Merge branch 'tweak_demos' into 'master'

cosmetics and adjustments

See merge request !182

demos/critical_filtering.py

@@ -23,9 +23,9 @@ def plot_parameters(m, t, p, p_d):
     t = t.val.get_full_data().real
     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.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")
+             go.Scatter(x=x, y=p_d)], filename="t.html", auto_open=False)
 
 
 class AdjointFFTResponse(LinearOperator):
@@ -106,7 +106,7 @@ if __name__ == "__main__":
     data_power = log(fft(d).power_analyze(binbounds=p_space.binbounds))
     d_data = d.val.get_full_data().real
     if rank == 0:
-        pl.plot([go.Heatmap(z=d_data)], filename='data.html')
+        pl.plot([go.Heatmap(z=d_data)], filename='data.html', auto_open=False)
 
     #  Minimization strategy
     def convergence_measure(a_energy, iteration):  # returns current energy

demos/wiener_filter_via_hamiltonian.py

@@ -10,6 +10,7 @@ rank = comm.rank
 
 np.random.seed(42)
 
+
 class AdjointFFTResponse(LinearOperator):
     def __init__(self, FFT, R, default_spaces=None):
         super(AdjointFFTResponse, self).__init__(default_spaces)
@@ -23,6 +24,7 @@ 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
@@ -36,13 +38,12 @@ class AdjointFFTResponse(LinearOperator):
         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
@@ -52,7 +53,8 @@ if __name__ == "__main__":
     # Setting up power space
     p_space = PowerSpace(h_space, distribution_strategy=distribution_strategy)
 
-    # Choosing the prior correlation structure and defining correlation operator
+    # Choosing the prior correlation structure and defining
+    # correlation operator
     p_spec = (lambda k: (42 / (k + 1) ** 3))
 
     S = create_power_operator(h_space, power_spectrum=p_spec,
@@ -69,7 +71,7 @@ if __name__ == "__main__":
     Instrument = DiagonalOperator(s_space, diagonal=1.)
 #    Instrument._diagonal.val[200:400, 200:400] = 0
 
-    #Adding a harmonic transformation to the instrument
+    # Adding a harmonic transformation to the instrument
     R = AdjointFFTResponse(fft, Instrument)
     signal_to_noise = 1.
     N = DiagonalOperator(s_space, diagonal=ss.var()/signal_to_noise, bare=True)
@@ -84,9 +86,9 @@ if __name__ == "__main__":
 
     # Choosing the minimization strategy
 
-    def convergence_measure(energy, iteration): # returns current energy
+    def convergence_measure(energy, iteration):  # returns current energy
         x = energy.value
-        print (x, iteration)
+        print(x, iteration)
 
 #    minimizer = SteepestDescent(convergence_tolerance=0,
 #                                iteration_limit=50,
@@ -109,20 +111,19 @@ if __name__ == "__main__":
     m0 = Field(h_space, val=.0)
 
     # Initializing the Wiener Filter energy
-    energy = WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S, inverter=inverter)
+    energy = WienerFilterEnergy(position=m0, d=d, R=R, N=N, S=S)
     D0 = energy.curvature
 
     # Solving the problem analytically
     m0 = D0.inverse_times(j)
 
-    sample_variance = Field(sh.domain,val=0. + 0j)
-    sample_mean = Field(sh.domain,val=0. + 0j)
+    sample_variance = Field(sh.domain, val=0. + 0j)
+    sample_mean = Field(sh.domain, val=0. + 0j)
 
     # sampling the uncertainty map
     n_samples = 1
     for i in range(n_samples):
-        sample = sugar.generate_posterior_sample(m0,D0)
+        sample = sugar.generate_posterior_sample(m0, D0)
         sample_variance += sample**2
         sample_mean += sample
     variance = sample_variance/n_samples - (sample_mean/n_samples)
-

nifty/library/wiener_filter/wiener_filter_energy.py

@@ -7,7 +7,7 @@ class WienerFilterEnergy(Energy):
     """The Energy for the Wiener filter.
 
     It covers the case of linear measurement with
-    Gaussian noise and Gaussain signal prior with known covariance.
+    Gaussian noise and Gaussian signal prior with known covariance.
 
     Parameters
     ----------