def mask_time(D,shot,dt,dx,vel):
# setup
# dt = 4e-3
# dx = 10
# vel_ocean = 1490
nt,nr = D.size
shot = shot - 1 # python stuffs start with zero
x1 = np.arange(shot)
x2 = np.arange(shot,nr)
t1 = (shot-x1)*dx/(vel*dt)
t2 = (x2-shot)*dx/(vel*dt)
t_vec = np.concatenate((t1,t2),axis=None) + 140
# mask for sediment reflection
t3 = np.sqrt(830**2 + ((shot-x1)*dx)**2/((vel*dt)**2))
t4 = np.sqrt(830**2 + ((x2-shot)*dx)**2/((vel*dt)**2))
t_vec2 = np.concatenate((t3,t4),axis=None)
mask_t2 = np.zeros((nt,nr))
for i in np.arange(nr):
mask_t2[int(t_vec[i]):int(t_vec2[i]),i] = 1
Sop_t = pylops.Smoothing2D(nsmooth=[11,3], dims=[nt, nr])
mask_2 = pylops.Diagonal(Sop_t*mask_t2.flatten())
D_mask = (mask_2*D.flatten()).reshape(nt,nr)
return D_mask,mask_2
def mask_fre(D,shot,dt,dx,vel):
"""
Under construction, need to pratice on different data
with different shot locations
"""
kn=1/(2*dx)
dk=2*kn/nr
mask_fre_loc = np.zeros((nt,nr))
v = np.zeros(nt)
k1 = np.zeros(nt)
k2 = np.zeros(nt)
for i in np.arange(350,540):
# v[i] = -vn + i*dv
k1[i] = ((-vn + i*dv)/(vel)) /dk +shot
k2[i] = ((-vn + i*dv)/(-1*vel)) /dk +shot
mask_fre_loc[i,int(k1[i]):int(k2[i])] = 1
for i in np.arange(540,730):
k1[i] = ((-vn + i*dv)/(vel) ) /dk +shot
k2[i] = ((-vn + i*dv)/(-1*vel) ) /dk +shot
mask_fre_loc[i,int(k2[i]):int(k1[i])] = 1
mask_fre_loc[530:550,140:160] = 1
mask_fre_iff = np.fft.ifftshift(mask_fre_loc.reshape(nt,nr))
mask_fre = pylops.Diagonal(mask_fre_iff)
fre_dec = np.fft.fftshift(np.fft.fft2(D_dec_mask))
return mask_fre
def from_data_mapper_and_regularization(
cls,
visibilities: vis.Visibilities,
noise_map: vis.VisibilitiesNoiseMap,
transformer: trans.TransformerNUFFT,
mapper: typing.Union[mappers.MapperRectangular, mappers.MapperVoronoi],
regularization: reg.Regularization,
settings=SettingsInversion(),
):
regularization_matrix = regularization.regularization_matrix_from_mapper(
mapper=mapper
)
Aop = pylops.MatrixMult(sparse.bsr_matrix(mapper.mapping_matrix))
Fop = transformer
Op = Fop * Aop
curvature_matrix_approx = np.multiply(
np.sum(noise_map.weight_list_ordered_1d),
mapper.mapping_matrix.T @ mapper.mapping_matrix,
)
preconditioner_matrix = np.add(curvature_matrix_approx, regularization_matrix)
preconditioner_inverse_matrix = np.linalg.inv(preconditioner_matrix)
MOp = pylops.MatrixMult(sparse.bsr_matrix(preconditioner_inverse_matrix))
log_det_curvature_reg_matrix_term = 2.0 * np.sum(
np.log(np.diag(np.linalg.cholesky(preconditioner_matrix)))
)
reconstruction = pylops.NormalEquationsInversion(
Op=Op,
Regs=None,
epsNRs=[1.0],
data=visibilities.ordered_1d,
Weight=pylops.Diagonal(diag=noise_map.weight_list_ordered_1d),
NRegs=[pylops.MatrixMult(sparse.bsr_matrix(regularization_matrix))],
M=MOp,
tol=settings.tolerance,
atol=settings.tolerance,
**dict(maxiter=settings.maxiter),
)
return InversionInterferometerLinearOperator(
visibilities=visibilities,
noise_map=noise_map,
transformer=transformer,
mapper=mapper,
regularization=regularization,
regularization_matrix=regularization_matrix,
reconstruction=np.real(reconstruction),
settings=settings,
log_det_curvature_reg_matrix_term=log_det_curvature_reg_matrix_term,
)
def deriv(D, dx):
fre = np.fft.fft2(D)
kn = 1 / (2 * dx)
ks = np.fft.fftfreq(nr, d=dx)
coeff1 = 1j * 2 * np.pi * ks
coeff2 = -(2 * np.pi * ks)**2
coeff1_m = np.tile(coeff1, nt)
coeff2_m = np.tile(coeff2, nt)
D1op_hand = pylops.Diagonal(coeff1_m)
D2op_hand = pylops.Diagonal(coeff2_m)
D1_hand_fre = D1op_hand * fre.flatten()
D2_hand_fre = D2op_hand * fre.flatten()
D1_hand = F.H * D1_hand_fre
D2_hand = F.H * D2_hand_fre
return D1_hand, D2_hand, D1op_hand, D2op_hand
ax.grid(linewidth=3, color='white') ax.xaxis.set_ticklabels([]) ax.yaxis.set_ticklabels([]) ############################################################################### # From now on, we can simply use the :py:func:`pylops.utils.dottest` implementation # of the dot-test and pass the operator we would like to validate, # its size in the model and data spaces and optionally the tolerance we will be # accepting for the dot-test to be considered succesfull. Finally we need to # specify if our data or/and model vectors contain complex numbers using the # ``complexflag`` parameter. While the dot-test will return ``True`` when # succesfull and ``False`` otherwise, we can also ask to print its outcome putting the # ``verb`` parameters to ``True``. N = 10 d = np.arange(N) Dop = pylops.Diagonal(d) dottest(Dop, N, N, tol=1e-6, complexflag=0, verb=True) ############################################################################### # We move now to a more complicated operator, the :py:func:`pylops.signalprocessing.FFT` # operator. We use once again the :py:func:`pylops.utils.dottest` to verify its implementation # and since we are dealing with a transform that can be applied to both real and complex # array, we try different combinations using the ``complexflag`` input. dt = 0.005 nt = 100 nfft = 2**10 FFTop = pylops.signalprocessing.FFT(dims=(nt, ), nfft=nfft, sampling=dt) dottest(FFTop, nfft, nt, complexflag=2, verb=True)
t1 = (loc - x1) * dx / (vel * dt)
t2 = (x2 - loc) * dx / (vel * dt)
t_vec = np.concatenate((t1, t2), axis=None) + 140
# mask for sediment reflection
t3 = np.sqrt(830**2 + ((loc - x1) * dx)**2 / ((vel * dt)**2))
t4 = np.sqrt(830**2 + ((x2 - loc) * dx)**2 / ((vel * dt)**2))
t_vec2 = np.concatenate((t3, t4), axis=None)
for k in np.arange(nr):
mask_t[int(t_vec[k]):int(t_vec2[k]), k, i] = 1
Sop_t = pylops.Smoothing2D(nsmooth=[11, 3], dims=[nt, nr])
mask_t[:, :, i] = (Sop_t * mask_t[:, :, i].flatten()).reshape(nt, nr)
mask_tt = pylops.Diagonal(mask_t.flatten())
D = (mask_tt * D.flatten()).reshape(nt, nr, ns)
# mask on Fourier domain
kn = 1 / (2 * dx)
dk = 2 * kn / nr
mask_fre_loc = np.zeros((nt, nr, ns))
vel = 1450
vn = 1 / (2 * dt)
dv = 2 * vn / nt
vs = np.arange(-vn, vn, dv)
k1 = np.zeros(nt)
k2 = np.zeros(nt)
t_vec = np.concatenate((t1, t2), axis=None) + 140
# mask for sediment reflection
t3 = np.sqrt(830**2 + ((150 - x1) * dx)**2 / ((vel_ocean * dt)**2))
t4 = np.sqrt(830**2 + ((x2 - 150) * dx)**2 / ((vel_ocean * dt)**2))
t_vec2 = np.concatenate((t3, t4), axis=None)
mask_t2 = np.zeros((nt, nr))
for i in np.arange(nr):
mask_t2[int(t_vec[i]):int(t_vec2[i]), i] = 1
Sop_t = pylops.Smoothing2D(nsmooth=[11, 3], dims=[nt, nr])
mask_ts = (Sop_t * mask_t2.flatten()).reshape(nt, nr)
mask_2 = pylops.Diagonal(mask_ts.T)
D_adj_mask = (mask_2 * D_adj.flatten()).reshape(nr, nt)
D_dec_mask = (R * D_adj_mask.flatten()).reshape(idr.size, nt)
D_mask2 = mask_2 * D.flatten()
D_aft = D_mask2.reshape(nr, nt)
scail = abs((D_aft).max())
ND = (D_aft) / scail
nwins = 10
winsize = 40
overlap = (nwins * winsize - nr) / (nwins - 1)
pmax = 1e-4
npx = 100
px = np.linspace(-pmax, pmax, npx)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as pltgs
import pylops
plt.close('all')
###############################################################################
# Let's define a diagonal operator :math:`\mathbf{d}` with increasing numbers from
# ``0`` to ``N`` and a unitary model :math:`\mathbf{x}`.
N = 10
d = np.arange(N)
x = np.ones(10)
Dop = pylops.Diagonal(d)
y = Dop * x
y1 = Dop.H * x
gs = pltgs.GridSpec(1, 6)
fig = plt.figure(figsize=(7, 3))
ax = plt.subplot(gs[0, 0:3])
im = ax.imshow(Dop.matrix(), cmap='rainbow', vmin=0, vmax=N)
ax.set_title('A', size=20, fontweight='bold')
ax.set_xticks(np.arange(N - 1) + 0.5)
ax.set_yticks(np.arange(N - 1) + 0.5)
ax.grid(linewidth=3, color='white')
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax = plt.subplot(gs[0, 3])
