some changes, nothing serious, not working

demos/critical_filtering.py

@@ -10,20 +10,21 @@ from mpi4py import MPI
 comm = MPI.COMM_WORLD
 rank = comm.rank
 
-np.random.seed(42)
+np.random.seed(43)
 
 
-def plot_parameters(m, t, p, p_d):
+def plot_parameters(m, t, p, p_sig,p_d):
 
     x = np.log(t.domain[0].kindex)
     m = fft.adjoint_times(m)
     m = m.val.get_full_data().real
     t = t.val.get_full_data().real
     p = p.val.get_full_data().real
+    pd_sig = p_sig.val.get_full_data()
     p_d = p_d.val.get_full_data().real
     pl.plot([go.Heatmap(z=m)], filename='map.html', auto_open=False)
     pl.plot([go.Scatter(x=x, y=t), go.Scatter(x=x, y=p),
-             go.Scatter(x=x, y=p_d)], filename="t.html", auto_open=False)
+             go.Scatter(x=x, y=p_d),go.Scatter(x=x, y=pd_sig)], filename="t.html", auto_open=False)
 
 
 class AdjointFFTResponse(ift.LinearOperator):
@@ -58,7 +59,8 @@ if __name__ == "__main__":
     distribution_strategy = 'not'
 
     # Set up position space
-    s_space = ift.RGSpace([128, 128])
+    dist = 1/128. *0.1
+    s_space = ift.RGSpace([128, 128], distances=[dist,dist])
     # s_space = ift.HPSpace(32)
 
     # Define harmonic transformation and associated harmonic space
@@ -72,7 +74,8 @@ if __name__ == "__main__":
                              distribution_strategy=distribution_strategy)
 
     # Choose the prior correlation structure and defining correlation operator
-    p_spec = (lambda k: (.5 / (k + 1) ** 3))
+    # p_spec = (lambda k: (.5 / (k + 1) ** 3))
+    p_spec = (lambda k: 1)
     S = ift.create_power_operator(h_space, power_spectrum=p_spec,
                                   distribution_strategy=distribution_strategy)
 
@@ -123,7 +126,6 @@ if __name__ == "__main__":
     IC3 = ift.GradientNormController(iteration_limit=100,
                                      tol_abs_gradnorm=0.1)
     minimizer3 = ift.SteepestDescent(IC3)
-
     # Set starting position
     flat_power = ift.Field(p_space, val=1e-8)
     m0 = flat_power.power_synthesize(real_signal=True)
@@ -137,11 +139,11 @@ if __name__ == "__main__":
         # Initialize non-linear Wiener Filter energy
         map_energy = WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S0)
         # Solve the Wiener Filter analytically
-        D0 = map_energy.curvature
-        m0 = D0.inverse_times(j)
+        # D0 = map_energy.curvature
+        # m0 = D0.inverse_times(j)
         # Initialize power energy with updated parameters
-        power_energy = CriticalPowerEnergy(position=t0, m=m0, D=D0,
-                                           smoothness_prior=10., samples=3)
+        power_energy = CriticalPowerEnergy(position=t0, m=sh, D=None,
+                                           smoothness_prior=1e-15, samples=3)
 
         (power_energy, convergence) = minimizer2(power_energy)
 
@@ -150,5 +152,6 @@ if __name__ == "__main__":
 
         # Plot current estimate
         print(i)
-        if i % 5 == 0:
-            plot_parameters(m0, t0, ift.log(sp), data_power)
+        if i % 1 == 0:
+            plot_parameters(sh, t0, ift.log(sp), ift.log(sh.power_analyze(binbounds=p_space.binbounds)),data_power)
+            print ift.log(sh.power_analyze(binbounds=p_space.binbounds)).val - t0.val

nifty/library/critical_filter/critical_power_energy.py

@@ -73,10 +73,11 @@ class CriticalPowerEnergy(Energy):
         self._w = w if w is not None else None
         if inverter is None:
             preconditioner = DiagonalOperator(self._theta.domain,
-                                              diagonal=self._theta.weight(-1),
+                                              diagonal=self._theta,
                                               copy=False)
             inverter = ConjugateGradient(preconditioner=preconditioner)
         self._inverter = inverter
+        self.one = Field(self.position.domain,val=1.)
 
     @property
     def inverter(self):
@@ -94,16 +95,16 @@ class CriticalPowerEnergy(Energy):
     @property
     @memo
     def value(self):
-        energy = self._theta.sum()
-        energy += self.position.weight(-1).vdot(self._rho_prime)
+        energy = self.one.vdot(self._theta)
+        energy += self.position.vdot(self.one/2.)
         energy += 0.5 * self.position.vdot(self._Tt)
         return energy.real
 
     @property
     @memo
     def gradient(self):
-        gradient = -self._theta.weight(-1)
-        gradient += (self._rho_prime).weight(-1)
+        gradient = -self._theta
+        gradient += (self.one/2.)
         gradient += self._Tt
         gradient.val = gradient.val.real
         return gradient
@@ -111,7 +112,7 @@ class CriticalPowerEnergy(Energy):
     @property
     @memo
     def curvature(self):
-        return CriticalPowerCurvature(theta=self._theta.weight(-1), T=self.T,
+        return CriticalPowerCurvature(theta=self._theta, T=self.T,
                                       inverter=self.inverter)
 
     # ---Added properties and methods---
@@ -142,7 +143,7 @@ class CriticalPowerEnergy(Energy):
                 w = self.m.power_analyze(
                      binbounds=self.position.domain[0].binbounds)
                 w *= self.rho
-            self._w = w
+            self._w = w.weight(-1)
         return self._w
 
     @property