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Gauss-Newton solving visualization #67

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Oct 3, 2022
Merged

Gauss-Newton solving visualization #67

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1f28f47
Gauss-Newton generator implementation
ChabaneAmaury Oct 1, 2022
56f33a8
Updated the example to include both the generator and non-generator v…
ChabaneAmaury Oct 1, 2022
92ff663
Updated to pass blackd pipeline
ChabaneAmaury Oct 1, 2022
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29 changes: 24 additions & 5 deletions examples/eit_static_jac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,13 47,32 @@
eit = jac.JAC(mesh_obj, protocol_obj)
eit.setup(p=0.25, lamb=1.0, method="lm")
# lamb = lamb * lamb_decay
ds = eit.gn(v1, lamb_decay=0.1, lamb_min=1e-5, maxiter=20, verbose=True)

# plot
plt.ion()
fig, ax = plt.subplots(figsize=(9, 6))
im = ax.tripcolor(xx, yy, tri, np.real(ds), alpha=1.0, cmap="viridis")
ax.axis("equal")
ax.set_title("Conductivities Reconstructed")
fig.colorbar(im)

# Calculate then show the results
# ds = eit.gn(v1, lamb_decay=0.1, lamb_min=1e-5, maxiter=20, verbose=True)
# im = ax.tripcolor(xx, yy, tri, np.real(ds), alpha=1.0, cmap="viridis")
# fig.colorbar(im)

# Real time update version
colorbar = None
for ds in eit.gn(
v1, lamb_decay=0.1, lamb_min=1e-5, maxiter=20, verbose=True, generator=True
):
im = ax.tripcolor(xx, yy, tri, np.real(ds), alpha=1.0, cmap="viridis")
if (
colorbar is not None
): # Update the colorbar as the min and max values are changing
colorbar.remove()
colorbar = fig.colorbar(im)
fig.canvas.draw() # Update the canvas
fig.canvas.flush_events() # Flush the drawing queue

# fig.savefig('../doc/images/demo_static.png', dpi=96)
plt.show()
plt.show(
block=True
) # Very important when using the generator version, otherwise the program exits automatically
66 changes: 39 additions & 27 deletions pyeit/eit/jac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -186,6 186,7 @@ def gn(
lamb_min: float = 0.0,
method: str = "kotre",
verbose: bool = False,
generator: bool = False,
**kwargs,
) -> np.ndarray:
"""
Expand Down Expand Up @@ -248,33 249,44 @@ def gn(
# convergence test
x0_norm = np.linalg.norm(x0)

for i in range(maxiter):

# forward solver,
jac, v0 = self.fwd.compute_jac(x0)
# Residual
r0 = v - v0

# Damped Gaussian-Newton
h_mat = self._compute_h(jac, p, lamb, method)

# update
d_k = np.dot(h_mat, r0)
x0 = x0 - d_k

# convergence test
c = np.linalg.norm(d_k) / x0_norm
if c < gtol:
break

if verbose:
print("iter = %d, lamb = %f, gtol = %f" % (i, lamb, c))

# update regularization parameter
# lambda can be given in user defined decreasing lists
lamb *= lamb_decay
lamb = max(lamb, lamb_min)
return x0
def generator_gn():
nonlocal x0, lamb
for i in range(maxiter):

# forward solver,
jac, v0 = self.fwd.compute_jac(x0)
# Residual
r0 = v - v0

# Damped Gaussian-Newton
h_mat = self._compute_h(jac, p, lamb, method)

# update
d_k = np.dot(h_mat, r0)
x0 = x0 - d_k

# convergence test
c = np.linalg.norm(d_k) / x0_norm
if c < gtol:
break

if verbose:
print("iter = %d, lamb = %f, gtol = %f" % (i, lamb, c))

# update regularization parameter
# lambda can be given in user defined decreasing lists
lamb *= lamb_decay
lamb = max(lamb, lamb_min)
yield x0

real_gen = generator_gn
if not generator:
item = None
for item in real_gen():
pass
return item
else:
return real_gen()

def project(self, ds: np.ndarray) -> np.ndarray:
"""
Expand Down