send(adapter.make_activity_update_message())
LOGGER.debug("real current p1 spike counts: %s",
p1.get_spike_counts().values())
if last_chunk_duration > 0:
pynnn.run(last_chunk_duration)
parent_conn.send(adapter.make_activity_update_message())
# Cleanup
pynnn.end()
# Wait for the visualisation process to terminate
p.join(VISU_PROCESS_JOIN_TIMEOUT)