demo tweaks

demos/log_normal_wiener_filter.py

@@ -7,7 +7,7 @@ if __name__ == "__main__":
     correlation_length = 1.     # Typical distance over which the field is correlated
     field_variance = 2.         # Variance of field in position space
     response_sigma = 0.02       # Smoothing length of response (in same unit as L)
-    signal_to_noise = 100       # The signal to noise ratio
+    signal_to_noise = 100         # The signal to noise ratio
     np.random.seed(43)          # Fixing the random seed
 
     def power_spectrum(k):      # Defining the power spectrum
@@ -20,66 +20,65 @@ if __name__ == "__main__":
     # signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
     signal_space = ift.HPSpace(16)
     harmonic_space = signal_space.get_default_codomain()
-    fft = ift.FFTOperator(harmonic_space, target=signal_space)
+    ht = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
     power_space = ift.PowerSpace(harmonic_space)
 
     # Creating the mock signal
     S = ift.create_power_operator(harmonic_space,
                                   power_spectrum=power_spectrum)
     mock_power = ift.PS_field(power_space, power_spectrum)
-    mock_signal = fft(ift.power_synthesize(mock_power, real_signal=True))
+    mock_signal = ift.power_synthesize(mock_power, real_signal=True)
 
     # Setting up an exemplary response
     mask = ift.Field.ones(signal_space)
     N10 = int(N_pixels/10)
     # mask.val[N10*5:N10*9, N10*5:N10*9] = 0.
-    R = ift.ResponseOperator(signal_space, sigma=(response_sigma,),
-                             exposure=(mask,))
+    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 = ift.ResponseOperator(signal_space, sigma=(response_sigma,),
+    #                         sensitivity=(mask,))
     data_domain = R.target[0]
-    R_harmonic = R*fft
 
     # Setting up the noise covariance and drawing a random noise realization
-    ndiag = ift.Field.full(data_domain, mock_signal.var()/signal_to_noise)
-    N = ift.DiagonalOperator(ndiag.weight(1))
-    noise = ift.Field.from_random(domain=data_domain, random_type='normal',
-        std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
-    data = R(ift.exp(mock_signal)) + noise
+    noiseless_data = R(mock_signal)
+    noise_amplitude = noiseless_data.std()/signal_to_noise
+    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 = noiseless_data + noise
 
     # Wiener filter
     m0 = ift.Field.zeros(harmonic_space)
     ctrl = ift.GradientNormController(tol_abs_gradnorm=0.0001)
     ctrl2 = ift.GradientNormController(tol_abs_gradnorm=0.1, name="outer")
     inverter = ift.ConjugateGradient(controller=ctrl)
-    energy = ift.library.LogNormalWienerFilterEnergy(m0, data, R_harmonic,
+    energy = ift.library.LogNormalWienerFilterEnergy(m0, data, R,
                                                      N, S, inverter=inverter)
-    # minimizer1 = ift.VL_BFGS(controller=ctrl2, max_history_length=20)
-    minimizer2 = ift.RelaxedNewton(controller=ctrl2)
-    # minimizer3 = ift.SteepestDescent(controller=ctrl2)
+    # minimizer = ift.VL_BFGS(controller=ctrl2, max_history_length=20)
+    minimizer = ift.RelaxedNewton(controller=ctrl2)
+    # minimizer = ift.SteepestDescent(controller=ctrl2)
 
-    # me1 = minimizer1(energy)
-    me2 = minimizer2(energy)
-    # me3 = minimizer3(energy)
-
-    # m1 = fft(me1[0].position)
-    m2 = fft(me2[0].position)
-    # m3 = fft(me3[0].position)
+    me = minimizer(energy)
+    m = ht(me[0].position)
 
     # Plotting
     plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
                 "colormap": "Planck-like"}
-    ift.plot(mock_signal, name="mock_signal.png", **plotdict)
+    ift.plot(ht(mock_signal), name="mock_signal.png", **plotdict)
     logdata = np.log(ift.dobj.to_global_data(data.val)).reshape(signal_space.shape)
     ift.plot(ift.Field(signal_space, val=ift.dobj.from_global_data(logdata)),
              name="log_of_data.png", **plotdict)
-    # ift.plot(m1,name='m_LBFGS.png', **plotdict)
-    ift.plot(m2, name='m_Newton.png', **plotdict)
-    # ift.plot(m3, name='m_SteepestDescent.png', **plotdict)
+    ift.plot(m, name='m.png', **plotdict)
 
     # Probing the variance
     class Proby(ift.DiagonalProberMixin, ift.Prober):
         pass
     proby = Proby(signal_space, probe_count=1)
-    proby(lambda z: fft(me2[0].curvature.inverse_times(fft.adjoint_times(z))))
+    proby(lambda z: ht(me2[0].curvature.inverse_times(ht.adjoint_times(z))))
 
     sm = ift.FFTSmoothingOperator(signal_space, sigma=0.02)
     variance = sm(proby.diagonal.weight(-1))

demos/paper_demos/cartesian_wiener_filter.py

@@ -2,7 +2,7 @@ import numpy as np
 import nifty4 as ift
 
 if __name__ == "__main__":
-    signal_to_noise = 1.5  # The signal to noise ratio
+    signal_to_noise = 0.5  # The signal to noise ratio
 
     # Setting up parameters
     L_1 = 2.                   # Total side-length of the domain
@@ -30,7 +30,7 @@ if __name__ == "__main__":
     harmonic_domain = ift.DomainTuple.make((harmonic_space_1,
                                             harmonic_space_2))
 
-    fft_1 = ift.FFTOperator(harmonic_domain, space=0)
+    ht_1 = ift.HarmonicTransformOperator(harmonic_domain, space=0)
     power_space_1 = ift.PowerSpace(harmonic_space_1)
 
     mock_power_1 = ift.PS_field(power_space_1, power_spectrum_1)
@@ -39,12 +39,12 @@ if __name__ == "__main__":
         a = 4 * correlation_length_2 * field_variance_2**2
         return a / (1 + k * correlation_length_2) ** 2.5
 
-    fft_2 = ift.FFTOperator(mid_domain, space=1)
+    ht_2 = ift.HarmonicTransformOperator(mid_domain, space=1)
     power_space_2 = ift.PowerSpace(harmonic_space_2)
 
     mock_power_2 = ift.PS_field(power_space_2, power_spectrum_2)
 
-    fft = fft_2*fft_1
+    ht = ht_2*ht_1
 
     mock_power = ift.Field(
         (power_space_1, power_space_2),
@@ -57,7 +57,7 @@ if __name__ == "__main__":
     S = ift.DiagonalOperator(diagonal)
 
     np.random.seed(10)
-    mock_signal = fft(ift.power_synthesize(mock_power, real_signal=True))
+    mock_signal = ift.power_synthesize(mock_power, real_signal=True)
 
     # Setting up a exemplary response
     N1_10 = int(N_pixels_1/10)
@@ -70,49 +70,50 @@ if __name__ == "__main__":
     mask_2[N2_10*7:N2_10*9] = 0.
     mask_2 = ift.Field(signal_space_2, ift.dobj.from_global_data(mask_2))
 
-    R = ift.ResponseOperator(signal_domain,
-                             sigma=(response_sigma_1, response_sigma_2),
-                             exposure=(mask_1, 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*ht
+    R = R * ift.create_harmonic_smoothing_operator(harmonic_domain, 0,
+                                                   response_sigma_1)
+    R = R * ift.create_harmonic_smoothing_operator(harmonic_domain, 1,
+                                                   response_sigma_2)
     data_domain = R.target
-    R_harmonic = R*fft
 
+    noiseless_data = R(mock_signal)
+    noise_amplitude = noiseless_data.std()/signal_to_noise
     # Setting up the noise covariance and drawing a random noise realization
-    ndiag = ift.Field.full(data_domain, mock_signal.var()/signal_to_noise)
-    N = ift.DiagonalOperator(ndiag.weight(1))
+    ndiag = ift.Field.full(data_domain, noise_amplitude**2)
+    N = ift.DiagonalOperator(ndiag)
     noise = ift.Field.from_random(
         domain=data_domain, random_type='normal',
-        std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
-    data = R(mock_signal) + noise
+        std=noise_amplitude, mean=0)
+    data = noiseless_data + noise
 
     # Wiener filter
-    j = R_harmonic.adjoint_times(N.inverse_times(data))
+    j = R.adjoint_times(N.inverse_times(data))
     ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=0.1)
     inverter = ift.ConjugateGradient(controller=ctrl)
     wiener_curvature = ift.library.WienerFilterCurvature(
-        S=S, N=N, R=R_harmonic, inverter=inverter)
+        S=S, N=N, R=R, inverter=inverter)
 
     m_k = wiener_curvature.inverse_times(j)
-    m = fft(m_k)
+    m = ht(m_k)
 
-    # Probing the variance
-    class Proby(ift.DiagonalProberMixin, ift.Prober):
-        pass
-    proby = Proby((signal_space_1, signal_space_2), probe_count=1, ncpu=1)
-    proby(lambda z: fft(wiener_curvature.inverse_times(fft.inverse_times(z))))
-#    sm = SmoothingOperator(signal_space, sigma=0.02)
-#    variance = sm(proby.diagonal.weight(-1))
-    variance = proby.diagonal.weight(-1)
-
-    plot_space = ift.RGSpace((N_pixels_1, N_pixels_2))
-    sm = ift.FFTSmoothingOperator(plot_space, sigma=0.03)
     plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
                 "colormap": "Planck-like"}
-    ift.plot(
-        ift.log(ift.sqrt(sm(ift.Field(plot_space, val=variance.val.real)))),
-        name='uncertainty.png', zmin=0., zmax=3., title="Uncertainty map",
-        **plotdict)
-    ift.plot(ift.Field(plot_space, val=mock_signal.val.real),
-             name='mock_signal.png', **plotdict)
-    ift.plot(ift.Field(plot_space, val=data.val.real),
-             name='data.png', **plotdict)
-    ift.plot(ift.Field(plot_space, val=m.val.real), name='map.png', **plotdict)
+    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)
+    # sampling the uncertainty map
+    sample_variance = ift.Field.zeros(signal_domain)
+    sample_mean = ift.Field.zeros(signal_domain)
+    n_samples = 10
+    for i in range(n_samples):
+        sample = ht(wiener_curvature.generate_posterior_sample()) + m
+        sample_variance += sample**2
+        sample_mean += sample
+    variance = sample_variance/n_samples - (sample_mean/n_samples)**2
+    ift.plot(ift.Field(plot_space, val=ift.sqrt(variance).val), name="uncertainty.png", **plotdict)

demos/paper_demos/wiener_filter.py

@@ -6,7 +6,7 @@ if __name__ == "__main__":
     # Setting up parameters
     L = 2.                         # Total side-length of the domain
     N_pixels = 512                 # Grid resolution (pixels per axis)
-    correlation_length_scale = 1.  # Typical distance over which the field is
+    correlation_length_scale = .2  # Typical distance over which the field is
                                    # correlated
     fluctuation_scale = 2.         # Variance of field in position space
     response_sigma = 0.05          # Smoothing length of response
@@ -19,7 +19,7 @@ if __name__ == "__main__":
 
     signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
     harmonic_space = signal_space.get_default_codomain()
-    fft = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
+    ht = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
     power_space = ift.PowerSpace(
         harmonic_space, binbounds=ift.PowerSpace.useful_binbounds(
             harmonic_space, logarithmic=True))
@@ -37,47 +37,43 @@ if __name__ == "__main__":
     mask = ift.Field(signal_space, ift.dobj.from_global_data(mask))
     R = ift.GeometryRemover(signal_space)
     R = R*ift.DiagonalOperator(mask)
-    R = R*fft
+    R = R*ht
     R = R * ift.create_harmonic_smoothing_operator((harmonic_space,),0,response_sigma)
     data_domain = R.target[0]
 
+    noiseless_data = R(mock_signal)
+    noise_amplitude = noiseless_data.std()/signal_to_noise
     # Setting up the noise covariance and drawing a random noise realization
-    ndiag = 1e-8*ift.Field.full(data_domain, fft(mock_signal).var()/signal_to_noise)
+    ndiag = ift.Field.full(data_domain, noise_amplitude**2)
     N = ift.DiagonalOperator(ndiag)
     noise = ift.Field.from_random(
         domain=data_domain, random_type='normal',
-        std=mock_signal.std()/np.sqrt(signal_to_noise), mean=0)
-    data = R(mock_signal) + noise
+        std=noise_amplitude, mean=0)
+    data = noiseless_data + noise
 
     # Wiener filter
     j = R.adjoint_times(N.inverse_times(data))
-    ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=1e-6)
+    ctrl = ift.GradientNormController(name="inverter", tol_abs_gradnorm=1e-2)
     inverter = ift.ConjugateGradient(controller=ctrl)
     wiener_curvature = ift.library.WienerFilterCurvature(
         S=S, N=N, R=R, inverter=inverter)
     m_k = wiener_curvature.inverse_times(j)
-    m = fft(m_k)
+    m = ht(m_k)
 
     plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
                 "colormap": "Planck-like"}
-    ift.plot(mock_signal, name="mock_signal.png", **plotdict)
+    ift.plot(ht(mock_signal), name="mock_signal.png", **plotdict)
     ift.plot(ift.Field(signal_space, val=data.val),
              name="data.png", **plotdict)
     ift.plot(m, name="map.png", **plotdict)
-    # Probing the uncertainty
-    class Proby(ift.DiagonalProberMixin, ift.Prober):
-        pass
-    proby = Proby(harmonic_space, probe_count=1, ncpu=1)
-    proby(lambda z: wiener_curvature.inverse_times(z))
 
-    sm = ift.FFTSmoothingOperator(signal_space, sigma=0.03)
-    variance = ift.sqrt(sm(proby.diagonal.weight(-1)))
-
-    # Plotting
-    plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
-                "colormap": "Planck-like"}
-    ift.plot(variance, name="uncertainty.png", **plotdict)
-    ift.plot(mock_signal, name="mock_signal.png", **plotdict)
-    ift.plot(ift.Field(signal_space, val=data.val),
-             name="data.png", **plotdict)
-    ift.plot(m, name="map.png", **plotdict)
+    # sampling the uncertainty map
+    sample_variance = ift.Field.zeros(signal_space)
+    sample_mean = ift.Field.zeros(signal_space)
+    n_samples = 10
+    for i in range(n_samples):
+        sample = ht(wiener_curvature.generate_posterior_sample()) + m
+        sample_variance += sample**2
+        sample_mean += sample
+    variance = sample_variance/n_samples - (sample_mean/n_samples)**2
+    ift.plot(ift.sqrt(variance), name="uncertainty.png", **plotdict)

demos/wiener_filter_easy.py

@@ -27,8 +27,8 @@ if __name__ == "__main__":
 
     # Set up the geometry
     s_space = ift.RGSpace([N_pixels, N_pixels], distances=pixel_width)
-    fft = ift.FFTOperator(s_space)
-    h_space = fft.target[0]
+    h_space = s_space.get_default_codomain()
+    ht = ift.HarmonicTransformOperator(h_space, s_space)
     p_space = ift.PowerSpace(h_space)
 
     # Create mock data
@@ -37,19 +37,19 @@ if __name__ == "__main__":
 
     sp = ift.PS_field(p_space, pow_spec)
     sh = ift.power_synthesize(sp, real_signal=True)
-    ss = fft.inverse_times(sh)
 
-    R = ift.FFTSmoothingOperator(s_space, sigma=response_sigma)
+    R = ht*ift.create_harmonic_smoothing_operator((h_space,),0,response_sigma)
 
-    signal_to_noise = 1
-    diag = ift.Field(s_space, ss.var()/signal_to_noise).weight(1)
-    N = ift.DiagonalOperator(diag)
+    noiseless_data = R(sh)
+    signal_to_noise = 1.
+    noise_amplitude = noiseless_data.std()/signal_to_noise
+    N = ift.DiagonalOperator(ift.Field.full(s_space, noise_amplitude**2))
     n = ift.Field.from_random(domain=s_space,
                               random_type='normal',
-                              std=ss.std()/np.sqrt(signal_to_noise),
+                              std=noise_amplitude,
                               mean=0)
 
-    d = R(ss) + n
+    d = noiseless_data + n
 
     # Wiener filter
 
@@ -57,8 +57,14 @@ if __name__ == "__main__":
     IC = ift.GradientNormController(name="inverter", iteration_limit=500,
                                     tol_abs_gradnorm=0.1)
     inverter = ift.ConjugateGradient(controller=IC)
-    S_inv = fft.adjoint*Sh.inverse*fft
-    D = (R.adjoint*N.inverse*R + S_inv).inverse
+    D = (R.adjoint*N.inverse*R + Sh.inverse).inverse
     # MR FIXME: we can/should provide a preconditioner here as well!
     D = ift.InversionEnabler(D, inverter)
     m = D(j)
+
+    plotdict = {"xlabel": "Pixel index", "ylabel": "Pixel index",
+                "colormap": "Planck-like"}
+    ift.plot(ht(sh), name="mock_signal.png", **plotdict)
+    ift.plot(ift.Field(s_space, val=d.val),
+             name="data.png", **plotdict)
+    ift.plot(ht(m), name="map.png", **plotdict)

demos/wiener_filter_via_curvature.py

@@ -16,14 +16,14 @@ if __name__ == "__main__":
     # sigma of field in position space sqrt(<|s_x|^2>)
     field_sigma = 2. * nu.K
     # smoothing length of response
-    response_sigma = 0.01*nu.m
-    # The signal to noise ratio ***CURRENTLY BROKEN***
-    signal_to_noise = 70.7
+    response_sigma = 0.03*nu.m
+    # The signal to noise ratio
+    signal_to_noise = 1
 
     # note that field_variance**2 = a*k_0/4. for this analytic form of power
     # spectrum
     def power_spectrum(k):
-    	#RL FIXME: signal_amplitude is not how much signal varies
+        #RL FIXME: signal_amplitude is not how much signal varies
         cldim = correlation_length**(2*dimensionality)
         a = 4/(2*np.pi) * cldim * field_sigma**2
         # to be integrated over spherical shells later on
@@ -39,7 +39,7 @@ if __name__ == "__main__":
 
     signal_space = ift.RGSpace(shape, distances=L/N_pixels)
     harmonic_space = signal_space.get_default_codomain()
-    fft = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
+    ht = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
     power_space = ift.PowerSpace(harmonic_space)
 
     # Creating the mock data
@@ -53,18 +53,18 @@ if __name__ == "__main__":
     sensitivity = (1./nu.m)**dimensionality/nu.K
     R = ift.GeometryRemover(signal_space)
     R = R*ift.ScalingOperator(sensitivity, signal_space)
-    R = R*fft
+    R = R*ht
     R = R * ift.create_harmonic_smoothing_operator((harmonic_space,),0,response_sigma)
     data_domain = R.target[0]
 
-    noise_amplitude = 1./signal_to_noise*field_sigma*sensitivity*((L/N_pixels)**dimensionality)
-    print "noise amplitude:", noise_amplitude
+    noiseless_data = R(mock_signal)
+    noise_amplitude = noiseless_data.std()/signal_to_noise
     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) + noise
+    data = noiseless_data + noise
      # Wiener filter
 
     j = R.adjoint_times(N.inverse_times(data))
@@ -75,14 +75,14 @@ if __name__ == "__main__":
         S=S, N=N, R=R, inverter=inverter)
 
     m = wiener_curvature.inverse_times(j)
-    m_s = fft(m)
+    m_s = ht(m)
 
     sspace2 = ift.RGSpace(shape, distances=L/N_pixels/nu.m)
 
-    ift.plot(ift.Field(sspace2, fft(mock_signal).val)/nu.K, name="mock_signal.png")
+    ift.plot(ift.Field(sspace2, ht(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=fft(R.adjoint_times(data)).val), name="data.png")
-    print "msig",np.min(fft(mock_signal).val)/nu.K, np.max(fft(mock_signal).val)/nu.K
+    ift.plot(ift.Field(sspace2, val=data.val), name="data.png")
+    print "msig",np.min(ht(mock_signal).val)/nu.K, np.max(ht(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, name="map.png")

demos/wiener_filter_via_hamiltonian.py

@@ -11,7 +11,7 @@ if __name__ == "__main__":