nifty5 -> nifty7 (5/n)

3_more_examples.py

@@ -28,21 +28,24 @@ for mode in [0, 1]:
     power_space = ift.PowerSpace(harmonic_space)
     HT = ift.HarmonicTransformOperator(harmonic_space, target=position_space)
 
-    # Set up an amplitude operator for the field
-    dct = {
-        'target': power_space,
-        'n_pix': 64,  # 64 spectral bins
-        # Spectral smoothness (affects Gaussian process part)
-        'a': 10,  # relatively high variance of spectral curvature
-        'k0': .2,  # quefrency mode below which cepstrum flattens
-        # Power-law part of spectrum:
-        'sm': -4,  # preferred power-law slope
-        'sv': .6,  # low variance of power-law slope
-        'im': -3,  # y-intercept mean, in-/decrease for more/less contrast
-        'iv': 2.  # y-intercept variance
+    args = {
+        'offset_mean': 0,
+        'offset_std': (1e-3, 1e-6),
+
+        # Amplitude of field fluctuations
+        'fluctuations': (1., 0.8),  # 1.0, 1e-2
+
+        # Exponent of power law power spectrum component
+        'loglogavgslope': (-3., 1),  # -6.0, 1
+
+        # Amplitude of integrated Wiener process power spectrum component
+        'flexibility': (2, 1.),  # 1.0, 0.5
+
+        # How ragged the integrated Wiener process component is
+        'asperity': (0.5, 0.4)  # 0.1, 0.5
     }
-    A = ift.SLAmplitude(**dct)
-    correlated_field = ift.CorrelatedField(position_space, A)
+    correlated_field = ift.SimpleCorrelatedField(position_space, **args)
+    A = correlated_field.amplitude
 
     dct = {}
     if mode == 0:
@@ -64,8 +67,8 @@ for mode in [0, 1]:
 
     # Solve inference problem
     ic_sampling = ift.GradientNormController(iteration_limit=100)
-    ic_newton = ift.GradInfNormController(
-        name='Newton', tol=1e-6, iteration_limit=50)
+    ic_newton = ift.GradInfNormController(name='Newton', tol=1e-6,
+                                          iteration_limit=10)
     minimizer = ift.NewtonCG(ic_newton)
     H = ift.StandardHamiltonian(likelihood, ic_sampling)
     initial_mean = ift.MultiField.full(H.domain, 0.)
@@ -73,9 +76,9 @@ for mode in [0, 1]:
     N_samples = 5
     for _ in range(5):
         # Draw new samples and minimize KL
-        KL = ift.MetricGaussianKL(mean, H, N_samples)
+        KL = ift.MetricGaussianKL(mean, H, N_samples, True)
         KL, convergence = minimizer(KL)
         mean = KL.position
     N_posterior_samples = 30
-    KL = ift.MetricGaussianKL(mean, H, N_posterior_samples)
+    KL = ift.MetricGaussianKL(mean, H, N_posterior_samples, True)
     h.plot_reconstruction_2d(data, ground_truth, KL, signal, R, A, name[mode])

helpers/responses.py

@@ -44,7 +44,7 @@ def exposure_response(position_space):
     exposure[:, x_shape*4//5:x_shape] *= .1
     exposure[:, x_shape//2:x_shape*3//2] *= 3.
 
-    exposure = ift.Field.makeField(position_space, exposure)
+    exposure = ift.makeField(position_space, exposure)
     E = ift.makeOp(exposure)
     G = ift.GeometryRemover(E.target)
     return G @ E