def evaluate(self, v):
"""
Evaluate coefficients in standard 2D coordinate basis from those in polar Fourier basis
:param v: A coefficient vector (or an array of coefficient vectors)
in polar Fourier basis to be evaluated. The last dimension must equal to
`self.count`.
:return x: Image instance in standard 2D coordinate basis with
resolution of `self.sz`.
"""
if self.dtype != real_type(v.dtype):
msg = (f"Input data type, {v.dtype}, is not consistent with"
f" type defined in the class {self.dtype}.")
logger.error(msg)
raise TypeError(msg)
v = v.reshape(-1, self.ntheta, self.nrad)
nimgs = v.shape[0]
half_size = self.ntheta // 2
v = v[:, :half_size, :] + v[:, half_size:, :].conj()
v = v.reshape(nimgs, self.nrad * half_size)
x = anufft(v, self.freqs, self.sz, real=True)
return Image(x)
def backproject(self, rot_matrices):
"""
Backproject images along rotation
:param im: An Image (stack) to backproject.
:param rot_matrices: An n-by-3-by-3 array of rotation matrices \
corresponding to viewing directions.
:return: Volume instance corresonding to the backprojected images.
"""
L = self.res
ensure(
self.n_images == rot_matrices.shape[0],
"Number of rotation matrices must match the number of images",
)
# TODO: rotated_grids might as well give us correctly shaped array in the first place
pts_rot = aspire.volume.rotated_grids(L, rot_matrices)
pts_rot = np.moveaxis(pts_rot, 1, 2)
pts_rot = m_reshape(pts_rot, (3, -1))
im_f = xp.asnumpy(fft.centered_fft2(xp.asarray(self.data))) / (L**2)
if L % 2 == 0:
im_f[:, 0, :] = 0
im_f[:, :, 0] = 0
im_f = im_f.flatten()
vol = anufft(im_f, pts_rot, (L, L, L), real=True) / L
return aspire.volume.Volume(vol)
def compute_kernel(self):
_2L = 2 * self.L
kernel = np.zeros((_2L, _2L, _2L), dtype=self.dtype)
sq_filters_f = self.src.eval_filter_grid(self.L, power=2)
for i in range(0, self.n, self.batch_size):
_range = np.arange(i, min(self.n, i + self.batch_size), dtype=int)
pts_rot = rotated_grids(self.L, self.src.rots[_range, :, :])
weights = sq_filters_f[:, :, _range]
weights *= self.src.amplitudes[_range]**2
if self.L % 2 == 0:
weights[0, :, :] = 0
weights[:, 0, :] = 0
pts_rot = m_reshape(pts_rot, (3, -1))
weights = m_flatten(weights)
kernel += (1 / (self.n * self.L**4) *
anufft(weights, pts_rot, (_2L, _2L, _2L), real=True))
# Ensure symmetric kernel
kernel[0, :, :] = 0
kernel[:, 0, :] = 0
kernel[:, :, 0] = 0
logger.info("Computing non-centered Fourier Transform")
kernel = mdim_ifftshift(kernel, range(0, 3))
kernel_f = fft2(kernel, axes=(0, 1, 2))
kernel_f = np.real(kernel_f)
return FourierKernel(kernel_f, centered=False)
def compute_kernel(self):
# TODO: Most of this stuff is duplicated in MeanEstimator - move up the hierarchy?
n = self.n
L = self.L
_2L = 2 * self.L
kernel = np.zeros((_2L, _2L, _2L, _2L, _2L, _2L), dtype=self.dtype)
sq_filters_f = self.src.eval_filter_grid(self.L, power=2)
for i in tqdm(range(0, n, self.batch_size)):
_range = np.arange(i, min(n, i + self.batch_size))
pts_rot = rotated_grids(L, self.src.rots[_range, :, :])
weights = sq_filters_f[:, :, _range]
weights *= self.src.amplitudes[_range]**2
if L % 2 == 0:
weights[0, :, :] = 0
weights[:, 0, :] = 0
# TODO: This is where this differs from MeanEstimator
pts_rot = np.moveaxis(pts_rot, -1, 0).reshape(-1, 3, L**2)
weights = weights.T.reshape((-1, L**2))
batch_n = weights.shape[0]
factors = np.zeros((batch_n, _2L, _2L, _2L), dtype=self.dtype)
for j in range(batch_n):
factors[j] = anufft(weights[j],
pts_rot[j], (_2L, _2L, _2L),
real=True)
factors = Volume(factors).to_vec()
kernel += vecmat_to_volmat(factors.T @ factors) / (n * L**8)
# Ensure symmetric kernel
kernel[0, :, :, :, :, :] = 0
kernel[:, 0, :, :, :, :] = 0
kernel[:, :, 0, :, :, :] = 0
kernel[:, :, :, 0, :, :] = 0
kernel[:, :, :, :, 0, :] = 0
kernel[:, :, :, :, :, 0] = 0
logger.info("Computing non-centered Fourier Transform")
kernel = mdim_ifftshift(kernel, range(0, 6))
kernel_f = fftn(kernel)
# Kernel is always symmetric in spatial domain and therefore real in Fourier
kernel_f = np.real(kernel_f)
return FourierKernel(kernel_f, centered=False)
def evaluate(self, v):
"""
Evaluate coefficients in standard 2D coordinate basis from those in FB basis
:param v: A coefficient vector (or an array of coefficient vectors)
in FB basis to be evaluated. The last dimension must equal `self.count`.
:return x: The evaluation of the coefficient vector(s) `x` in standard 2D
coordinate basis. This is Image instance with resolution of `self.sz`
and the first dimension correspond to remaining dimension of `v`.
"""
if v.dtype != self.dtype:
logger.debug(
f"{self.__class__.__name__}::evaluate"
f" Inconsistent dtypes v: {v.dtype} self: {self.dtype}")
sz_roll = v.shape[:-1]
v = v.reshape(-1, self.count)
# number of 2D image samples
n_data = v.shape[0]
# get information on polar grids from precomputed data
n_theta = np.size(self._precomp["freqs"], 2)
n_r = np.size(self._precomp["freqs"], 1)
# go through each basis function and find corresponding coefficient
pf = np.zeros((n_data, 2 * n_theta, n_r),
dtype=complex_type(self.dtype))
mask = self._indices["ells"] == 0
ind = 0
idx = ind + np.arange(self.k_max[0], dtype=int)
# include the normalization factor of angular part into radial part
radial_norm = self._precomp["radial"] / np.expand_dims(
self.angular_norms, 1)
pf[:, 0, :] = v[:, mask] @ radial_norm[idx]
ind = ind + np.size(idx)
ind_pos = ind
for ell in range(1, self.ell_max + 1):
idx = ind + np.arange(self.k_max[ell], dtype=int)
idx_pos = ind_pos + np.arange(self.k_max[ell], dtype=int)
idx_neg = idx_pos + self.k_max[ell]
v_ell = (v[:, idx_pos] - 1j * v[:, idx_neg]) / 2.0
if np.mod(ell, 2) == 1:
v_ell = 1j * v_ell
pf_ell = v_ell @ radial_norm[idx]
pf[:, ell, :] = pf_ell
if np.mod(ell, 2) == 0:
pf[:, 2 * n_theta - ell, :] = pf_ell.conjugate()
else:
pf[:, 2 * n_theta - ell, :] = -pf_ell.conjugate()
ind = ind + np.size(idx)
ind_pos = ind_pos + 2 * self.k_max[ell]
# 1D inverse FFT in the degree of polar angle
pf = 2 * pi * xp.asnumpy(fft.ifft(xp.asarray(pf), axis=1))
# Only need "positive" frequencies.
hsize = int(np.size(pf, 1) / 2)
pf = pf[:, 0:hsize, :]
for i_r in range(0, n_r):
pf[..., i_r] = pf[..., i_r] * (self._precomp["gl_weights"][i_r] *
self._precomp["gl_nodes"][i_r])
pf = np.reshape(pf, (n_data, n_r * n_theta))
# perform inverse non-uniformly FFT transform back to 2D coordinate basis
freqs = m_reshape(self._precomp["freqs"], (2, n_r * n_theta))
x = 2 * anufft(pf, 2 * pi * freqs, self.sz, real=True)
# Return X as Image instance with the last two dimensions as *self.sz
x = x.reshape((*sz_roll, *self.sz))
return Image(x)
def evaluate(self, v):
"""
Evaluate coefficients in standard 3D coordinate basis from those in 3D FB basis
:param v: A coefficient vector (or an array of coefficient vectors) in FB basis
to be evaluated. The last dimension must equal `self.count`.
:return x: The evaluation of the coefficient vector(s) `x` in standard 3D
coordinate basis. This is an array whose last three dimensions equal
`self.sz` and the remaining dimensions correspond to `v`.
"""
# roll dimensions of v
sz_roll = v.shape[:-1]
v = v.reshape((-1, self.count))
# get information on polar grids from precomputed data
n_theta = np.size(self._precomp["ang_theta_wtd"], 0)
n_phi = np.size(self._precomp["ang_phi_wtd_even"][0], 0)
n_r = np.size(self._precomp["radial_wtd"], 0)
# number of 3D image samples
n_data = v.shape[0]
u_even = np.zeros(
(
n_r,
int(2 * self.ell_max + 1),
n_data,
int(np.floor(self.ell_max / 2) + 1),
),
dtype=v.dtype,
)
u_odd = np.zeros(
(n_r, int(2 * self.ell_max + 1), n_data,
int(np.ceil(self.ell_max / 2))),
dtype=v.dtype,
)
# go through each basis function and find corresponding coefficient
# evaluate the radial parts
for ell in range(0, self.ell_max + 1):
k_max_ell = self.k_max[ell]
radial_wtd = self._precomp["radial_wtd"][:, 0:k_max_ell, ell]
ind = self._indices["ells"] == ell
v_ell = m_reshape(v[:, ind].T, (k_max_ell, (2 * ell + 1) * n_data))
v_ell = radial_wtd @ v_ell
v_ell = m_reshape(v_ell, (n_r, 2 * ell + 1, n_data))
if np.mod(ell, 2) == 0:
u_even[:,
int(self.ell_max - ell):int(self.ell_max + ell + 1), :,
int(ell / 2), ] = v_ell
else:
u_odd[:,
int(self.ell_max - ell):int(self.ell_max + ell + 1), :,
int((ell - 1) / 2), ] = v_ell
u_even = np.transpose(u_even, (3, 0, 1, 2))
u_odd = np.transpose(u_odd, (3, 0, 1, 2))
w_even = np.zeros((n_phi, n_r, n_data, 2 * self.ell_max + 1),
dtype=v.dtype)
w_odd = np.zeros((n_phi, n_r, n_data, 2 * self.ell_max + 1),
dtype=v.dtype)
# evaluate the phi parts
for m in range(0, self.ell_max + 1):
ang_phi_wtd_m_even = self._precomp["ang_phi_wtd_even"][m]
ang_phi_wtd_m_odd = self._precomp["ang_phi_wtd_odd"][m]
n_even_ell = np.size(ang_phi_wtd_m_even, 1)
n_odd_ell = np.size(ang_phi_wtd_m_odd, 1)
if m == 0:
sgns = (1, )
else:
sgns = (1, -1)
for sgn in sgns:
end = np.size(u_even, 0)
u_m_even = u_even[end - n_even_ell:end, :,
self.ell_max + sgn * m, :]
end = np.size(u_odd, 0)
u_m_odd = u_odd[end - n_odd_ell:end, :,
self.ell_max + sgn * m, :]
u_m_even = m_reshape(u_m_even, (n_even_ell, n_r * n_data))
u_m_odd = m_reshape(u_m_odd, (n_odd_ell, n_r * n_data))
w_m_even = ang_phi_wtd_m_even @ u_m_even
w_m_odd = ang_phi_wtd_m_odd @ u_m_odd
w_m_even = m_reshape(w_m_even, (n_phi, n_r, n_data))
w_m_odd = m_reshape(w_m_odd, (n_phi, n_r, n_data))
w_even[:, :, :, self.ell_max + sgn * m] = w_m_even
w_odd[:, :, :, self.ell_max + sgn * m] = w_m_odd
w_even = np.transpose(w_even, (3, 0, 1, 2))
w_odd = np.transpose(w_odd, (3, 0, 1, 2))
u_even = w_even
u_odd = w_odd
u_even = m_reshape(u_even,
(2 * self.ell_max + 1, n_phi * n_r * n_data))
u_odd = m_reshape(u_odd, (2 * self.ell_max + 1, n_phi * n_r * n_data))
# evaluate the theta parts
w_even = self._precomp["ang_theta_wtd"] @ u_even
w_odd = self._precomp["ang_theta_wtd"] @ u_odd
pf = w_even + 1j * w_odd
pf = m_reshape(pf, (n_theta * n_phi * n_r, n_data))
pf = np.moveaxis(pf, 0, -1)
# perform inverse non-uniformly FFT transformation back to 3D rectangular coordinates
freqs = m_reshape(self._precomp["fourier_pts"],
(3, n_r * n_theta * n_phi))
x = anufft(pf, freqs, self.sz, real=True)
# Roll, return the x with the last three dimensions as self.sz
# Higher dimensions should be like v.
x = x.reshape((*sz_roll, *self.sz))
return x
