Minor formatting

pyramid/reconstruction.py

@@ -117,7 +117,7 @@ def optimize_linear(data, regularisator=None, maxiter=1000, verbosity=0):
     # Set up necessary objects:
     cost = Costfunction(data, regularisator)
     print cost(np.zeros(cost.n))
-    x_opt = jutil.cg.conj_grad_minimize(cost, max_iter=20)
+    x_opt = cg.conj_grad_minimize(cost, max_iter=20)
     print cost(x_opt)
     # Create and return fitting MagData object:
     mag_opt = MagData(data.a, np.zeros((3,)+data.dim))
@@ -164,18 +164,19 @@ def optimize_nonlin(data, first_guess=None, regularisator=None):
 #    print jac1, jac2.T, abs(jac1-jac2.T).sum()
 #    print jac1.shape, jac2.shape
 
-
- #   jac1 = np.array([fwd_model.jac_dot(x_0, np.eye(fwd_model.m)[:, i]) for i in range(fwd_model.m)])
- #   jac2 = np.array([fwd_model.jac_T_dot(x_0, np.eye(fwd_model.n)[:, i]) for i in range(fwd_model.n)])
- #   print proj_jac1.dot(pm_jac1)
- #   print (pm_jac2.dot(proj_jac2)).T
- #   print jac1
+#   jac1 = np.array([fwd_model.jac_dot(x_0, np.eye(fwd_model.m)[:, i])
+#                    for i in range(fwd_model.m)])
+#   jac2 = np.array([fwd_model.jac_T_dot(x_0, np.eye(fwd_model.n)[:, i])
+#                    for i in range(fwd_model.n)])
+#   print proj_jac1.dot(pm_jac1)
+#   print (pm_jac2.dot(proj_jac2)).T
+#   print jac1
 #    print jac2.T
 #    print abs(jac1-jac2.T).sum()
 #    print jac1.shape, jac2.shape
 
     assert len(x_0) == cost.n, (len(x_0), cost.m, cost.n)
-    result = jutil.minimizer.minimize(cost, x_0, options={"conv_rel":1e-2}, tol={"max_iteration":4})
+    result = minimizer.minimize(cost, x_0, options={"conv_rel": 1e-2}, tol={"max_iteration": 4})
     x_opt = result.x
     print cost(x_opt)
     mag_opt = MagData(data.a, np.zeros((3,)+data.dim))

scripts/collaborations/example_joern.py

@@ -38,7 +38,7 @@ inter = 'none'
 dim = (1,) + (64, 64)
 dim_small = (64, 64)
 smoothed_pictures = True
-lam = 1E-4
+lam = 1E-6
 order = 1
 log = True
 PATH = '../../output/joern/'
@@ -65,7 +65,8 @@ mag_data_rec = rc.optimize_linear(data_set, regularisator=regularisator)
 print 'reconstruction time:', clock() - tic
 # Display the reconstructed phase map and holography image:
 phase_map_rec = pm(mag_data_rec)
-phase_map_rec.display_combined('Reconstr. Distribution', gain=gain, interpolation=inter, show=False)
+phase_map_rec.display_combined('Reconstr. Distribution', gain=gain,
+                               interpolation=inter, show=False)
 plt.savefig(dirname + "/reconstr.png")
 
 # Plot the magnetization:
@@ -82,14 +83,16 @@ plt.savefig(dirname + "/difference.png")
 # Get the average difference from the experimental results:
 print 'Average difference:', np.average(phase_diff.phase)
 # Plot holographic contour maps with overlayed magnetic distributions:
-axis = phase_map_rec.display_holo('Magnetization Overlay', gain=0.1, interpolation=inter, show=False)
+axis = phase_map_rec.display_holo('Magnetization Overlay', gain=0.1,
+                                  interpolation=inter, show=False)
 mag_data_rec.quiver_plot(axis=axis, show=False)
 axis = plt.gca()
 axis.set_xlim(20, 45)
 axis.set_ylim(20, 45)
 plt.savefig(dirname + "/overlay_normal.png")
 
-axis = phase_map_rec.display_holo('Magnetization Overlay', gain=0.1, interpolation=inter, show=False)
+axis = phase_map_rec.display_holo('Magnetization Overlay', gain=0.1,
+                                  interpolation=inter, show=False)
 mag_data_rec.quiver_plot(axis=axis, log=log, show=False)
 axis = plt.gca()
 axis.set_xlim(20, 45)