cosmetics

demos/critical_filtering.py

@@ -8,7 +8,7 @@ np.random.seed(42)
 if __name__ == "__main__":
     # Set up position space
     s_space = ift.RGSpace([128, 128])
-    #s_space = ift.HPSpace(32)
+    # s_space = ift.HPSpace(32)
 
     # Define harmonic transformation and associated harmonic space
     h_space = s_space.get_default_codomain()
@@ -63,7 +63,8 @@ if __name__ == "__main__":
                                      tol_abs_gradnorm=1e-15)
     minimizer = ift.RelaxedNewton(IC1)
 
-    ICI = ift.GradientNormController(iteration_limit=500, tol_abs_gradnorm=1e-10)
+    ICI = ift.GradientNormController(iteration_limit=500,
+                                     tol_abs_gradnorm=1e-10)
     map_inverter = ift.ConjugateGradient(controller=ICI)
 
     ICI2 = ift.GradientNormController(iteration_limit=200,

demos/nonlinear_critical_filter.py

@@ -53,8 +53,9 @@ if __name__ == "__main__":
     MaskOperator = ift.DiagonalOperator(mask)
     R = ift.GeometryRemover(s_space)
     R = R*MaskOperator
-    #R = R*HT
-    #R = R * ift.create_harmonic_smoothing_operator((harmonic_space,),0,response_sigma)
+    # R = R*HT
+    # R = R * ift.create_harmonic_smoothing_operator((harmonic_space,), 0,
+    #                                                response_sigma)
     MeasurementOperator = R
 
     d_space = MeasurementOperator.target

demos/paper_demos/cartesian_wiener_filter.py

@@ -71,8 +71,8 @@ if __name__ == "__main__":
     mask_2 = ift.Field(signal_space_2, ift.dobj.from_global_data(mask_2))
 
     R = ift.GeometryRemover(signal_domain)
-    R = R*ift.DiagonalOperator(mask_1, signal_domain,spaces=0)
-    R = R*ift.DiagonalOperator(mask_2, signal_domain,spaces=1)
+    R = R*ift.DiagonalOperator(mask_1, signal_domain, spaces=0)
+    R = R*ift.DiagonalOperator(mask_2, signal_domain, spaces=1)
     R = R*ht
     R = R * ift.create_harmonic_smoothing_operator(harmonic_domain, 0,
                                                    response_sigma_1)
@@ -101,11 +101,13 @@ if __name__ == "__main__":
 
     plotdict = {"colormap": "Planck-like"}
     plot_space = ift.RGSpace((N_pixels_1, N_pixels_2))
-    ift.plot(ift.Field(plot_space,val=ht(mock_signal).val), name='mock_signal.png',
-             **plotdict)
-    ift.plot(ift.Field(plot_space,val=data.val), name='data.png', **plotdict)
-    ift.plot(ift.Field(plot_space,val=m.val), name='map.png', **plotdict)
+    ift.plot(ift.Field(plot_space, val=ht(mock_signal).val),
+             name='mock_signal.png', **plotdict)
+    ift.plot(ift.Field(plot_space, val=data.val), name='data.png', **plotdict)
+    ift.plot(ift.Field(plot_space, val=m.val), name='map.png', **plotdict)
     # sampling the uncertainty map
     mean, variance = ift.probe_with_posterior_samples(wiener_curvature, ht, 10)
-    ift.plot(ift.Field(plot_space, val=ift.sqrt(variance).val), name="uncertainty.png", **plotdict)
-    ift.plot(ift.Field(plot_space, val=(mean+m).val), name="posterior_mean.png", **plotdict)
+    ift.plot(ift.Field(plot_space, val=ift.sqrt(variance).val),
+             name="uncertainty.png", **plotdict)
+    ift.plot(ift.Field(plot_space, val=(mean+m).val),
+             name="posterior_mean.png", **plotdict)

demos/paper_demos/wiener_filter.py

@@ -38,7 +38,8 @@ if __name__ == "__main__":
     R = ift.GeometryRemover(signal_space)
     R = R*ift.DiagonalOperator(mask)
     R = R*ht
-    R = R * ift.create_harmonic_smoothing_operator((harmonic_space,),0,response_sigma)
+    R = R * ift.create_harmonic_smoothing_operator((harmonic_space,), 0,
+                                                   response_sigma)
     data_domain = R.target[0]
 
     noiseless_data = R(mock_signal)

demos/wiener_filter_via_hamiltonian.py

@@ -84,5 +84,5 @@ if __name__ == "__main__":
 
     # try to do the same with diagonal probing
     variance = ift.probe_diagonal(ht*curv.inverse*ht.adjoint, 100)
-    #sm = ift.FFTSmoothingOperator(s_space, sigma=0.015)
+    # sm = ift.FFTSmoothingOperator(s_space, sigma=0.015)
     ift.plot(variance, name="posterior_variance2.png", **plotdict)