def test_freq():
from nest import SetKernelStatus, Simulate, ResetKernel, raster_plot
ResetKernel()
SetKernelStatus({
'total_num_virtual_procs': 2,
'print_time': True,
'resolution': 0.1})
paf = ProkhazkaAfferentFiber()
sd = Create('spike_detector', params={'withgid': True, 'withtime': True})
mm = Create('')
print(sd)
Connect(paf.neurons, sd)
Simulate(1000.)
raster_plot.from_device(sd, hist=True)
raster_plot.show()
print("avg seconds spent per nest.Simulate()"\
" simulation " + str(avg))
##print("sum " + str(sum))
# get multimeter recordings
events = nest.GetStatus(mm)[0]["events"]
#t = events["times"]
### for plotting
if (myPl):
# spike raster plot
raster_plot.from_device(sd)
# multiply by 10 for actual length of time (1sim/10ms)
pylab.xlim(0, len(optLengths) * 10)
raster_plot.show()
# 1 plot
pylab.clf()
pylab.subplot(211)
pylab.plot(events["times"], events["primary_rate"])
pylab.plot(events["times"], events["secondary_rate"])
#pylab.axis([0, len(optLengths)*10, -200, 200])
# change y bounds z
pylab.xlabel("time (ms)")
pylab.ylabel("membrane potential (mV) (with electrode "\
"inaccuracy,interfence ? )")
pylab.legend(("primary_rate", "secondary_rate"))
#pylab.show() # shouldnt work on cluster
pylab.savefig(outputDir + "plots/pylabPlots/"\
+ names[myV]+currentMuscles[myC]\
nest.SetStatus(proxy_sensory, [{'port_name': 'sensory', 'music_channel': c} for c in range(100)])
nest.SetAcceptableLatency('sensory', 2.0)
print("Create devices")
sd_sensory = nest.Create("spike_detector", 1)
print("Connect")
# proxy to sd
nest.Connect(proxy_sensory, sd_sensory)
print("Simulate")
comm.Barrier()
nest.Simulate(10000)
print("Done")
rplt.from_device(sd_sensory)
rplt.show()
[e for e in nest.GetStatus([sd_actions[i]], keys="events")[0]["times"] if e > last_action_time]
) # calc the "firerate" of each actor population
if rate > max_rate:
max_rate = rate # the population with the hightes rate wins
chosen_action = i
possible_actions = env.actionsAvailable
FIRE_RATE_K = 0.5
new_position, outcome, in_end_position = env.move(possible_actions[chosen_action], max_rate * FIRE_RATE_K)
nest.SetStatus([sensors["right"]], {"rate": 0.0})
nest.SetStatus([sensors["left"]], {"rate": 0.0})
nest.SetStatus(noise, {"rate": 0.0})
for t in range(5):
nest.Simulate(4)
time.sleep(0.01)
last_action_time += 60
actions_executed += 1
else:
state = env.getState().copy()
nest.SetStatus([sensors["right"]], {"rate": 0.0})
nest.SetStatus([sensors["left"]], {"rate": 0.0})
nest.SetStatus(noise, {"rate": 0.0})
_, in_end_position = env.init_new_trial()
rplt.from_device(sd_all_actions, title="Actions")
rplt.from_device(sd_all, title="Difference neurons")
rplt.show()