def analyseSim(cRes):
v = cRes.v
#v = (v>0)*1.0
delta = SND.getDelta(v)
Omega = SND.getOmega(delta)
redHom = SND.getHomo(v)
OmegaM = Omega.mean()
redHomM = redHom.mean()
cRes.OmegaM = OmegaM
cRes.redHomM = redHomM
return cRes
def analyseSim(cRes):
v = cRes.imI
v =(v>0)*1.0 #Binarizing the data
#plt.imshow(v)
delta = SND.getDelta(v)
Omega = SND.getOmega(delta)
redHom = SND.getHomo(v)
OmegaM = Omega.mean()
redHomM = redHom.mean()
cRes.OmegaM = OmegaM
cRes.redHomM = redHomM
return cRes
#resDat = FHN_res('fhn_1d_perBnd_noisy_scriptable_nsGain_0.15_seed1_247seed2_321seed3_423.mat')
#v = resDat.v;
v = np.asanyarray(Image.open('testBild_Streifen_Noise.tif'),float)
v = (v>1)*1.0
# test
f1 = plt.figure()
ax = f1.add_axes([0, 0, 1, 1])
plt.imshow(v)
plt.title('Spatiotemporal')
plt.xlabel('t')
plt.ylabel('s')
f1.show()
delta = SND.getDelta(v)
Omega = SND.getOmega(delta)
redHom = SND.getHomo(v)
f2 = plt.figure()
plt.plot(Omega)
plt.title('Omega')
plt.xlabel('t')
plt.ylabel('Omega')
f2.show()
f3 = plt.figure()
plt.title('Reduced homogeneity')
plt.xlabel('t')
plt.ylabel('h')
