Merge branch 'line_search' of gitlab.mpcdf.mpg.de:ift/NIFTy into line_search

demos/critical_filtering.py

-from nifty import *
-from nifty.library.wiener_filter import WienerFilterEnergy
+import numpy as np
+from nifty import (VL_BFGS, DiagonalOperator, FFTOperator, Field,
+                   LinearOperator, PowerSpace, RelaxedNewton, RGSpace,
+                   SteepestDescent, create_power_operator, exp, log, sqrt)
 from nifty.library.critical_filter import CriticalPowerEnergy
-import plotly.offline as pl
-import plotly.graph_objs as go
+from nifty.library.wiener_filter import WienerFilterEnergy
 
+import plotly.graph_objs as go
+import plotly.offline as pl
 from mpi4py import MPI
+
 comm = MPI.COMM_WORLD
 rank = comm.rank
 
 np.random.seed(42)
 
 
-def plot_parameters(m,t,p, p_d):
+def plot_parameters(m, t, p, p_d):
 
     x = log(t.domain[0].kindex)
     m = fft.adjoint_times(m)
@@ -20,7 +24,8 @@ def plot_parameters(m,t,p, p_d):
     p = p.val.get_full_data().real
     p_d = p_d.val.get_full_data().real
     pl.plot([go.Heatmap(z=m)], filename='map.html')
-    pl.plot([go.Scatter(x=x,y=t), go.Scatter(x=x ,y=p), go.Scatter(x=x, y=p_d)], filename="t.html")
+    pl.plot([go.Scatter(x=x, y=t), go.Scatter(x=x, y=p),
+             go.Scatter(x=x, y=p_d)], filename="t.html")
 
 
 class AdjointFFTResponse(LinearOperator):
@@ -36,6 +41,7 @@ class AdjointFFTResponse(LinearOperator):
 
     def _adjoint_times(self, x, spaces=None):
         return self.FFT(self.R.adjoint_times(x))
+
     @property
     def domain(self):
         return self._domain
@@ -48,41 +54,40 @@ class AdjointFFTResponse(LinearOperator):
     def unitary(self):
         return False
 
+
 if __name__ == "__main__":
 
     distribution_strategy = 'not'
 
     # Set up position space
-    s_space = RGSpace([128,128])
+    s_space = RGSpace([128, 128])
     # s_space = HPSpace(32)
 
     # Define harmonic transformation and associated harmonic space
     fft = FFTOperator(s_space)
     h_space = fft.target[0]
 
-    # Setting up power space
+    # Set up power space
     p_space = PowerSpace(h_space, logarithmic=True,
                          distribution_strategy=distribution_strategy)
 
-    # Choosing the prior correlation structure and defining correlation operator
+    # Choose the prior correlation structure and defining correlation operator
     p_spec = (lambda k: (.5 / (k + 1) ** 3))
     S = create_power_operator(h_space, power_spectrum=p_spec,
                               distribution_strategy=distribution_strategy)
 
-    # Drawing a sample sh from the prior distribution in harmonic space
+    # Draw a sample sh from the prior distribution in harmonic space
     sp = Field(p_space,  val=p_spec,
                distribution_strategy=distribution_strategy)
     sh = sp.power_synthesize(real_signal=True)
 
-
-    # Choosing the measurement instrument
+    # Choose the measurement instrument
     # Instrument = SmoothingOperator(s_space, sigma=0.01)
     Instrument = DiagonalOperator(s_space, diagonal=1.)
     # Instrument._diagonal.val[200:400, 200:400] = 0
-    #Instrument._diagonal.val[64:512-64, 64:512-64] = 0
+    # Instrument._diagonal.val[64:512-64, 64:512-64] = 0
 
-
-    #Adding a harmonic transformation to the instrument
+    # Add a harmonic transformation to the instrument
     R = AdjointFFTResponse(fft, Instrument)
 
     noise = 1.
@@ -92,7 +97,7 @@ if __name__ == "__main__":
                           std=sqrt(noise),
                           mean=0)
 
-    # Creating the mock data
+    # Create mock data
     d = R(sh) + n
 
     # The information source
@@ -103,18 +108,15 @@ if __name__ == "__main__":
     if rank == 0:
         pl.plot([go.Heatmap(z=d_data)], filename='data.html')
 
-    #  minimization strategy
-
+    #  Minimization strategy
     def convergence_measure(a_energy, iteration):  # returns current energy
         x = a_energy.value
-        print (x, iteration)
-
+        print(x, iteration)
 
     minimizer1 = RelaxedNewton(convergence_tolerance=1e-8,
                                convergence_level=1,
                                iteration_limit=5,
                                callback=convergence_measure)
-
     minimizer2 = VL_BFGS(convergence_tolerance=1e-8,
                          convergence_level=1,
                          iteration_limit=1000,
@@ -124,35 +126,31 @@ if __name__ == "__main__":
                                  iteration_limit=500,
                                  callback=convergence_measure)
 
-    # Setting starting position
-    flat_power = Field(p_space,val=1e-8)
+    # Set starting position
+    flat_power = Field(p_space, val=1e-8)
     m0 = flat_power.power_synthesize(real_signal=True)
 
     t0 = Field(p_space, val=log(1./(1+p_space.kindex)**2))
 
-
-
-    for i in range(500):
+    for i in range(50):
         S0 = create_power_operator(h_space, power_spectrum=exp(t0),
                                    distribution_strategy=distribution_strategy)
 
-        # Initializing the  nonlinear Wiener Filter energy
+        # Initialize non-linear Wiener Filter energy
         map_energy = WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S0)
-        # Solving the Wiener Filter analytically
+        # Solve the Wiener Filter analytically
         D0 = map_energy.curvature
         m0 = D0.inverse_times(j)
-        # Initializing the power energy with updated parameters
-        power_energy = CriticalPowerEnergy(position=t0, m=m0, D=D0, smoothness_prior=10., samples=3)
+        # Initialize power energy with updated parameters
+        power_energy = CriticalPowerEnergy(position=t0, m=m0, D=D0,
+                                           smoothness_prior=10., samples=3)
 
         (power_energy, convergence) = minimizer2(power_energy)
 
-
-        # Setting new power spectrum
+        # Set new power spectrum
         t0.val = power_energy.position.val.real
 
-        # Plotting current estimate
-        print i
-        if i%50 == 0:
-            plot_parameters(m0,t0,log(sp), data_power)
-
-
+        # Plot current estimate
+        print(i)
+        if i % 5 == 0:
+            plot_parameters(m0, t0, log(sp), data_power)