def __init__(self,
mesh=None,
el_pos=None,
parser="fmmu",
algo="jac",
p=0.20,
lamb=0.001):
""" initialize """
if algo == "jac":
solver = jac.JAC(mesh, el_pos, perm=1.0, parser=parser)
solver.setup(p=p, lamb=lamb, method="kotre")
else:
# default: 'bp'
solver = bp.BP(mesh, el_pos, parser="fmmu", step=1)
solver.setup(weight="simple")
self.solver = solver
def get_MAP(self,lamb_decay=1.,lamb_min=1e-5,maxiter=20,verbose=True,**kwargs):
map=jac.JAC(self.mesh_obj,self.el_pos,self.ex_mat,self.step,perm=1.0,parser='std')
map.setup(p=kwargs.pop('p',0.25),lamb=kwargs.pop('lamb',self.lamb),method=kwargs.pop('method','lm'))
ds=map.gn(self.obs,lamb_decay=lamb_decay,lamb_min=lamb_min,maxiter=maxiter,verbose=verbose,**kwargs)
return ds
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('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')
# plt.savefig('quasi-demo-eit.pdf')
# 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",
)
eit.setup(p=0.5, lamb=0.01, method="kotre")
ds = eit.solve(f1.v, f0.v, normalize=True)
ds_n = sim2pts(pts, tri, np.real(ds))
# plot ground truth
fig, ax = plt.subplots(figsize=(6, 4))
delta_perm = mesh_new["perm"] - mesh_obj["perm"]
im = ax.tripcolor(x, y, tri, np.real(delta_perm), shading="flat")
fig.colorbar(im)
ax.set_aspect("equal")
# plot EIT reconstruction
ax.set_aspect("equal")
ax.set_title(r"$\Delta$ Permittivity")
""" 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(mesh_obj, el_pos)
f0 = fwd.solve_eit(ex_mat, step, perm=mesh_obj["perm"])
f1 = fwd.solve_eit(ex_mat, step, perm=mesh_new["perm"])
""" 3. solving using dynamic EIT """
# number of stimulation lines/patterns
eit = jac.JAC(mesh_obj, 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, normalize=False)
""" 4. plot """
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(
pts[:, 0],
pts[:, 1],
tri,
np.real(ds),
shading="flat",
alpha=0.90,
cmap=plt.cm.viridis,
)
fig.colorbar(im)
ax.set_aspect("equal")
# 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, '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, tri_perm)
# f = np.real(f)
# calculate simulated data
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 """
# number of stimulation lines/patterns
eit = jac.JAC(mesh_obj,
el_pos,
ex_mat=ex_mat,
step=step,
perm=1.,
parser='std')
eit.setup(p=0.50, lamb=1e-3, method='kotre')
ds = eit.solve(f1.v, f0.v, normalize=False)
node_ds = sim2pts(pts, tri, np.real(ds))
# mplot.tetplot(p, t, edge_color=(0.2, 0.2, 1.0, 1.0), alpha=0.01)
mplot.tetplot(pts, tri, vertex_color=node_ds, alpha=1.0)
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)
fig.colorbar(im)
ax.axis('equal')
ax.set_title('Conductivities Reconstructed')
""" 2. FEM simulation """
el_dist, step = 8, 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, jac_normalized in JAC and 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 JAC and data must be normalized.
eit = jac.JAC(mesh_obj, el_pos, ex_mat=ex_mat, step=step,
perm=1., parser='std', jac_normalized=False)
eit.setup(p=0.5, lamb=0.01, method='kotre')
ds = eit.solve(f1.v, f0.v, normalize=True)
ds_n = sim2pts(pts, tri, np.real(ds))
# plot ground truth
fig, ax = plt.subplots(figsize=(6, 4))
delta_perm = mesh_new['perm'] - mesh_obj['perm']
im = ax.tripcolor(x, y, tri, np.real(delta_perm), shading='flat')
fig.colorbar(im)
ax.set_aspect('equal')
# plot EIT reconstruction
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.tripcolor(x, y, tri, ds_n, shading='flat')
for i, e in enumerate(el_pos):
# 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, jac_normalized in JAC and 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 JAC and data must be normalized.
eit = jac.JAC(
mesh_obj,
el_pos,
ex_mat=ex_mat,
step=step,
perm=1.0,
parser="std",
jac_normalized=False,
)
eit.setup(p=0.5, lamb=0.01, method="kotre")
ds = eit.solve(f1.v, f0.v, normalize=True)
ds_n = sim2pts(pts, tri, np.real(ds))
# plot ground truth
fig, ax = plt.subplots(figsize=(6, 4))
delta_perm = mesh_new["perm"] - mesh_obj["perm"]
im = ax.tripcolor(x, y, tri, np.real(delta_perm), shading="flat")
fig.colorbar(im)
ax.set_aspect("equal")