def plotcur(logim_step):
global nit
nit += 1
#print "iteration %d" % nit
#refresh_interval = 100
if nit%10 == 0:
print "iteration %d" % nit
Frames = np.exp(logim_step.reshape((-1, N_pixel, N_pixel)))
chi2 = np.sum(mx.chisq(Frames[i].ravel(), A_List[i], vis_List[i], sigma_List[i]) for i in range(N_frame))/N_frame
s = np.sum(-mx.ssimple(Frames[i].ravel(), nprior_List[i]) for i in range(N_frame))
s_dynamic = dynamic_regularizer(Frames, gauss)
print "chi2: %f" % chi2
print "s: %f" % s
print "s_dynamic: %f" % s_dynamic
#plot_i(Frames[0], Prior_List[0], nit, chi2, ipynb=ipynb)
if nit%refresh_interval == 0:
plot_i_dynamic(Frames, Prior_List[0], nit, chi2, s, s_dynamic, ipynb=ipynb)
if 1==0:
numeric_gradient = objgrad_numeric(logim_step)
analytic_gradient = objgrad(logim_step)
print "Numeric Gradient:"
print numeric_gradient
print "Analytic Gradient:"
print analytic_gradient
print "Max Fractional Difference in gradients:"
print max(abs((numeric_gradient - analytic_gradient)/numeric_gradient))
def objfunc(logim):
Frames = np.exp(logim.reshape((-1, N_pixel, N_pixel)))
if entropy == "simple":
s = np.sum(-mx.ssimple(Frames[i].ravel(), nprior_List[i]) for i in range(N_frame))
elif entropy == "l1":
s = np.sum(-mx.sl1(Frames[i].ravel(), nprior_List[i]) for i in range(N_frame))
elif entropy == "gs":
s = np.sum(-mx.sgs(Frames[i].ravel(), nprior_List[i]) for i in range(N_frame))
elif entropy == "tv":
s = np.sum(-mx.stv(Frames[i].ravel(), Prior_List[0].xdim, Prior_List[0].ydim) for i in range(N_frame))
chisq_total = np.sum(mx.chisq(Frames[i].ravel(), A_List[i], vis_List[i], sigma_List[i]) for i in range(N_frame))/N_frame
#print ("chisq_total ",chisq_total)
s_dynamic = dynamic_regularizer(Frames, gauss)
#print ("s_dynamic ",s_dynamic)
#return s_dynamic
return s + beta * s_dynamic + alpha * (chisq_total - 1.)
