def test_cmscr1d_img_zero_Dirichlet(self):
print("Running test 'test_cmscr1d_img_custom_mesh'")
# Create zero image.
img = np.zeros((10, 25))
v, k, res, fun, converged = cmscr1d_img(img, 1.0, 1.0, 1.0, 1.0, 1.0,
'mesh', bc='zero')
np.testing.assert_allclose(v.shape, img.shape)
np.testing.assert_allclose(v, np.zeros_like(v))
np.testing.assert_allclose(k.shape, img.shape)
np.testing.assert_allclose(k, np.zeros_like(k))
def test_cmscr1d_img_default_fd(self):
print("Running test 'test_cmscr1d_img_default_fd'")
# Create zero image.
img = np.zeros((10, 25))
v, k, res, fun, converged = cmscr1d_img(img,
1.0, 1.0, 1.0, 1.0, 1.0, 'fd')
np.testing.assert_allclose(v.shape, img.shape)
np.testing.assert_allclose(v, np.zeros_like(v))
np.testing.assert_allclose(k.shape, img.shape)
np.testing.assert_allclose(k, np.zeros_like(k))
def test_cmscr1d_img_custom_mesh(self):
print("Running test 'test_cmscr1d_img_custom_mesh'")
# Create zero image.
img = np.zeros((10, 25))
# Create custom mesh.
m, n = img.shape
mesh = RectangleMesh(Point(0, 0), Point(0.1, 1), m - 1, n - 1)
v, k, res, fun, converged = cmscr1d_img(img, 1.0, 1.0, 1.0, 1.0, 1.0,
'mesh', mesh=mesh)
np.testing.assert_allclose(v.shape, img.shape)
np.testing.assert_allclose(v, np.zeros_like(v))
np.testing.assert_allclose(k.shape, img.shape)
np.testing.assert_allclose(k, np.zeros_like(k))
cax = ax.imshow(err, cmap=cm.viridis)
ax.set_title('Error')
fig.colorbar(cax, orientation='vertical')
# Set regularisation parameters.
alpha0 = 1e-1
alpha1 = 1e-2
alpha2 = 1e3
alpha3 = 1e3
beta = 1e-10
# Compute velocities.
# vel = of1d(f, alpha0, alpha1)
# vel = cm1d(f, alpha0, alpha1)
# vel, k = cms1d(f, alpha0, alpha1, alpha2, alpha3)
vel, k, res, fun, converged = cmscr1d_img(f, alpha0, alpha1, alpha2, alpha3,
beta, 'mesh')
# vel, k, res, fun converged = cmscr1dnewton(f, alpha0, alpha1, alpha2,
# alpha3, beta)
# Plot and save figures.
saveimage(os.path.join(resultpath, 'artificial'), name, f)
savevelocity(os.path.join(resultpath, 'artificial'), name, f, vel)
savevelocity(os.path.join(resultpath, 'artificial'), 'artificial-gt', f, v)
savesource(os.path.join(resultpath, 'artificial'), name, k)
saveerror(os.path.join(resultpath, 'artificial'), name,
np.abs(v - vel[:-1, :]))
# Compute differences.
# Compute and save strain rate.
m, n = f.shape
res_cmscr1d = [collections.defaultdict(dict) for x in range(len(prod_cmscr1d))]
fun_cmscr1d = [collections.defaultdict(dict) for x in range(len(prod_cmscr1d))]
converged_cmscr1d = [collections.defaultdict(dict)
for x in range(len(prod_cmscr1d))]
count = 1
for idx, p in enumerate(prod_cmscr1d):
# Run through datasets.
for gen in genotypes:
for dat in datasets[gen]:
print("{0}/{1}".format(count, num_datasets * len(prod_cmscr1d)))
vel_cmscr1d[idx][gen][dat], \
k_cmscr1d[idx][gen][dat], \
res_cmscr1d[idx][gen][dat], \
fun_cmscr1d[idx][gen][dat], \
converged_cmscr1d[idx][gen][dat] = \
cmscr1d_img(imgp[gen][dat],
p[0], p[1], p[2], p[3], p[4], 'mesh')
count += 1
# Store results.
with open(os.path.join(resultpath, 'pkl', 'vel_cmscr1d.pkl'), 'wb') as f:
pickle.dump(vel_cmscr1d, f, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(resultpath, 'pkl', 'k_cmscr1d.pkl'), 'wb') as f:
pickle.dump(k_cmscr1d, f, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(resultpath, 'pkl', 'res_cmscr1d.pkl'), 'wb') as f:
pickle.dump(res_cmscr1d, f, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(resultpath, 'pkl', 'fun_cmscr1d.pkl'), 'wb') as f:
pickle.dump(fun_cmscr1d, f, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(resultpath, 'pkl', 'converged_cmscr1d.pkl'), 'wb') as f:
pickle.dump(converged_cmscr1d, f, pickle.HIGHEST_PROTOCOL)
# Clear memory.