from Stimulator import Constant_Stimulator as Current

from Stimulator import Current_Poisson_Stimulator as Noise

from Stimulator import Current_Poisson_Pool as Inhibitory_Pool

from LIF_STDP_Neuron import LIF_STDP_Neuron as Neuron

from LIF_STDP_Neuron import Event

import SimPy.SimulationTrace as simpy

import random

#import matplotlib.pyplot as plot

#import numpy

import os.path

ex_settings = {}

ex_settings['reset_potential'] = -70.0

ex_settings['spike_potential'] = 0

ex_settings['threshold'] = -54.0

ex_settings['refactory_period'] = 5.0

ex_settings['left_window_constant'] = 20#(t+)

ex_settings['right_window_constant'] = 20#(t-)

ex_settings['learning_rate'] = 0.05# (A+)

ex_settings['stability'] = 1.05# (B)

ex_settings['weight_ceiling'] = 1.0

ex_settings['type'] = 'current'

ex_settings['output_current_decay'] = 3.0

ex_settings['output_current_peak'] = 20.7

in_settings = {}

in_settings['reset_potential'] = -70.0

in_settings['spike_potential'] = 0

in_settings['threshold'] = -54.0

in_settings['refactory_period'] = 0.0

in_settings['left_window_constant'] = 20#(t+)

in_settings['right_window_constant'] = 20#(t-)

in_settings['learning_rate'] = 0.05# (A+)

in_settings['stability'] = 1.05# (B)

in_settings['weight_ceiling'] = 1.0

in_settings['type'] = 'current'

in_settings['output_current_decay'] = 3.0

in_settings['output_current_peak'] = -5.4

ds_settings = {}

ds_settings['reset_potential'] = -70.0

ds_settings['spike_potential'] = 0

ds_settings['threshold'] = -54.0

ds_settings['refactory_period'] = 0.0

ds_settings['left_window_constant'] = 20#(t+)

ds_settings['right_window_constant'] = 20#(t-)

ds_settings['learning_rate'] = 0.05# (A+)

ds_settings['stability'] = 1.05# (B)

ds_settings['weight_ceiling'] = 1.0

ds_settings['type'] = 'voltage'

inhi = 'on'

if os.path.isfile('no_inhi.tmp'):

inhi = 'off'

noise_intensy = 8.2

all_neuron = []

excitatory_a = []

excitatory_b = []

observer_a = []

observer_b = []

inhibitory = []

noise = []

downstream = []

source_a = Current('source', 0, 'current', 20.7)

source_b = Current('source', 1, 'current', 21.5)

for i in range(99):

neuron_producing = Neuron('excitatory', i, ex_settings, 'off')

noise_pos = Noise('noise', i, 100, noise_intensy, 3.0)

noise_neg = Noise('noise', i, 100, -noise_intensy, 3.0)

noise.append(noise_pos)

noise.append(noise_neg)

noise_pos.connect(neuron_producing)

noise_neg.connect(neuron_producing)

if random.random() < 0.5:

excitatory_a.append(neuron_producing)

source_a.connect(neuron_producing)

else:

excitatory_b.append(neuron_producing)

source_b.connect(neuron_producing)

if random.random() < 0.5:

observer_a.append(neuron_producing)

else:

observer_b.append(neuron_producing)

#for i in range(99):

#neuron_producing = Neuron('downstream', i, ds_settings, 'off')

#downstream.append(neuron_producing)

#for observee in random.sample(excitatory_a+excitatory_b, 20):

#observee.connect(neuron_producing)

for i in range(801):

neuron_producing = Neuron('inhibitory', i, in_settings, 'off')

if inhi == 'on':

inhibitory.append(neuron_producing)

for inhibitee in random.sample(excitatory_a+excitatory_b, 20):

inhibitee.connect(neuron_producing)

neuron_producing.connect(inhibitee)

all_neuron = excitatory_a + excitatory_b + inhibitory + downstream + noise

duration = 2400

for i in range(duration):

for neuron in all_neuron:

event = Event(name = 'update')

simpy.activate(event, event.update(neuron), delay = i)

print("simulation scheduled.")

simpy.simulate(until = duration+0.0)

print("simulation done.")

#ex_spikes_number = 0.0

#for i in excitatory_a+excitatory_b:

#ex_spikes_number += i.spikes_number

#ds_spikes_number = 0.0

#for i in downstream:

#ds_spikes_number += i.spikes_number

#print ds_spikes_number/ex_spikes_number

is_continue = os.path.isfile('continue.tmp')

file_op = 'w'

if is_continue:

file_op = 'a'

for i in range(99):

outfile = open('spikes_record/'+str(i)+'_inhib_'+inhi+'.txt', file_op)

for j in (excitatory_a+excitatory_b)[i].spikes_record:

outfile.write(str(j)+'\n')

outfile.close()

continue_file = open('continue.tmp', 'w')

continue_file.write('!')

continue_file.close()

#for i in excitatory_a+excitatory_b+inhibitory:

#continue_file.write(str(i)+'\n')

#continue_file.close()

exit()

x = list(range(len(excitatory_a[1].value_record)))

valen = len(inhibitory[1].value_record)

va = [0.0] * valen

for inh in (excitatory_a+excitatory_b)[1].dendrites.keys():

if inh in inhibitory:

for i in range(valen):

va[i] += inh.value_record[i]

plot.plot(x, va)

#plot.plot(x, excitatory_a[1].value_record)

plot.plot(x, excitatory_a[1].spikes_record, '.-')

plot.plot(x, inhibitory[1].spikes_record, '+')

plot.show()