def test_of1d_img_default_fd(self):
# Create zero image.
img = np.zeros((10, 25))
v, res, fun = of1d_img(img, 1, 1, 'fd')
np.testing.assert_allclose(v.shape, img.shape)
np.testing.assert_allclose(v, np.zeros_like(v))
w = 0.1
lambdap = 1 / (4 * np.pi)
tau = 1.0
c0 = 0.0
# Create mesh and function spaces.
m, n = 30, 100
mesh = UnitSquareMesh(m - 1, n - 1)
V = dh.create_function_space(mesh, 'default')
W = dh.create_function_space(mesh, 'periodic')
# Run experiments with non-decaying data.
f = ConstantData().create(m, n, w, lambdap, tau)
datastr = ConstantData().string()
v, res, fun = of1d_img(f, alpha0, alpha1, 'mesh')
saveresults(resultpath, 'analytic_example_const_of1d_l2h1_img', 'l2h1', f, v)
v, res, fun = of1d_img_pb(f, alpha0, alpha1, 'mesh')
saveresults(resultpath, 'analytic_example_const_of1d_l2h1_img_pb', 'l2h1', f,
v)
v, res, fun = cm1d_img(f, alpha0, alpha1, 'mesh')
saveresults(resultpath, 'analytic_example_const_cm1d_l2h1_img', 'l2h1', f, v)
v, res, fun = cm1d_img_pb(f, alpha0, alpha1, 'mesh')
saveresults(resultpath, 'analytic_example_const_cm1d_l2h1_img_pb', 'l2h1', f,
v)
v, k, res, fun = cms1d_img(f, alpha0, alpha1, alpha2, alpha3, 'mesh')
saveresults(resultpath, 'analytic_example_const_cms1d_l2h1h1_img', 'l2h1h1', f,
# Compute velocity and source for all parameter pairs.
print("Running of1d on {0} datasets ".format(num_datasets) +
"and {0} parameter combinations.".format(len(prod_of1d)))
vel_of1d = [collections.defaultdict(dict) for x in range(len(prod_of1d))]
res_of1d = [collections.defaultdict(dict) for x in range(len(prod_of1d))]
fun_of1d = [collections.defaultdict(dict) for x in range(len(prod_of1d))]
count = 1
for idx, p in enumerate(prod_of1d):
# Run through datasets.
for gen in genotypes:
for dat in datasets[gen]:
print("{0}/{1}".format(count, num_datasets * len(prod_of1d)))
vel_of1d[idx][gen][dat], \
res_of1d[idx][gen][dat], \
fun_of1d[idx][gen][dat] = of1d_img(imgp[gen][dat],
p[0], p[1], 'mesh')
count += 1
# Store results.
with open(os.path.join(resultpath, 'pkl', 'vel_of1d.pkl'), 'wb') as f:
pickle.dump(vel_of1d, f, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(resultpath, 'pkl', 'res_of1d.pkl'), 'wb') as f:
pickle.dump(res_of1d, f, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(resultpath, 'pkl', 'fun_of1d.pkl'), 'wb') as f:
pickle.dump(fun_of1d, f, pickle.HIGHEST_PROTOCOL)
# Clear memory.
del vel_of1d
# Compute velocity and source for all parameter pairs.
print("Running cms1dl2 on {0} datasets ".format(num_datasets) +
# Load kymograph.
datfolder = os.path.join(datapath, os.path.join(gen, dat))
img, name = load_kymo(datfolder, dat)
# Prepare image.
imgp = prepareimage(img)
# Figure 5: different models.
# Set regularisation parameters for of1d.
alpha0 = 5e-3
alpha1 = 5e-3
# Compute velocity.
vel, res, fun = of1d_img(imgp, alpha0, alpha1, 'mesh')
# Plot and save figures.
path = os.path.join(*[resultpath, 'of1d', gen, dat])
if not os.path.exists(resultpath):
os.makedirs(resultpath)
ph.saveimage(path, name, imgp)
ph.savevelocity(path, name, img, vel)
# Set regularisation parameters for cms1dl2.
alpha0 = 5e-3
alpha1 = 5e-3
gamma = 1e-1
# Compute velocity and source.