====MindControl.py====
from brian import *
import scipy.optimize
import matplotlib.pyplot as plt
import random
from pyevolve import G1DList
from pyevolve import GSimpleGA

'''
Hodgkin-Huxley model
Assuming area 1*cm**2
'''
from brian.library.ionic_currents import *

random.randrange(0,3000,1)

x = arange(0,2500)
for n in range (0,2500):
    x[n] = random.random()


def eval_func(chromosome):
    score = 3
    for value in chromosome:
        refract = 3
        tau = 10 * ms
        Vr = -70 * mV
        Vt = -55 * mV
        G = NeuronGroup(1, model='V:volt', threshold=Vt, reset=Vr, refractory=(refract*ms))
        input = SpikeGeneratorGroup(1, [(0, t * ms) for t in x])
        C = Connection(input, G)
        C[0, 0] = 2 * mV
        M = SpikeMonitor(G)
        run(3000 * ms)
        value1 = arange(0,(M.spiketimes[0].size)-1)
        if (M.spiketimes[0].size = 1)
            value1 = M.spiketimes[0][0]
        for n in range (0,(M.spiketimes[0].size)-2):
            value1[n] = M.spiketimes[0][n+1]-M.spiketimes[0][n]
            if value1[n] == 3
                score += 1
        return score


genome = G1DList.G1DList(20)
genome.evaluator.set(eval_func)
ga = GSimpleGA.GSimpleGA(genome)
ga.evolve(freq_stats=10)
print ga.bestIndividual()