Merge branch 'nicer_plots_demo3' into 'NIFTy_5'

Nicer plots demo3

See merge request ift/nifty-dev!93

demos/getting_started_3.py

@@ -41,11 +41,12 @@ if __name__ == '__main__':
     power_space = A.target[0]
     power_distributor = ift.PowerDistributor(harmonic_space, power_space)
     dummy = ift.Field.from_random('normal', harmonic_space)
-    domain = ift.MultiDomain.union(
-        (A.domain, ift.MultiDomain.make({'xi': harmonic_space})))
+    domain = ift.MultiDomain.union((A.domain,
+                                    ift.MultiDomain.make({
+                                        'xi': harmonic_space
+                                    })))
 
-    correlated_field = ht(
-        power_distributor(A)*ift.FieldAdapter(domain, "xi"))
+    correlated_field = ht(power_distributor(A)*ift.FieldAdapter(domain, "xi"))
     # alternatively to the block above one can do:
     # correlated_field = ift.CorrelatedField(position_space, A)
 
@@ -54,8 +55,7 @@ if __name__ == '__main__':
 
     # Building the Line of Sight response
     LOS_starts, LOS_ends = get_random_LOS(100)
-    R = ift.LOSResponse(position_space, starts=LOS_starts,
-                        ends=LOS_ends)
+    R = ift.LOSResponse(position_space, starts=LOS_starts, ends=LOS_ends)
     # build signal response model and model likelihood
     signal_response = R(signal)
     # specify noise
@@ -68,8 +68,7 @@ if __name__ == '__main__':
     data = signal_response(MOCK_POSITION) + N.draw_sample()
 
     # set up model likelihood
-    likelihood = ift.GaussianEnergy(
-        mean=data, covariance=N)(signal_response)
+    likelihood = ift.GaussianEnergy(mean=data, covariance=N)(signal_response)
 
     # set up minimization and inversion schemes
     ic_sampling = ift.GradientNormController(iteration_limit=100)
@@ -83,17 +82,18 @@ if __name__ == '__main__':
     INITIAL_POSITION = ift.from_random('normal', domain)
     position = INITIAL_POSITION
 
-    ift.plot(signal(MOCK_POSITION), title='ground truth')
-    ift.plot(R.adjoint_times(data), title='data')
-    ift.plot([A(MOCK_POSITION)], title='power')
-    ift.plot_finish(nx=3, xsize=16, ysize=5, title="setup", name="setup.png")
+    ift.plot(signal(MOCK_POSITION), title='Ground Truth')
+    ift.plot(R.adjoint_times(data), title='Data')
+    ift.plot([A(MOCK_POSITION)], title='Power Spectrum')
+    ift.plot_finish(ny=1, nx=3, xsize=24, ysize=6, name="setup.png")
 
     # number of samples used to estimate the KL
     N_samples = 20
     for i in range(2):
         metric = H(ift.Linearization.make_var(position)).metric
-        samples = [metric.draw_sample(from_inverse=True)
-                   for _ in range(N_samples)]
+        samples = [
+            metric.draw_sample(from_inverse=True) for _ in range(N_samples)
+        ]
 
         KL = ift.SampledKullbachLeiblerDivergence(H, samples)
         KL = ift.EnergyAdapter(position, KL)
@@ -102,15 +102,16 @@ if __name__ == '__main__':
 
         ift.plot(signal(position), title="reconstruction")
         ift.plot([A(position), A(MOCK_POSITION)], title="power")
-        ift.plot_finish(nx=2, xsize=12, ysize=6, title="loop", name="loop.png")
+        ift.plot_finish(ny=1, ysize=6, xsize=16, name="loop.png")
 
     sc = ift.StatCalculator()
     for sample in samples:
-        sc.add(signal(sample+position))
-    ift.plot(sc.mean, title="mean")
-    ift.plot(ift.sqrt(sc.var), title="std deviation")
-
-    powers = [A(s+position) for s in samples]
-    ift.plot([A(position), A(MOCK_POSITION)]+powers, title="power")
-    ift.plot_finish(nx=3, xsize=16, ysize=5, title="results",
-                    name="results.png")
+        sc.add(signal(sample + position))
+    ift.plot(sc.mean, title="Posterior Mean")
+    ift.plot(ift.sqrt(sc.var), title="Posterior Standard Deviation")
+
+    powers = [A(s + position) for s in samples]
+    ift.plot(
+        [A(position), A(MOCK_POSITION)] + powers,
+        title="Sampled Posterior Power Spectrum")
+    ift.plot_finish(ny=1, nx=3, xsize=24, ysize=6, name="results.png")