import matplotlib # these lines to
matplotlib.use('Agg') # work remotely
import numpy as np
import pylab as pl
from sys import path
path.append('../')
from scipy.sparse import csr_matrix
from NetPop import NetPop
import cfunctions as cfn
from functions import simpleaxis, errorfill, init_fig
init_fig()
net = NetPop(1)
W = np.copy(net.W)
dt = 1
R0 = net.R4flatPi(net.pstart_state)
Rmax = np.dot(net.calc_Qvalue().max(axis=1), net.pstart_state)
ref = 20
step = .2
rate = 400
## offline ##
try:
perf = np.load('results/performance.npy')
except IOError:
try:
S = np.load('results/spikes.npz')['S']
except IOError:
import numpy as np
import pylab as pl
from sys import argv, path
path.append('../')
from NetPop import NetPop, NetPopPredict
from functions import simpleaxis, smooth_spikes, accumulate, init_fig
savefig = False if len(argv) == 1 else True
init_fig()
pl.rc('legend', **{'fontsize': 30})
# colors for colorblind from http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
colRT = ["#009E73", "#0072B2", "#D55E00", "#E69F00",
"#56B4E9", "#F0E442", "#CC79A7", "#999999"]
# orange to green to cyan:
col = map(tuple, [np.array([.902, .624, 0]) +
(np.array([0, .62, .451]) -
np.array([.902, .624, 0])) * i / 4 for i in range(5)]) +\
map(tuple, [np.array([0, .62, .451]) +
(np.array([.337, .706, .914]) -
np.array([0, .62, .451])) * i / 3 for i in range(1, 4)])
def runpopU(W, uinit, step, pop_size, rate=100,
T=700, tm=50, ts=2, reset=3, delay=0):
K = len(W)
spikes = np.zeros((T / step, K))
spikes[0, K - pop_size:] = 1
fs = np.exp(-step / ts) # decay factor synapse
fm = np.exp(-step / tm) # decay factor membrane
# combine factor to save computing time