pts = mesh_obj['node']
tri = mesh_obj['element']
# report the status of the 2D mesh
quality.stats(pts, tri)
""" 1. FEM forward simulations """
# setup EIT scan conditions
el_dist, step = 7, 1
ex_mat = eit_scan_lines(16, el_dist)
# calculate simulated data
fwd = Forward(mesh_obj, el_pos)
# in python, index start from 0
ex_line = ex_mat[1].ravel()
# change alpha
anomaly = [{'x': 0.40, 'y': 0.40, 'z': 0.0, 'd': 0.30, 'perm': 100.0}]
mesh_new = mesh.set_perm(mesh_obj, anomaly=anomaly, background=1.0)
tri_perm = mesh_new['perm']
node_perm = sim2pts(pts, tri, np.real(tri_perm))
# solving once using fem
f, _ = fwd.solve(ex_line, perm=tri_perm)
f = np.real(f)
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(pts, tri, vertex_color=f, alpha=1.0)
im = ax.tripcolor(no2xy[:, 0], no2xy[:, 1], el2no,
np.real(alpha), shading='flat')
fig.colorbar(im)
ax.axis('tight')
ax.set_title(r'$\Delta$ Permitivity')
""" 2. calculate simulated data using stack ex_mat """
el_dist, step = 7, 1
n_el = len(el_pos)
ex_mat1 = eit_scan_lines(n_el, el_dist)
ex_mat2 = eit_scan_lines(n_el, 1)
ex_mat = np.vstack([ex_mat1, ex_mat2])
# forward solver
fwd = Forward(ms, el_pos)
f0 = fwd.solve(ex_mat, step, perm=ms['alpha'])
f1 = fwd.solve(ex_mat, step, perm=ms1['alpha'])
""" 3. solving using dynamic EIT """
# number of stimulation lines/patterns
eit = jac.JAC(ms, el_pos, ex_mat=ex_mat, step=step, parser='std')
eit.setup(p=0.40, lamb=1e-3, method='kotre')
ds = eit.solve(f1.v, f0.v)
""" 4. plot """
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(no2xy[:, 0], no2xy[:, 1], el2no, np.real(ds),
shading='flat', alpha=0.90, cmap=plt.cm.viridis)
fig.colorbar(im)
ax.axis('tight')
ax.set_title(r'$\Delta$ Permitivity Reconstructed')
ex_mat = eit_scan_lines(16, el_dist)
# calculate simulated data
fwd = Forward(ms, el_pos)
# in python, index start from 0
ex_line = ex_mat[2].ravel()
# change alpha
anomaly = [{'x': 0.40, 'y': 0.40, 'z': 0.0, 'd': 0.30, 'alpha': 100.0}]
ms_test = mesh.set_alpha(ms, anomaly=anomaly, background=1.0)
tri_perm = ms_test['alpha']
node_perm = pdeprtni(no2xy, el2no, np.real(tri_perm))
# solving once using fem
# f, _ = fwd.solve_once(ex_line, tri_perm)
# f = np.real(f)
# calculate simulated data
f0 = fwd.solve(ex_mat, step=step, perm=ms['alpha'])
f1 = fwd.solve(ex_mat, step=step, perm=ms_test['alpha'])
""" 3. JAC solver """
# number of stimulation lines/patterns
eit = jac.JAC(ms, el_pos, ex_mat=ex_mat, step=step, perm=1., parser='std')
eit.setup(p=0.50, lamb=1e-4, method='kotre')
ds = eit.solve(f1.v, f0.v)
node_ds = pdeprtni(no2xy, el2no, np.real(ds))
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(no2xy, el2no, vertex_color=node_ds, alpha=1.0)
}, {
'x': 0,
'y': -0.5,
'd': 0.1,
'alpha': 0.1
}]
ms1 = mesh.set_alpha(ms, anomaly=anomaly, background=1.0)
alpha = np.real(ms1['alpha'] - ms0['alpha'])
""" ax1. FEM forward simulations """
# setup EIT scan conditions
el_dist, step = 1, 1
ex_mat = eit_scan_lines(16, el_dist)
# calculate simulated data
fwd = Forward(ms, el_pos)
f0 = fwd.solve(ex_mat, step=step, perm=ms0['alpha'])
f1 = fwd.solve(ex_mat, step=step, perm=ms1['alpha'])
""" ax2. BP """
eit = bp.BP(ms, el_pos, ex_mat=ex_mat, step=1, parser='std')
ds = eit.solve(f1.v, f0.v, normalize=True)
ds_bp = ds
""" ax3. JAC """
eit = jac.JAC(ms, el_pos, ex_mat=ex_mat, step=step, perm=1., parser='std')
eit.setup(p=0.2, lamb=0.001, method='kotre')
# parameter tuning is needed for better display
ds = eit.solve(f1.v, f0.v)
ds_jac = pdeprtni(no2xy, el2no, ds)
""" ax4. GREIT """
eit = greit.GREIT(ms, el_pos, ex_mat=ex_mat, step=step, parser='std')
ds = eit.solve(f1.v, f0.v)
x, y, ds_greit = eit.mask_value(ds, mask_value=np.NAN)
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(no2xy[:, 0],
no2xy[:, 1],
el2no,
np.real(alpha),
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(ms, el_pos)
f1 = fwd.solve(ex_mat, step, perm=ms1['alpha'], parser='std')
""" 3. solve using gaussian-newton """
# number of stimulation lines/patterns
eit = jac.JAC(ms, el_pos, ex_mat, step, perm=1.0, parser='std')
eit.setup(p=0.25, lamb=1.0, method='lm')
ds = eit.gn(f1.v, lamb_decay=0.1, lamb_min=1e-4, maxiter=20, verbose=True)
# plot
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(no2xy[:, 0],
no2xy[:, 1],
el2no,
np.real(ds),
shading='flat',
alpha=1.0,
cmap=plt.cm.viridis)
x, y = pts[:, 0], pts[:, 1]
quality.stats(pts, tri)
# change permittivity
anomaly = [{"x": 0.40, "y": 0.50, "d": 0.20, "perm": 100.0}]
mesh_new = mesh.set_perm(mesh_obj, anomaly=anomaly, background=1.0)
perm = mesh_new["perm"]
""" 1. FEM forward simulations """
# setup EIT scan conditions
ex_dist, step = 7, 1
ex_mat = eit_scan_lines(16, ex_dist)
ex_line = ex_mat[0].ravel()
# calculate simulated data using FEM
fwd = Forward(mesh_obj, el_pos)
f, _ = fwd.solve(ex_line, perm=perm)
f = np.real(f)
""" 2. plot """
fig = plt.figure()
ax1 = fig.add_subplot(111)
# draw equi-potential lines
vf = np.linspace(min(f), max(f), 32)
ax1.tricontour(x, y, tri, f, vf, cmap=plt.cm.viridis)
# draw mesh structure
ax1.tripcolor(
x,
y,
tri,
np.real(perm),
edgecolors="k",
shading="flat",