From 056b131f400e8d1ab76d91566f861747aa58b979 Mon Sep 17 00:00:00 2001
From: Jan Caron <j.caron@fz-juelich.de>
Date: Fri, 31 Oct 2014 12:13:53 +0100
Subject: [PATCH] Minor formatting
---
pyramid/reconstruction.py | 17 +++++++++--------
scripts/collaborations/example_joern.py | 11 +++++++----
2 files changed, 16 insertions(+), 12 deletions(-)
diff --git a/pyramid/reconstruction.py b/pyramid/reconstruction.py
index b4134e0..3d3031f 100644
--- a/pyramid/reconstruction.py
+++ b/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))
diff --git a/scripts/collaborations/example_joern.py b/scripts/collaborations/example_joern.py
index c96ebfa..c725f28 100644
--- a/scripts/collaborations/example_joern.py
+++ b/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)
--
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