def constructMatexplicit(self,uHalf2D,fun,m=0):
# temp = fun(self.irfft2(uHalf2D)).T#ini kenapa harus di transpose ya?
temp = fun(self.irfft2(uHalf2D))#Harusnya ga ditranspose!!
temp = self.rfft2(temp)
#symmetrize here
temp = util.symmetrize_2D(temp)
if m != 0:
temp = util.shift_2D(temp,self.Kmatrix[:,m])
return self.constructU(temp)
def initialize_using_FBP(sim):
#Set initial condition of fourier index as the fourier index of FBP
FBP_image = iradon(cp.asnumpy(sim.measurement.sinogram),
theta=cp.asnumpy(sim.measurement.theta),
circle=True)
fbp_fHalf = sim.fourier.rfft2(cp.asarray(FBP_image, dtype=cp.float32))
fbp_fSym2D = util.symmetrize_2D(fbp_fHalf)
fbp_fSym = fbp_fSym2D.ravel(im.ORDER)
sim.Layers[-1].current_sample_sym = fbp_fSym
sim.Layers[-1].current_sample = sim.Layers[-1].current_sample_sym[
sim.fourier.basis_number_2D_ravel - 1:]
sim.Layers[-1].record_sample()
def irfft2(self,uHalf2D):
"""
Fourier transform of one dimensional signal
ut = 1D signal
num = Ut length - 1
dt = timestep
(now using cp.fft.fft) in the implementation
"""
u2D = util.extend2D(util.symmetrize_2D(uHalf2D),self.extended_basis_number)
if self._plan_ifft2 is None:
self._plan_ifft2 = cpxFFT.get_fft_plan(u2D, shape=u2D.shape)
u2D = cp.fft.ifftshift(u2D)
temp = cpxFFT.ifft2(u2D,shape=u2D.shape,plan=self._plan_ifft2,overwrite_x=True)
return temp.real*(2*self.extended_basis_number-1)
def post_analysis(input_dir):
relative_path = pathlib.Path(
"//data.triton.aalto.fi/work/emzirm1/SimulationResult/")
SimulationResult_dir = relative_path / input_dir
file = h5py.File(str(SimulationResult_dir / 'result.hdf5'), mode='r')
samples_history = file['Layers 1/samples_history'][()]
u_samples_history = file['Layers 0/samples_history'][()]
# meas_std = file['measurement/stdev'][()]
burn_start_index = np.int(0.3 * u_samples_history.shape[0])
u_samples_history = u_samples_history[burn_start_index:, :]
samples_history = samples_history[burn_start_index:, :]
N = u_samples_history.shape[0]
mean_field = util.symmetrize(cp.asarray(np.mean(samples_history, axis=0)))
# u_mean_field = util.symmetrize(cp.asarray(np.mean(u_samples_history,axis=0)))
# stdev_field = util.symmetrize(cp.asarray(np.std(samples_history,axis=0)))
n = file['fourier/basis_number'][()]
n_ext = file['fourier/extended_basis_number'][()]
t_start = file['t_start'][()]
t_end = file['t_end'][()]
target_image = file['measurement/target_image'][()]
corrupted_image = file['measurement/corrupted_image'][()]
isSinogram = 'sinogram' in file['measurement'].keys()
if isSinogram:
sinogram = file['measurement/sinogram'][()]
theta = file['measurement/theta'][()]
fbp = iradon(sinogram, theta, circle=True)
fourier = imc.FourierAnalysis_2D(n, n_ext, t_start, t_end)
sL2 = util.sigmasLancosTwo(cp.int(n))
vF = mean_field.reshape(2 * n - 1, 2 * n - 1, order=imc.ORDER).T
# if not isSinogram:
vForiginal = sL2 * util.symmetrize_2D(
fourier.fourierTransformHalf(cp.array(target_image)))
vFn = cp.asnumpy(vF)
reconstructed_image = fourier.inverseFourierLimited(vF[:, n - 1:])
if isSinogram:
reconstructed_image = cp.rot90(cp.fft.fftshift(reconstructed_image),
-1)
reconstructed_image_original = fourier.inverseFourierLimited(
vForiginal[:, n - 1:])
scalling_factor = (cp.max(reconstructed_image_original) -
cp.min(reconstructed_image_original)) / (
cp.max(reconstructed_image) -
cp.min(reconstructed_image))
u_samples_history_cp = cp.asarray(u_samples_history)
u_image = cp.zeros_like(reconstructed_image)
for i in range(N):
uF = util.symmetrize(u_samples_history_cp[i, :]).reshape(
2 * n - 1, 2 * n - 1, order=imc.ORDER).T
u_image += fourier.inverseFourierLimited(uF[:, n - 1:]) / N
if isSinogram:
u_image = cp.rot90(cp.fft.fftshift(u_image), -1)
ri_n = cp.asnumpy(reconstructed_image)
if isSinogram:
ri_or_n = fbp
else:
ri_or_n = cp.asnumpy(reconstructed_image_original)
ri_n_scalled = ri_n * cp.asnumpy(scalling_factor)
u_n = cp.asnumpy(u_image / (np.max(ri_n) - np.min(ri_n)))
ell_n = np.exp(u_n)
fig, ax = plt.subplots(ncols=3, nrows=3, figsize=(15, 15))
ax[0, 0].imshow(ri_n_scalled, cmap=plt.cm.Greys_r)
ax[0, 0].set_title('Reconstructed Image From vF--- RI')
ax[0, 1].imshow(target_image, cmap=plt.cm.Greys_r)
ax[0, 1].set_title('Target Image --- TI')
if isSinogram:
ax[0, 2].imshow(fbp, cmap=plt.cm.Greys_r)
ax[0, 2].set_title('FBP --- RIO')
else:
ax[0, 2].imshow(ri_or_n, cmap=plt.cm.Greys_r)
ax[0, 2].set_title('Reconstructed Image From vFOriginal --- RIO')
ax[1, 0].imshow(np.abs(target_image - ri_n_scalled), cmap=plt.cm.Greys_r)
ax[1, 0].set_title('Absolute error--- RI-TI')
ax[1, 1].imshow(np.abs(ri_or_n - target_image), cmap=plt.cm.Greys_r)
ax[1, 1].set_title('Absolute error--- RIO-TI')
ax[1, 2].imshow(np.abs(ri_n_scalled - ri_or_n), cmap=plt.cm.Greys_r)
ax[1, 2].set_title('Absolute error--- RI-RIO')
ax[2, 0].imshow(u_n, cmap=plt.cm.Greys_r)
ax[2, 0].set_title('Field u--- u')
ax[2, 1].imshow(ell_n, cmap=plt.cm.Greys_r)
ax[2, 1].set_title('Length Scale of v--- ell')
if isSinogram:
im = ax[2, 2].imshow(sinogram, cmap=plt.cm.Greys_r)
ax[2, 2].set_title('Measurement (Sinogram) --- CI')
else:
im = ax[2, 2].imshow(corrupted_image, cmap=plt.cm.Greys_r)
ax[2, 2].set_title('Measurement (corrupted_image) --- CI')
fig.colorbar(im, ax=ax[:, :], shrink=0.8)
fig.savefig(str(SimulationResult_dir / 'Result.pdf'), bbox_inches='tight')
for ax_i in ax.flatten():
extent = ax_i.get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
# print(ax_i.title.get_text())
fig.savefig(str(SimulationResult_dir / ax_i.title.get_text()) + '.pdf',
bbox_inches=extent.expanded(1.2, 1.2))
return file
if i + 1 % 5 == 0:
acceptancePercentage = accepted_count / (i + 1)
pcn.adapt_beta(acceptancePercentage)
#%%
mean_field = util.symmetrize(
cp.asarray(np.mean(Layers[-1].samples_history[:n_samples, :], axis=0)))
u_mean_field = util.symmetrize(
cp.asarray(np.mean(Layers[0].samples_history[:n_samples, :], axis=0)))
vF = mean_field.reshape(2 * f.basis_number - 1,
2 * f.basis_number - 1,
order=im.ORDER).T
uF = u_mean_field.reshape(2 * f.basis_number - 1,
2 * f.basis_number - 1,
order=im.ORDER).T
vForiginal = util.symmetrize_2D(
f.fourierTransformHalf(measurement.target_image))
vFwithNoise = util.symmetrize_2D(
f.fourierTransformHalf(measurement.corrupted_image))
vFn = cp.asnumpy(vF)
uFn = cp.asnumpy(uF)
vForiginaln = cp.asnumpy(vForiginal)
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(20, 20))
ax[0, 0].imshow(vFn.real, cmap=plt.cm.Greys_r)
ax[0, 1].imshow(vForiginaln.real, cmap=plt.cm.Greys_r)
ax[1, 0].imshow(vFn.imag, cmap=plt.cm.Greys_r)
ax[1, 1].imshow(vForiginaln.imag, cmap=plt.cm.Greys_r)
fig.savefig(str(simResultPath / 'FourierDomain.pdf'), bbox_inches='tight')
#%%
reconstructed_image = f.inverseFourierLimited(vF[:, f.basis_number - 1:])
reconstructed_image_length_scale = f.inverseFourierLimited(
def symmetrize_2D(self,uHalf2D):
return util.symmetrize_2D(uHalf2D)
def constructMat(self,uHalf2D,power):
U = self.constructU(util.symmetrize_2D(uHalf2D))
exp_power_U = util.expm(power*U) #sla.expm(power*cp.asnumpy(U))
# res = cp.asarray(exp_power_U,dtype=cp.complex64)*self._Umask
res = exp_power_U*self._Umask
return res