evolutionary changes

demos/critical_filtering.py

@@ -52,7 +52,7 @@ if __name__ == "__main__":
     h_space = fft.target[0]
 
     # Set up power space
-    p_space = ift.PowerSpace(h_space)
+    p_space = ift.PowerSpace(h_space,binbounds=ift.PowerSpace.useful_binbounds(h_space,logarithmic=True))
 
     # Choose the prior correlation structure and defining correlation operator
     p_spec = (lambda k: (.5 / (k + 1) ** 3))
@@ -88,11 +88,11 @@ if __name__ == "__main__":
     d_data = d.val.real
     ift.plotting.plot(d.real, name="data.pdf")
 
-    IC1 = ift.DefaultIterationController(verbose=True,iteration_limit=100)
+    IC1 = ift.DefaultIterationController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
     minimizer1 = ift.RelaxedNewton(IC1)
-    IC2 = ift.DefaultIterationController(verbose=True,iteration_limit=100)
+    IC2 = ift.DefaultIterationController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
     minimizer2 = ift.VL_BFGS(IC2, max_history_length=20)
-    IC3 = ift.DefaultIterationController(verbose=True,iteration_limit=100)
+    IC3 = ift.DefaultIterationController(verbose=True,iteration_limit=100,tol_abs_gradnorm=0.1)
     minimizer3 = ift.SteepestDescent(IC3)
 
     # Set starting position
@@ -107,22 +107,22 @@ if __name__ == "__main__":
         S0 = ift.create_power_operator(h_space, power_spectrum=ps0)
 
         # Initialize non-linear Wiener Filter energy
-        ICI = ift.DefaultIterationController(verbose=False,iteration_limit=60)
+        ICI = ift.DefaultIterationController(verbose=False,iteration_limit=500,tol_abs_gradnorm=0.1)
         map_inverter = ift.ConjugateGradient(controller=ICI)
         map_energy = ift.library.WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S0, inverter=map_inverter)
         # Solve the Wiener Filter analytically
         D0 = map_energy.curvature
         m0 = D0.inverse_times(j)
         # Initialize power energy with updated parameters
-        ICI2 = ift.DefaultIterationController(verbose=False,iteration_limit=60)
+        ICI2 = ift.DefaultIterationController(name="powI",verbose=True,iteration_limit=200,tol_abs_gradnorm=1e-5)
         power_inverter = ift.ConjugateGradient(controller=ICI2)
         power_energy = ift.library.CriticalPowerEnergy(position=t0, m=m0, D=D0,
                                            smoothness_prior=10., samples=3, inverter=power_inverter)
 
-        (power_energy, convergence) = minimizer3(power_energy)
+        (power_energy, convergence) = minimizer1(power_energy)
 
         # Set new power spectrum
-        t0.val = power_energy.position.val.real
+        t0 = power_energy.position.real
 
         # Plot current estimate
         print(i)

nifty2go/library/critical_filter/critical_power_energy.py

@@ -90,7 +90,7 @@ class CriticalPowerEnergy(Energy):
         gradient = -self._theta.weight(-1)
         gradient += (self._rho_prime).weight(-1)
         gradient += self._Tt
-        gradient.val = gradient.val.real
+        gradient = gradient.real
         return gradient
 
     @property