"""
% This file is licensed under the Allen Institute Terms of Use, which can be found at:
%http://www.alleninstitute.org/Media/policies/terms_of_use_content.html
--------------------------------------------------------------------------

Script to generate NEST data for Supplement Fig.S3. 

NOTE: Data files originally generated and used for Fig. have been provided

Run as : 

$ python SuppFig_S3_LIFCond_generate_NEST_data.py <simulator>

where <simulator> is 'neuron', 'nest', brian, etc

"""

#import modules
from pyNN.utility import get_script_args, Timer
from pyNN.random import NumpyRNG, RandomDistribution
import numpy
from NeuroTools import stgen


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

timer = Timer()

# single neuron parameters
tauMem      = 20.0    # neuron membrane time constant [ms]
tauSyn      = 0.1     # synaptic time constant [ms]
tauRef      = 0.0     # refractory time [ms]
U0          = 0.0     # resting potential [mV]
theta       = 20.0    # threshold potential [mV]
e_rev_E = 100.0       #Reversal potential for excitatory input[mV]
e_rev_I = -10.0       #Reversal potential for inhibitory input[mV]
firstbinedge = 0.4822620085363334

#With these parameters, the resistance of the cell works out to 40 M-ohms

# simulation-related parameters  
simtime     = 205.0   # simulation time [ms] 
dt          = 0.1     # simulation step length [ms]   

# compute neuron numbers
NE = 10000#number of exc neurons

#Synaptic Weight and Rate#
JE = 0.5             #cond-based synapse's weight in [microS]
p_rate = 100         #firing rate in Hz

# put cell parameters into a dict
cell_params = {'tau_m'      : tauMem,
               'tau_syn_E'  : tauSyn,
               'tau_syn_I'  : tauSyn,
               'tau_refrac' : tauRef,
               'e_rev_E' : e_rev_E,
               'e_rev_I' : e_rev_I,
               'v_rest'     : U0,
               'v_reset'    : U0,
               'v_thresh'   : theta,
               'cm'         : 1.0}     # (nF)

# === Build the population ========================================================

rank = setup(timestep = dt, min_delay = 0.1, spike_precision = 'off_grid') 

timer.start() #start timer on construction

#Create a target population and initialize the potentials
E_net = Population(NE, IF_cond_exp, cell_params, label="E_net")
vinit_distr = RandomDistribution('uniform', [U0, firstbinedge])
E_net.initialize('v', vinit_distr)

#Create a source population
ex_spikes = Population(NE, SpikeSourcePoisson, {'rate' : p_rate}, "ex_spikes")

#Record spike-times and voltages of the target neuronal population
E_net.record()
E_net.record_v()

#Connect the source to the target using a user-defined connector
ext_Connector = OneToOneConnector(weights = JE, delays = None, verbose = True)
E_input = Projection(ex_spikes, E_net, ext_Connector, target = "excitatory")

# read out time used for building
buildCPUTime = timer.elapsedTime()

# === Run simulation ===========================================================

# run, measure computer time
timer.start() # start timer on construction
print "%d Running simulation for %g ms." % (rank, simtime)
run(simtime)
simCPUTime = timer.elapsedTime()

E_rate = E_net.meanSpikeCount()*1000.0/simtime

#Report run times
print("Build time         : %g s" % buildCPUTime)   
print("Simulation time    : %g s" % simCPUTime)
print("Excitatory rate    : %g Hz" % E_rate)
print("dt                 : %g ms" %dt)

# write data to file
E_net.printSpikes("IFCO_OGUniMoSprProb_5mV_100Hz_st_%s" % (simulator_name))
E_net.print_v("IFCO_OGUniMoSprProb_5mV_100Hz_v_%s" % (simulator_name))

# === Clean up and quit ========================================================
end()