Improvements demo 3

demos/getting_started_3.py

@@ -17,129 +17,132 @@
 
 ############################################################
 # Non-linear tomography
-# data is line of sight (LOS) field
-# random lines (set mode=0), radial lines (mode=1)
+# The data is integrated lines of sight
+# Random lines (set mode=0), radial lines (mode=1)
 #############################################################
-mode = 0
 
-import nifty5 as ift
 import numpy as np
 
+import nifty5 as ift
+
+
+def random_los(n_los):
+    starts = list(np.random.uniform(0, 1, (n_los, 2)).T)
+    ends = list(0.5 + 0*np.random.uniform(0, 1, (n_los, 2)).T)
+    return starts, ends
+
 
-def get_random_LOS(n_los):
-    # Setting up LOS
+def radial_los(n_los):
     starts = list(np.random.uniform(0, 1, (n_los, 2)).T)
-    if mode == 0:
     ends = list(np.random.uniform(0, 1, (n_los, 2)).T)
-    else:
-        ends = list(0.5+0*np.random.uniform(0, 1, (n_los, 2)).T)
     return starts, ends
 
 
 if __name__ == '__main__':
-    # FIXME description of the tutorial
     np.random.seed(420)
-    np.seterr(all='raise')
+
+    # Choose between random line-of-sight response (mode=1) and radial lines
+    # of sight (mode=2)
+    mode = 1
+
     position_space = ift.RGSpace([128, 128])
 
-    # Setting up an amplitude model for the field
-    A = ift.AmplitudeModel(position_space, 64, 3, 0.4, -5., 0.5, 0.4, 0.3)
-    # made choices:
+    # Set up an amplitude model for the field
+    # The parameters mean:
     # 64 spectral bins
     #
     # Spectral smoothness (affects Gaussian process part)
     # 3 = relatively high variance of spectral curbvature
     # 0.4 = quefrency mode below which cepstrum flattens
     #
-    # power law part of spectrum:
-    # -5= preferred power-law slope
+    # Power-law part of spectrum:
+    # -5 = preferred power-law slope
     # 0.5 = low variance of power-law slope
-    #
-    # Gaussian process part of log-spectrum
-    # 0.4 = y-intercept mean of additional power 
-    # 0.3 = y-intercept variance of additional power
+    # 0.4 = y-intercept mean
+    # 0.3 = relatively high y-intercept variance
+    A = ift.AmplitudeModel(position_space, 64, 3, 0.4, -5., 0.5, 0.4, 0.3)
 
-    # Building the model for a correlated signal
+    # Build the model for a correlated signal
     harmonic_space = position_space.get_default_codomain()
     ht = ift.HarmonicTransformOperator(harmonic_space, position_space)
     power_space = A.target[0]
     power_distributor = ift.PowerDistributor(harmonic_space, power_space)
 
-    vol = harmonic_space.scalar_dvol
-    vol = ift.ScalingOperator(vol**(-0.5), harmonic_space)
+    vol = ift.ScalingOperator(harmonic_space.scalar_dvol**(-0.5),
+                              harmonic_space)
     correlated_field = ht(
         vol(power_distributor(A))*ift.ducktape(harmonic_space, None, 'xi'))
-    # alternatively to the block above one can do:
-    #correlated_field = ift.CorrelatedField(position_space, A)
+    # Alternatively, one can use:
+    # correlated_field = ift.CorrelatedField(position_space, A)
 
-    # apply some nonlinearity
+    # Apply a nonlinearity
     signal = ift.positive_tanh(correlated_field)
 
-    # Building the Line of Sight response
-    LOS_starts, LOS_ends = get_random_LOS(100)
+    # Build the line-of-sight response and define signal response
+    LOS_starts, LOS_ends = random_los(100) if mode == 1 else radial_los(100)
     R = ift.LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends)
-    # build signal response model and model likelihood
     signal_response = R(signal)
-    # specify noise
+
+    # Specify noise
     data_space = R.target
     noise = .001
     N = ift.ScalingOperator(noise, data_space)
 
-    # generate mock signal and data
-    MOCK_POSITION = ift.from_random('normal', signal_response.domain)
-    data = signal_response(MOCK_POSITION) + N.draw_sample()
-
-    # set up model likelihood
-    likelihood = ift.GaussianEnergy(mean=data, covariance=N)(signal_response)
+    # Generate mock signal and data
+    mock_position = ift.from_random('normal', signal_response.domain)
+    data = signal_response(mock_position) + N.draw_sample()
 
-    # set up minimization and inversion schemes
+    # Minimization parameters
     ic_sampling = ift.GradientNormController(iteration_limit=100)
     ic_newton = ift.GradInfNormController(
         name='Newton', tol=1e-7, iteration_limit=35)
     minimizer = ift.NewtonCG(ic_newton)
 
-    # build model Hamiltonian
+    # Set up model likelihood and information Hamiltonian
+    likelihood = ift.GaussianEnergy(mean=data, covariance=N)(signal_response)
     H = ift.Hamiltonian(likelihood, ic_sampling)
 
-    INITIAL_POSITION = ift.MultiField.full(H.domain, 0.)
-    position = INITIAL_POSITION
+    initial_position = ift.MultiField.full(H.domain, 0.)
+    position = initial_position
 
     plot = ift.Plot()
-    plot.add(signal(MOCK_POSITION), title='Ground Truth')
+    plot.add(signal(mock_position), title='Ground Truth')
     plot.add(R.adjoint_times(data), title='Data')
-    plot.add([A.force(MOCK_POSITION)], title='Power Spectrum')
+    plot.add([A.force(mock_position)], title='Power Spectrum')
     plot.output(ny=1, nx=3, xsize=24, ysize=6, name="setup.png")
 
     # number of samples used to estimate the KL
     N_samples = 20
 
-    # five intermediate steps in minimization to illustrate progress
+    # Draw new samples to approximate the KL five times
     for i in range(5):
-        # set up KL
         KL = ift.KL_Energy(position, H, N_samples)
-        # minimize KL until iteration limit reached
+        # Minimize KL
         KL, convergence = minimizer(KL)
-        # read out position
+        # Update position
         position = KL.position
-        # plot momentariy field
+
+        # Plot current reconstruction
         plot = ift.Plot()
         plot.add(signal(KL.position), title="reconstruction")
-        plot.add([A.force(KL.position), A.force(MOCK_POSITION)], title="power")
+        plot.add([A.force(KL.position), A.force(mock_position)], title="power")
         plot.output(ny=1, ysize=6, xsize=16, name="loop-{:02}.png".format(i))
 
-    # final plot 
+    # Draw posterior samples
     KL = ift.KL_Energy(position, H, N_samples)
-    plot = ift.Plot()
-    # do statistics
     sc = ift.StatCalculator()
     for sample in KL.samples:
         sc.add(signal(sample + KL.position))
+
+    # Plotting
+    plot = ift.Plot()
     plot.add(sc.mean, title="Posterior Mean")
     plot.add(ift.sqrt(sc.var), title="Posterior Standard Deviation")
 
     powers = [A.force(s + KL.position) for s in KL.samples]
     plot.add(
-        powers + [A.force(KL.position), A.force(MOCK_POSITION)],
+        powers + [A.force(KL.position),
+                  A.force(mock_position)],
         title="Sampled Posterior Power Spectrum",
         linewidth=[1.]*len(powers) + [3., 3.])
     plot.output(ny=1, nx=3, xsize=24, ysize=6, name="results.png")