T = 15
plt.ion()
plt.figure(1)
# for i in range(N):
# ax = plt.subplot2grid((N,2),(i,0))
# plt.imshow(np.random.randn(D,T),interpolation='none',aspect=T/D,cmap='Greys_r')
# pltuls.strip_ticks(ax)
#
# plt.text(0.3,0.95,'True W',transform=plt.gcf().transFigure)
#
ims = []
for i in range(N):
ax = plt.subplot2grid((N, 2), (i, 1))
im = ax.imshow(np.random.randn(D, T),
interpolation='none',
aspect=T / D,
cmap='Greys_r')
ims.append(im)
pltuls.strip_ticks(ax)
# plt.show()
for j in range(100):
for i in range(N):
im = ims[i]
test_im = np.random.randn(D, T)
im.set_array(test_im)
plt.draw()
plt.pause(0.1)
SS_tot = np.sum(np.square(M_nsy-np.mean(M_nsy)))
oneminusrsq = SS_res/SS_tot
print("1 - R^2: "+str(oneminusrsq))
#Reassign 1 - R^2
oneminusrsq = 5.
plt.ion()
#Plot the true W
plt.figure(1)
colormap = matplotlib.cm.get_cmap('Reds')
for i in range(N):
ax = plt.subplot2grid((N,3),(i,0))
plt.imshow(util.normalize(W[:,i].T[:,None]),interpolation='none',
aspect=3./D,cmap=colormap,vmin=0,vmax=1)
pltuls.strip_bare(ax,axis='x')
pltuls.strip_ticks(ax,axis='y')
plt.text(0.25,0.95,'True W',transform=plt.gcf().transFigure)
#Initialize synergy estimators
synergy_estimator = synergy_estimators.SynchronousSynergyEstimator(
D,T,N,M_nsy)
#Plot the W_ests
ims = []
plt.figure(1)
for i in range(N):
ax = plt.subplot2grid((N,3),(i,2))
im = plt.imshow(synergy_estimator.W_est[:,i].T[
:,None],interpolation='none',aspect=3./D,
cmap=colormap,vmin=0,vmax=1)
pltuls.strip_bare(ax,axis='x')