# coding: latin-1
"""
Benchmark adapted from a PyNN benchmark.
"""
from brian import *
import time

#set_global_preferences(useweave=False, usecodegen=False, usenewpropagate=False)

start_time = time.time()
taum = 20 * ms
taue = 5 * ms
taui = 10 * ms
Vt = -50 * mV
Vr = -60 * mV
El = -49 * mV

eqs = Equations('''
dv/dt  = (ge+gi-(v-El))/taum : volt
dge/dt = -ge/taue : volt
dgi/dt = -gi/taui : volt
''')

P = NeuronGroup(10000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms)
P.v = Vr
P.ge = 0 * mV
P.gi = 0 * mV

Pe = P.subgroup(8000)
Pi = P.subgroup(2000)
we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage)
wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight
Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.02,
                #delay=lambda:0.2*ms,max_delay=5*ms)
                delay=0.2*ms)
Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.02,
                #delay=lambda:0.2*ms,max_delay=5*ms)
                delay=0.2*ms)
P.v = Vr + rand(len(P)) * (Vt - Vr)

# Record the number of spikes
Me = PopulationSpikeCounter(Pe)
Mi = PopulationSpikeCounter(Pi)
# A population rate monitor
#M = PopulationRateMonitor(P)
M=StateMonitor(P,True)

print "Network construction time:", time.time() - start_time, "seconds"
print len(P), "neurons in the network"
print "Simulation running..."
start_time = time.time()
run(1 * msecond)
print "Preparation time:", time.time()-start_time
start_time = time.time()

run(2 * second)

duration = time.time() - start_time
print "Simulation time:", duration, "seconds"
print Me.nspikes, "excitatory spikes"
print Mi.nspikes, "inhibitory spikes"
#plot(M.times / ms, M.smooth_rate(2 * ms, 'gaussian'))
#show()