cmap=plt.cm.viridis)
ax.set_title(r'$\Delta$ Conductivities')
fig.colorbar(im)
ax.axis('equal')
fig.set_size_inches(6, 4)
# fig.savefig('demo_bp_0.png', dpi=96)
plt.show()
""" 2. FEM forward simulations """
# setup EIT scan conditions
# adjacent stimulation (el_dist=1), adjacent measures (step=1)
el_dist, step = 1, 1
ex_mat = eit_scan_lines(16, el_dist)
# calculate simulated data
fwd = Forward(mesh_obj, el_pos)
f0 = fwd.solve_eit(ex_mat, step=step, perm=mesh_obj['perm'])
f1 = fwd.solve_eit(ex_mat, step=step, perm=mesh_new['perm'])
"""
3. naive inverse solver using back-projection
"""
eit = bp.BP(mesh_obj, el_pos, ex_mat=ex_mat, step=1, parser='std')
eit.setup(weight='none')
ds = 192.0 * eit.solve(f1.v, f0.v)
# plot
fig = plt.figure()
ax1 = fig.add_subplot(111)
im = ax1.tripcolor(pts[:, 0], pts[:, 1], tri, ds, cmap=plt.cm.viridis)
ax1.set_title(r'$\Delta$ Conductivities')
ax1.axis('equal')
fig.colorbar(im)
perm = mesh_new['perm']
# show
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(pts[:, 0], pts[:, 1], tri,
np.real(perm), shading='flat', cmap=plt.cm.viridis)
fig.colorbar(im)
ax.axis('equal')
ax.set_title(r'$\Delta$ Conductivities')
plt.show()
""" 2. calculate simulated data """
el_dist, step = 1, 1
ex_mat = eit_scan_lines(n_el, el_dist)
fwd = Forward(mesh_obj, el_pos)
f1 = fwd.solve_eit(ex_mat, step, perm=mesh_new['perm'], parser='std')
""" 3. solve_eit using gaussian-newton (with regularization) """
# number of stimulation lines/patterns
eit = jac.JAC(mesh_obj, el_pos, ex_mat, step, perm=1.0, parser='std')
eit.setup(p=0.25, lamb=1.0, method='lm')
# lamb = lamb * lamb_decay
ds = eit.gn(f1.v, lamb_decay=0.1, lamb_min=1e-5, maxiter=20, verbose=True)
# plot
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(pts[:, 0], pts[:, 1], tri, np.real(ds),
shading='flat', alpha=1.0, cmap=plt.cm.viridis)
fig.colorbar(im)
ax.axis('equal')
ax.set_title('Conductivities Reconstructed')
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(pts[:, 0],
pts[:, 1],
tri,
np.real(perm),
shading="flat",
cmap=plt.cm.viridis)
fig.colorbar(im)
ax.axis("equal")
ax.set_title(r"$\Delta$ Conductivities")
plt.show()
""" 2. calculate simulated data """
el_dist, step = 1, 1
ex_mat = eit_scan_lines(n_el, el_dist)
fwd = Forward(mesh_obj, el_pos)
f1 = fwd.solve_eit(ex_mat, step, perm=mesh_new["perm"], parser="std")
""" 3. solve_eit using gaussian-newton (with regularization) """
# number of stimulation lines/patterns
eit = jac.JAC(mesh_obj, el_pos, ex_mat, step, perm=1.0, parser="std")
eit.setup(p=0.25, lamb=1.0, method="lm")
# lamb = lamb * lamb_decay
ds = eit.gn(f1.v, lamb_decay=0.1, lamb_min=1e-5, maxiter=20, verbose=True)
# plot
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(
pts[:, 0],
pts[:, 1],
tri,
np.real(ds),
shading="flat",
pts = mesh_obj["node"]
tri = mesh_obj["element"]
x, y = pts[:, 0], pts[:, 1]
""" 1. problem setup """
mesh_obj["alpha"] = np.random.rand(tri.shape[0]) * 200 + 100
anomaly = [{"x": 0.5, "y": 0.5, "d": 0.1, "perm": 1000.0}]
mesh_new = mesh.set_perm(mesh_obj, anomaly=anomaly)
""" 2. FEM simulation """
el_dist, step = 1, 1
ex_mat = eit_scan_lines(16, el_dist)
# calculate simulated data
fwd = Forward(mesh_obj, el_pos)
f0 = fwd.solve_eit(ex_mat, step=step, perm=mesh_obj["perm"])
f1 = fwd.solve_eit(ex_mat, step=step, perm=mesh_new["perm"])
""" 3. JAC solver """
# Note: if the jac and the real-problem are generated using the same mesh,
# then, data normalization in solve are not needed.
# However, when you generate jac from a known mesh, but in real-problem
# (mostly) the shape and the electrode positions are not exactly the same
# as in mesh generating the jac, then data must be normalized.
eit = jac.JAC(
mesh_obj,
el_pos,
ex_mat=ex_mat,
step=step,
perm=1.0,
parser="fmmu",