tweaks

demos/wiener_filter_via_curvature.py

@@ -2,6 +2,7 @@ import numpy as np
 import nifty4 as ift
 import numericalunits as nu
 
+
 if __name__ == "__main__":
     # In MPI mode, the random seed for numericalunits must be set by hand
     #nu.reset_units(43)
@@ -38,7 +39,7 @@ if __name__ == "__main__":
 
     signal_space = ift.RGSpace(shape, distances=L/N_pixels)
     harmonic_space = signal_space.get_default_codomain()
-    fft = ift.FFTOperator(harmonic_space, target=signal_space)
+    fft = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
     power_space = ift.PowerSpace(harmonic_space)
 
     # Creating the mock data
@@ -48,34 +49,27 @@ if __name__ == "__main__":
 
     mock_power = ift.PS_field(power_space, power_spectrum)
     mock_harmonic = ift.power_synthesize(mock_power, real_signal=True)
-    print mock_harmonic.val[0]/nu.K/(nu.m**dimensionality)
     mock_signal = fft(mock_harmonic)
-    print "msig",mock_signal.val[0:10]/nu.K
 
     sensitivity = (1./nu.m)**dimensionality/nu.K
-    R = ift.ResponseOperator(signal_space, sigma=(0.*response_sigma,),
+    R = ift.ResponseOperator(signal_space, sigma=(response_sigma,),
                              sensitivity=(sensitivity,))
     data_domain = R.target[0]
     R_harmonic = R*fft
 
     noise_amplitude = 1./signal_to_noise*field_sigma*sensitivity*((L/N_pixels)**dimensionality)
-    print noise_amplitude
+    print "noise amplitude:", noise_amplitude
     N = ift.DiagonalOperator(
         ift.Field.full(data_domain, noise_amplitude**2))
     noise = ift.Field.from_random(
         domain=data_domain, random_type='normal',
         std=noise_amplitude, mean=0)
-    data = R(mock_signal)
-    print data.val[5:10]
-    data += noise
-    print data.val[5:10]
+    data = R(mock_signal) + noise
      # Wiener filter
 
     j = R_harmonic.adjoint_times(N.inverse_times(data))
-    print "xx",j.val[0]*nu.K*(nu.m**dimensionality)
-    exit()
     ctrl = ift.GradientNormController(
-        verbose=True, tol_abs_gradnorm=1e-40/(nu.K*(nu.m**dimensionality)))
+        verbose=True, tol_abs_gradnorm=1e-5/(nu.K*(nu.m**dimensionality)))
     inverter = ift.ConjugateGradient(controller=ctrl)
     wiener_curvature = ift.library.WienerFilterCurvature(
         S=S, N=N, R=R_harmonic, inverter=inverter)
@@ -85,8 +79,10 @@ if __name__ == "__main__":
 
     sspace2 = ift.RGSpace(shape, distances=L/N_pixels/nu.m)
 
-    ift.plot(ift.Field(sspace2, mock_signal.val)/nu.K, name="mock_signal.png")
-    data = ift.dobj.to_global_data(data.val).reshape(sspace2.shape)
-    data = ift.Field(sspace2, val=ift.dobj.from_global_data(data))
-    ift.plot(ift.Field(sspace2, val=data), name="data.png")
-    ift.plot(ift.Field(sspace2, m_s.val)/nu.K, name="map.png")
+    ift.plot(ift.Field(sspace2, mock_signal.val)/nu.K, title="mock_signal.png")
+    #data = ift.dobj.to_global_data(data.val).reshape(sspace2.shape)
+    #data = ift.Field(sspace2, val=ift.dobj.from_global_data(data))
+    ift.plot(ift.Field(sspace2, val=R.adjoint_times(data).val), title="data.png")
+    print "msig",np.min(mock_signal.val)/nu.K, np.max(mock_signal.val)/nu.K
+    print "map",np.min(m_s.val)/nu.K, np.max(m_s.val)/nu.K
+    ift.plot(ift.Field(sspace2, m_s.val)/nu.K, title="map.png")