def plot_rasters(K, I_app, Phi):
T = np.arange(tTransition, tFinal, 0.05)
nk = len(K)
for cn in networks:
for p in Phi:
for ip in I_app:
for k in range(nk):
fig, ax = pl.subplots(1, figsize=(10, 5))
ifname = str('%s-%.6f-%.6f-%.6f' % (cn, K[k], p, ip))
spikes = lib.read_from_file('text/spk-' + ifname + '.txt',
N)
for ii in range(N):
ax.plot(spikes[ii], [ii] * len(spikes[ii]),
'.',
c='royalblue',
markersize=2)
ax.set_ylabel("Node index")
ax.set_xlabel('Time(ms)')
ax.set_xlim(tTransition, tFinal)
ax.set_xlim(2500, 2900)
ax.set_title(cn + str(', K = %g, phi = %g, I = %g' %
(K[k], p, ip)))
fig.savefig('fig/r-' + ifname + '.png')
pl.close()
def plot_raster(e_file, i_file, ax):
t_e_spikes = read_from_file(e_file)
t_i_spikes = read_from_file(i_file)
for i in range(num_i):
if len(t_i_spikes[i]) > 0:
ax.plot(t_i_spikes[i], [i] * len(t_i_spikes[i]), "b.")
for i in range(num_i, num_i + num_e):
if len(t_e_spikes) > 0:
ax.plot(t_e_spikes[i - num_i], [i] * len(t_e_spikes[i - num_i]),
"r.")
ax.set_xlabel("time [ms]")
ax.set_ylabel("neuron #")
for i in range(num_i):
frequency_of_i_cell = len(t_i_spikes[i]) * 1000 / t_final
print("frequency of i cell {}: {}".format(i, frequency_of_i_cell))
for i in range(num_e):
frequency_of_e_cell = len(t_e_spikes[i]) * 1000 / t_final
print("frequency of e cell {}: {}".format(i, frequency_of_e_cell))
def plot_correlations(g, omega, ns=num_sim):
n = len(omega)
communities_l1 = lib.read_from_file(
str('../src/networks/communities_%d_l1.txt' % N))
for g in G:
for i in range(n):
cor = np.zeros((N, N))
for ens in range(ns):
subname = str("%.6f-%.6f" % (g, omega[i]))
c = np.fromfile("text/c-"+subname+"-"+str(ens)+".bin",
dtype=float, count=-1)
c = np.reshape(c, (N, N))
cor += c
cor /= float(ns)
cor_ordered = lib.reorder_nodes(cor, communities_l1)
np.savez("npz/cor-ave-"+subname, cor=cor_ordered)
lib.imshow_plot(cor_ordered,
fname="fig/c-"+subname+'.png',
vmax=1, vmin=-1)
def plot_nmi(adj, G, Omega, threshold=0.95, to_npz=False):
n = len(Omega)
N = adj.shape[0]
communities_l1 = lib.read_from_file(
str('../src/networks/communities_%d_l1.txt' % N))
communities_l2 = lib.nodes_of_each_cluster([32, 33])
adj = lib.reorder_nodes(adj, communities_l1)
adj_communities1 = lib.comm_weighted(adj)
adj_membership1 = adj_communities1.membership
adj_membership2 = [0]*32+[1]*33
fig, ax = pl.subplots(1, figsize=(6, 5))
for g in G:
nmi1 = np.zeros((num_sim, n))
nmi2 = np.zeros((num_sim, n))
for i in range(n):
for ens in range(num_sim):
subname = str("%.6f-%.6f" % (g, Omega[i]))
c = np.fromfile("text/c-"+subname+"-"+str(ens)+".bin",
dtype=float, count=-1)
c = np.reshape(c, (N, N))
c = lib.binarize(c, threshold)
cor_communities1 = lib.walktrap( # comm_unweighted
lib.reorder_nodes(c, communities_l1), steps=8)
cor_communities2 = lib.walktrap(
lib.reorder_nodes(c, communities_l2), steps=60)
# nmi[ens, i] = lib.calculate_NMI(adj_communities,
# cor_communities)
nmi1[ens, i] = lib.calculate_NMI(
adj_membership1,
cor_communities1.membership)
nmi2[ens, i] = lib.calculate_NMI(
adj_membership2,
cor_communities2.membership)
if num_sim > 1:
nmi_mean1 = np.mean(nmi1, axis=0)
nmi_std1 = np.std(nmi1, axis=0)
nmi_mean2 = np.mean(nmi2, axis=0)
nmi_std2 = np.std(nmi2, axis=0)
else:
nmi_mean1 = nmi1[0, :]
nmi_std1 = 0
nmi_mean2 = nmi2[0, :]
nmi_std2 = 0
if to_npz:
np.savez("npz/nmi-"+str('%.6f' % g),
nmi=[nmi_mean1, nmi_mean2],
std=[nmi_std1, nmi_std2],
omega=Omega,
g=g)
if num_sim > 1:
ax.errorbar(Omega, nmi_mean1, yerr=nmi_std1, lw=2,
marker='o', label=str("%.2f" % g))
ax.errorbar(Omega, nmi_mean2, yerr=nmi_std2, lw=2,
marker='o', label=str("%.2f" % g))
else:
ax.plot(Omega, nmi_mean1, lw=2, label=str("%.2f" % g))
ax.plot(Omega, nmi_mean2, lw=2, label=str("%.2f" % g))
ax.legend()
ax.set_xlabel(r"$\omega$", fontsize=14)
ax.set_ylabel(r"NMI")
pl.tight_layout()
pl.savefig("fig/nmiq.png", dpi=300)
pl.close()
right=right - 0.028,
wspace=0.4,
hspace=0.02,
top=0.37,
bottom=0.2)
ax6 = pl.subplot(gs3[0])
ax7 = pl.subplot(gs3[1])
ax8 = pl.subplot(gs3[2])
gs4 = GridSpec(1, 3)
gs4.update(left=left, right=right - 0.028, wspace=0.4, top=0.175, bottom=0.05)
ax9 = pl.subplot(gs4[0])
ax10 = pl.subplot(gs4[1])
ax11 = pl.subplot(gs4[2])
communities = lib.read_from_file('communities.txt', 5)
con = np.loadtxt("r_C65.dat")
length = np.loadtxt("r_L65.dat")
c = np.load("R.npz")
Rg = c['Rg'][0]
Rl1 = c['Rl1'][0]
Rl2 = c['Rl2'][0]
g = c['g']
mu = c['mu']
cor1 = np.load("cor-0.06.npz")['cor']
cor2 = np.load("cor-0.29.npz")['cor']
cor3 = np.load("cor-0.63.npz")['cor']
c1 = np.load('nmi-30.npz')
import numpy as np
import pylab as pl
from lib import read_from_file
from main import num_e, num_i
t_e_spikes = read_from_file("t_e_spikes.txt")
t_i_spikes = read_from_file("t_i_spikes.txt")
lfp = np.loadtxt("lfp.txt", dtype=float)
fig, ax = pl.subplots(2, figsize=(10, 8), sharex=True)
for i in range(num_i):
ax[0].plot(t_i_spikes[i], [i] * len(t_i_spikes[i]), "b.")
for i in range(num_i, num_i + num_e):
ax[0].plot(t_e_spikes[i - num_i], [i] *
len(t_e_spikes[i - num_i]), "r.")
t = lfp[:, 0]
lfp = lfp[:, 1]
ax[1].plot(t, lfp, lw=2, color="k")
ax[1].set_xlabel("time [ms]", fontsize=18)
ax[0].set_ylabel("neuron #", fontsize=18)
ax[1].set_ylabel("mean(v), E-cells", fontsize=18)
for i in range(2):
ax[i].tick_params(labelsize=14)
ax[1].set_ylim(-100, 50)
ax[1].set_xlim(0, np.max(t))
pl.tight_layout()