"""
This is 3x3onA (described below ---) but now is done for the full
range of neurons.

--------------------------------------------------------
This is just a 1ms Izhikevich model that spikes every 10ms. from istanbul.
Now use that in the earlier (RetNetI.py) configuration, to get variable
behaviour.
In this we get 6 different behaviours.  It spikes fastest and most with
just the centre on.  It decreases with 1-5 surround on.  With 5 to 8 surround
on there is no firing.
This is a direct copy of RetNetJ.py, just to get the names right for the
other 5 sensors.
"""

from pyNN.utility import get_script_args, normalized_filename
#from neo.io import NeoHdf5IO
from neo.io import PyNNTextIO
#from neo import io

INPUT_NEURONS_HEIGHT = 50
INPUT_NEURONS_WIDTH = 50

#DELAY = 10.0
DELAY = 1.0

SIM_LENGTH = 200.0

#----------setup connections for input to 1 3x3 on centre off surround detector
def set3x3Connections(X,Y):
    excite = 25.0
    inhib = -2.0
    retinaNumber = INPUT_NEURONS_WIDTH*(X)+ Y
    inhib1 = (INPUT_NEURONS_WIDTH*(X-1))+Y-1 
    inhib2 = (INPUT_NEURONS_WIDTH*(X-1))+Y
    inhib3 = (INPUT_NEURONS_WIDTH*(X-1))+Y+1
    inhib4 = (INPUT_NEURONS_WIDTH*(X))+Y-1
    inhib6 = (INPUT_NEURONS_WIDTH*(X))+Y+1
    inhib7 = (INPUT_NEURONS_WIDTH*(X+1))+Y-1
    inhib8 = (INPUT_NEURONS_WIDTH*(X+1))+Y
    inhib9 = (INPUT_NEURONS_WIDTH*(X+1))+Y+1
    connector = [
        (retinaNumber, retinaNumber, excite, DELAY),
        (inhib1, retinaNumber, inhib, DELAY),
        (inhib2, retinaNumber, inhib, DELAY),
        (inhib3, retinaNumber, inhib, DELAY),
        (inhib4, retinaNumber, inhib, DELAY),
        (inhib6, retinaNumber, inhib, DELAY),
        (inhib7, retinaNumber, inhib, DELAY),
        (inhib8, retinaNumber, inhib, DELAY),
        (inhib9, retinaNumber, inhib, DELAY),
        ]
    return connector;


#------------Main Body---------------
simulator_name = get_script_args(1)[0]  
exec("from pyNN.%s import *" % simulator_name)

setup(timestep=DELAY,min_delay=DELAY,max_delay=DELAY,debug=0)

#input neurons
cInputNeurons = INPUT_NEURONS_HEIGHT * INPUT_NEURONS_WIDTH 

#default a .02, b .2, c -65, d 2.0, i_offset 0
inp_cell_params = {'a' : 0.0, 'b' :0.2, 'c' : -65, 'd' : 0.0}
inpPop = Population(cInputNeurons,Izhikevich(**inp_cell_params))

inpPop.initialize(v=-58.0)

#output neuron
retina_cell_params = {'a' : 0.2, 'b' :0.2, 'c' : -65, 'd' : 2.0}
retinaPop = Population(cInputNeurons,Izhikevich(**retina_cell_params))
    
#set up clamped input
pulse = DCSource(amplitude=0.0038, start=0.0, stop=SIM_LENGTH)
inputAssembly = Assembly(inpPop[51,52])
pulse.inject_into(inputAssembly)

synapseArray = []
#setup connections
for row in range (1,49):
    for col in range (1,49):
        newSynapses = set3x3Connections(col,row)
        synapseArray = synapseArray + newSynapses

#connectorA = set3x3Connections(1,1)
#print connectorA
#input_conns = Projection(inpPop, retinaPop, connectorA, StaticSynapse())
#connectorB = set3x3Connections(1,2)
#connectorA = connectorA+ connectorB
#print connectorA
#print synapseArray
input_conns = Projection(inpPop, retinaPop, FromListConnector(synapseArray), StaticSynapse())


#setup recording
inpPop.record(['spikes','v'])
retinaPop.record(['spikes','v'])

print inpPop[0].get_parameters()

run(SIM_LENGTH)

#--------------print results
outAss =  Assembly(retinaPop[51,52])
outDat = outAss.get_data()
for seg in outDat.segments:
    print(seg.spiketrains)

end()