def shift_data_subpixel(inputs):
''' rigid shift of X by ymax and xmax '''
''' allows subpixel shifts '''
''' ** not being used ** '''
X, ymax, xmax, pad_fft = inputs
ymax = ymax.flatten()
xmax = xmax.flatten()
if X.ndim<3:
X = X[np.newaxis,:,:]
nimg, Ly0, Lx0 = X.shape
if pad_fft:
X = fft2(X.astype('float32'), (next_fast_len(Ly0), next_fast_len(Lx0)))
else:
X = fft2(X.astype('float32'))
nimg, Ly, Lx = X.shape
Ny = fft.ifftshift(np.arange(-np.fix(Ly/2), np.ceil(Ly/2)))
Nx = fft.ifftshift(np.arange(-np.fix(Lx/2), np.ceil(Lx/2)))
[Nx,Ny] = np.meshgrid(Nx,Ny)
Nx = Nx.astype('float32') / Lx
Ny = Ny.astype('float32') / Ly
dph = Nx * np.reshape(xmax, (-1,1,1)) + Ny * np.reshape(ymax, (-1,1,1))
Y = np.real(ifft2(X * np.exp((2j * np.pi) * dph)))
# crop back to original size
if Ly0<Ly or Lx0<Lx:
Lyhalf = int(np.floor(Ly/2))
Lxhalf = int(np.floor(Lx/2))
Y = Y[np.ix_(np.arange(0,nimg,1,int),
np.arange(-np.fix(Ly0/2), np.ceil(Ly0/2),1,int) + Lyhalf,
np.arange(-np.fix(Lx0/2), np.ceil(Lx0/2),1,int) + Lxhalf)]
return Y
def __init__(self, backend, xv, yv, tsgf_or_TH):
self.backend = backend
self.xv = xv
self.yv = yv
self.backend.check_freespace(self.xv, self.yv)
self.backend.initialize_freespace()
self.h = self.backend.h
self.n = self.xv.size
self.spread_width = self.backend.spread_width
# get the T operator
self.expand_n = self.n + 2 * self.spread_width
self.n_extract = self.expand_n // 2 + self.spread_width
self.big_n = 2 * self.expand_n
self.Big_n = 4 * self.expand_n
if type(tsgf_or_TH) == np.ndarray:
self.TH = tsgf_or_TH
else:
self.truncated_spectral_gf = lambda k, L: tsgf_or_TH(
k, L, **self.backend.kernel_kwargs)
self.drange = self.xv[-1] - self.xv[0] + self.h
self.kv = np.fft.fftfreq(self.Big_n, self.h / (2 * np.pi))
self.kx, self.ky = np.meshgrid(self.kv, self.kv, indexing='ij')
self.tsgf = self.truncated_spectral_gf(np.hypot(self.kx, self.ky),
2.5 * self.drange)
w1 = np.zeros([self.Big_n, self.Big_n], dtype=float)
w1[self.big_n, self.big_n] = 1.0
w2 = ifft2(fft2(w1) * self.tsgf)
self.T = w2[self.expand_n:-self.expand_n,
self.expand_n:-self.expand_n]
self.TH = fft2(self.T)
# storage for padded array
self.big_op = np.zeros([self.big_n, self.big_n], dtype=float)
self.big_u = np.zeros([self.big_n, self.big_n], dtype=float)
# extract local ewald function from backend
self.local_func = self.backend.ewald_local_freespace
def prepare_masks(refImg0, ops):
refImg = refImg0.copy()
if ops['1Preg']:
maskSlope = ops['spatial_taper'] # slope of taper mask at the edges
else:
maskSlope = 3 * ops['smooth_sigma'] # slope of taper mask at the edges
Ly, Lx = refImg.shape
maskMul = spatial_taper(maskSlope, Ly, Lx)
if ops['1Preg']:
refImg = one_photon_preprocess(refImg[np.newaxis, :, :], ops).squeeze()
maskOffset = refImg.mean() * (1. - maskMul)
# reference image in fourier domain
if ops['pad_fft']:
cfRefImg = np.conj(
fft2(refImg, (next_fast_len(ops['Ly']), next_fast_len(ops['Lx']))))
else:
cfRefImg = np.conj(fft2(refImg))
absRef = np.absolute(cfRefImg)
cfRefImg = cfRefImg / (eps0 + absRef)
# gaussian filter in space
fhg = gaussian_fft(ops['smooth_sigma'], cfRefImg.shape[0],
cfRefImg.shape[1])
cfRefImg *= fhg
maskMul = maskMul.astype('float32')
maskOffset = maskOffset.astype('float32')
cfRefImg = cfRefImg.astype('complex64')
cfRefImg = np.reshape(cfRefImg, (1, cfRefImg.shape[0], cfRefImg.shape[1]))
return maskMul, maskOffset, cfRefImg
def phasecorr_cpu(data, refAndMasks, lcorr):
maskMul = refAndMasks[0]
maskOffset = refAndMasks[1]
cfRefImg = refAndMasks[2].squeeze()
nimg = data.shape[0]
ly, lx = cfRefImg.shape[-2:]
lyhalf = int(np.floor(ly / 2))
lxhalf = int(np.floor(lx / 2))
# shifts and corrmax
ymax = np.zeros((nimg, ), np.int32)
xmax = np.zeros((nimg, ), np.int32)
cmax = np.zeros((nimg, ), np.float32)
X = addmultiplytype(data, maskMul, maskOffset)
for t in range(X.shape[0]):
fft2(X[t], overwrite_x=True)
X = apply_dotnorm(X, cfRefImg)
for t in np.arange(nimg):
ifft2(X[t], overwrite_x=True)
x00, x01, x10, x11 = my_clip(X, lcorr)
cc = np.real(np.block([[x11, x10], [x01, x00]]))
for t in np.arange(nimg):
ymax[t], xmax[t] = np.unravel_index(np.argmax(cc[t], axis=None),
(2 * lcorr + 1, 2 * lcorr + 1))
cmax[t] = cc[t, ymax[t], xmax[t]]
ymax, xmax = ymax - lcorr, xmax - lcorr
return ymax, xmax, cmax
def phasecorr_reference(refImg0, ops):
""" computes masks and fft'ed reference image for phasecorr
Parameters
----------
refImg0 : int16
reference image
ops : dictionary
requires 'smooth_sigma'
(if ```ops['1Preg']```, need 'spatial_taper', 'spatial_hp', 'pre_smooth')
Returns
-------
maskMul : float32
mask that is multiplied to spatially taper frames
maskOffset : float32
shifts in x from cfRefImg to data for each frame
cfRefImg : complex64
reference image fft'ed and complex conjugate and multiplied by gaussian
filter in the fft domain with standard deviation 'smooth_sigma'
"""
refImg = refImg0.copy()
if '1Preg' in ops and ops['1Preg']:
maskSlope = ops['spatial_taper'] # slope of taper mask at the edges
else:
maskSlope = 3 * ops['smooth_sigma'] # slope of taper mask at the edges
Ly, Lx = refImg.shape
maskMul = utils.spatial_taper(maskSlope, Ly, Lx)
if ops['1Preg']:
refImg = utils.one_photon_preprocess(refImg[np.newaxis, :, :],
ops).squeeze()
maskOffset = refImg.mean() * (1. - maskMul)
# reference image in fourier domain
if 'pad_fft' in ops and ops['pad_fft']:
cfRefImg = np.conj(fft2(refImg,
(next_fast_len(Ly), next_fast_len(Lx))))
else:
cfRefImg = np.conj(fft2(refImg))
absRef = np.absolute(cfRefImg)
cfRefImg = cfRefImg / (1e-5 + absRef)
# gaussian filter in space
fhg = utils.gaussian_fft(ops['smooth_sigma'], cfRefImg.shape[0],
cfRefImg.shape[1])
cfRefImg *= fhg
maskMul = maskMul.astype('float32')
maskOffset = maskOffset.astype('float32')
cfRefImg = cfRefImg.astype('complex64')
cfRefImg = np.reshape(cfRefImg, (1, cfRefImg.shape[0], cfRefImg.shape[1]))
return maskMul, maskOffset, cfRefImg
def phasecorr_cpu(data, refAndMasks, lcorr, smooth_sigma_time):
""" compute phase correlation between data and reference image
Parameters
----------
data : int16
array that's frames x Ly x Lx
refAndMasks : list
maskMul, maskOffset and cfRefImg (from prepare_refAndMasks)
lcorr : int
maximum shift in pixels
smooth_sigma_time : float
how many frames to smooth in time
Returns
-------
ymax : int
shifts in y from cfRefImg to data for each frame
xmax : int
shifts in x from cfRefImg to data for each frame
cmax : float
maximum of phase correlation for each frame
"""
maskMul = refAndMasks[0]
maskOffset = refAndMasks[1]
cfRefImg = refAndMasks[2].squeeze()
nimg = data.shape[0]
ly, lx = cfRefImg.shape[-2:]
lyhalf = int(np.floor(ly / 2))
lxhalf = int(np.floor(lx / 2))
# shifts and corrmax
ymax = np.zeros((nimg, ), np.int32)
xmax = np.zeros((nimg, ), np.int32)
cmax = np.zeros((nimg, ), np.float32)
X = addmultiplytype(data, maskMul, maskOffset)
for t in range(X.shape[0]):
fft2(X[t], overwrite_x=True)
X = utils.apply_dotnorm(X, cfRefImg)
for t in np.arange(nimg):
ifft2(X[t], overwrite_x=True)
x00, x01, x10, x11 = clip(X, lcorr)
cc = np.real(np.block([[x11, x10], [x01, x00]]))
if smooth_sigma_time > 0:
cc = gaussian_filter1d(cc, smooth_sigma_time, axis=0)
for t in np.arange(nimg):
ymax[t], xmax[t] = np.unravel_index(np.argmax(cc[t], axis=None),
(2 * lcorr + 1, 2 * lcorr + 1))
cmax[t] = cc[t, ymax[t], xmax[t]]
ymax, xmax = ymax - lcorr, xmax - lcorr
return ymax, xmax, cmax
def _delta(self, phi): phi_x = mkl_fft.ifft2(phi) self.phi_sq = mkl_fft.fft2(phi_x**2) self.phi_cube = mkl_fft.fft2(phi_x**3) np.putmask(self.phi_cube, self.dealiasing_triple, 0) np.putmask(self.phi_sq, self.dealiasing_double, 0) mu = self.a*(-phi+self.phi_cube) + self.k*self.ksq*phi birth_death = - self.u*(self.phi_sq+(self.phi_shift-self.phi_target)*phi) birth_death[0, 0] += self.u*self.phi_shift*self.phi_target*self.size**2 dphidt = -self.M1*self.ksq*mu + birth_death return dphidt
def evolve_vode(self, verbose=True):
self._make_k_grid()
self._make_filters()
self.phi = np.zeros((self.n_batches, self.size, self.size))
small_batch = self.batch_size
while small_batch > 10000:
small_batch /= 10 # decrease the amount of time integration at each step
r = ode(self._delta).set_integrator('vode',
atol=1e-8,
nsteps=small_batch)
r.set_initial_value(self.phi_initial, 0)
n = 0
phi = self._make_real(fft2(self.phi_initial))
for i in range(int((self.T / self.dt) / small_batch)):
if r.successful():
if i % int(self.batch_size / small_batch) == 0:
phi_complex = self._make_complex(phi)
self.phi[n] = np.real(ifft2(phi_complex))
if verbose:
print('iteration: {} mean: {}'.format(
n, self._mean_bd(self.phi[n])))
n += 1
phi = r.integrate(r.t + self.dt * small_batch)
def mfft2(a, overwrite_x = False):
"""Computes matrix fft2 on a matrix of shape (..., n,n,4,4).
This is identical to np.fft2(a, axes = (-4,-3))
Parameters
----------
a : array_like
Input array (must be complex).
overwrite_x : bool
Specifies whether original array can be destroyed (for inplace transform)
Returns
-------
out : complex ndarray
Result of the transformation along the (-4,-3) axes.
"""
a = np.asarray(a, dtype = CDTYPE)
libname = DTMMConfig["fftlib"]
if libname == "mkl_fft":
return mkl_fft.fft2(a, axes = (-4,-3), overwrite_x = overwrite_x)
elif libname == "scipy":
return spfft.fft2(a, axes = (-4,-3), overwrite_x = overwrite_x)
elif libname == "numpy":
return npfft.fft2(a, axes = (-4,-3))
elif libname == "pyfftw":
return pyfftw.interfaces.scipy_fft.fft2(a, axes = (-4,-3), overwrite_x = overwrite_x)
else: #default implementation is numpy
return npfft.fft2(a, axes = (-4,-3))
def fft2(data, s=None):
if s == None:
s = (data.shape[-2], data.shape[-1])
if HAS_MKL:
data = mkl_fft.fft2(data, shape=s, axes=(-2, -1))
else:
data = fft.fft2(data, s, axes=(-2, -1))
return data
def evolve(self, verbose=True, cython=True): self.phi_initial = mkl_fft.fft2(self.phi_initial) if cython: nitr = int(self.T/self.dt) self.phi = ps.evolve_sto_ps(self.phi_initial, self.a, self.k, self.u, self.phi_shift, self.phi_target, self.epsilon, self.dt, nitr, self.n_batches, self.X) else: self.naive_evolve(verbose)
def _fft2(a, overwrite_x = False):
libname = CDDMConfig["fftlib"]
if libname == "mkl_fft":
return mkl_fft.fft2(a, overwrite_x = overwrite_x)
elif libname == "scipy":
return spfft.fft2(a, overwrite_x = overwrite_x)
elif libname == "numpy":
return np.fft.fft2(a) #force real in case input is complex
else:#default implementation is numpy fft2
return np.fft.fft2(a)
def qsCat(ti,dst,path,template,n,k): dist=numpy.zeros(shape=template.shape); dist[math.floor(dist.shape[0]/2),math.floor(dist.shape[1]/2)]=1; dist=ndimage.morphology.distance_transform_edt(1-dist); unValue=numpy.unique(ti.flat); fftim = numpy.stack([fft.fft2(-1*(ti==x)) for x in unValue],axis=2); adjustedTi=numpy.ones(ti.shape)*numpy.nan; for x in range(unValue.size): adjustedTi[ti==unValue[x]]=x; return unValue[runsim((fftim ,ti.shape, dist, n, k), adjustedTi, dst, path, template, qsSampleCat).astype(int)];
def gaussian_fft(sig, Ly, Lx):
''' gaussian filter in the fft domain with std sig and size Ly,Lx '''
xx, yy = meshgrid_mean_centered(x=Lx, y=Ly)
hgx = np.exp(-np.square(xx / sig) / 2)
hgy = np.exp(-np.square(yy / sig) / 2)
hgg = hgy * hgx
hgg /= hgg.sum()
fhg = np.real(fft2(fft.ifftshift(hgg)))
# smoothing filter in Fourier domain
return fhg
def process(data):
''' computes movement using phase correlation '''
nt, Ly, Lx = data.shape
ly = int(np.floor(Ly * 0.4))
lx = int(np.floor(Lx * 0.4))
lcorr = min(ly, lx)
# taper edges
maskMul = spatial_taper((Ly * Lx)**0.5 * 0.01, Ly, Lx).astype(np.complex64)
# spatial filter
fhg = gaussian_fft(2, Ly, Lx)
# shifts and corrmax
ymax = np.zeros((nt - 1, ), np.int32)
xmax = np.zeros((nt - 1, ), np.int32)
#cmax = np.zeros((nimg,), np.float32)
#maskOffset = X[1:].mean() * (1. - maskMul)
# taper and fft data
X = data.astype(np.float32)
#data -= data.mean(axis=-1, dtype=np.uint8).mean(axis=-1, dtype=np.uint8)[:,np.newaxis,np.newaxis]
X -= X.mean(axis=-1).mean(axis=-1)[:, np.newaxis, np.newaxis]
X = multiplytype(X, maskMul)
#X -= X.mean(axis=-1).mean(axis=-1)[:,np.newaxis,np.newaxis]
for t in range(nt):
fft2(X[t], overwrite_x=True)
# phase correlation with previous frame
X = phase_norm(X, fhg.astype(np.complex64))
Xout = X[1:] * np.conj(X[:-1])
for t in range(nt - 1):
ifft2(Xout[t], overwrite_x=True)
x00, x01, x10, x11 = my_clip(Xout, lcorr)
cc = np.real(np.block([[x11, x10], [x01, x00]]))
#cc = spatial_smooth(cc, 2)
for t in range(nt - 1):
ymax[t], xmax[t] = np.unravel_index(np.argmax(cc[t], axis=None),
(2 * lcorr + 1, 2 * lcorr + 1))
#cmax[t] = cc[t, ymax[t], xmax[t]]
ymax, xmax = ymax - lcorr, xmax - lcorr
return ymax, xmax
def _rfft2(a, overwrite_x = False):
libname = CDDMConfig["fftlib"]
cutoff = a.shape[-1]//2 + 1
if libname == "mkl_fft":
return mkl_fft.fft2(a, overwrite_x = overwrite_x)[...,0:cutoff]
elif libname == "scipy":
return spfft.fft2(a, overwrite_x = overwrite_x)[...,0:cutoff]
elif libname == "numpy":
return np.fft.rfft2(a.real) #force real in case input is complex
else:#default implementation is numpy fft2
return np.fft.fft2(a)[...,0:cutoff]
def __call__(self, src, ch):
self.big_op[:] = 0.0
self.big_u[:] = 0.0
self.local_func(src, ch, self.xv, self.yv, self.big_op, self.big_u,
self.n_extract)
big_adj = np.fft.fftshift(ifft2(fft2(self.big_op) * self.TH).real)
small_u = self.big_u[self.n_extract:-self.n_extract,
self.n_extract:-self.n_extract].copy()
small_adj = big_adj[self.n_extract:-self.n_extract,
self.n_extract:-self.n_extract].copy()
np.subtract(small_u, small_adj, small_u)
return small_u
def fft2c(img):
shp = img.shape
nimg = int(np.prod(shp[0:-2]))
scale = 1 / np.sqrt(np.prod(shp[-2:]))
img = np.reshape(img, (nimg, shp[-2], shp[-1]))
tmp = np.empty_like(img, dtype=np.complex64)
for i in range(nimg):
tmp[i] = scale * np.fft.fftshift(mkl_fft.fft2(np.fft.ifftshift(
img[i])))
kspace = np.reshape(tmp, shp)
return kspace
def _initialize_fft(self):
""" Define the two-dimensional FFT methods.
"""
if self.use_mkl:
import mkl
mkl.set_num_threads(self.nthreads)
import mkl_fft
self.fft = (lambda x: mkl_fft.fft2(x))
self.ifft = (lambda x: mkl_fft.ifft2(x))
else:
self.fft = (lambda x: np.fft.fft2(x))
self.ifft = (lambda x: np.fft.ifft2(x))
def gaussian_fft(sig, Ly, Lx):
''' gaussian filter in the fft domain with std sig and size Ly,Lx '''
x = np.arange(0, Lx)
y = np.arange(0, Ly)
x = np.abs(x - x.mean())
y = np.abs(y - y.mean())
xx, yy = np.meshgrid(x, y)
hgx = np.exp(-np.square(xx/sig) / 2)
hgy = np.exp(-np.square(yy/sig) / 2)
hgg = hgy * hgx
hgg /= hgg.sum()
fhg = np.real(fft2(fft.ifftshift(hgg))); # smoothing filter in Fourier domain
return fhg
def qsSample(parameter,source,template): (fftim,fftim2, imSize,dist,n,k)=parameter; for r in range(1,numpy.max(numpy.array(template.shape))//2): if numpy.logical_not(numpy.isnan(source[dist<=r])).sum()>=n: break; source[dist>r]=numpy.nan; extendSource=numpy.pad(source,((0,imSize[0]-source.shape[0]),(0,imSize[1]-source.shape[1])),'constant', constant_values=numpy.nan); extendtemplate=numpy.pad(template,((0,imSize[0]-source.shape[0]),(0,imSize[1]-source.shape[1])),'constant', constant_values=0); mismatchMap=numpy.real( fft.ifft2( fftim2 * numpy.conj(fft.fft2(extendtemplate* numpy.nan_to_num(extendSource*0+1))) - 2 * fftim * numpy.conj(fft.fft2(extendtemplate* numpy.nan_to_num(extendSource))))); mismatchMap[-template.shape[0]+1:,:]=numpy.nan; mismatchMap[:,-template.shape[1]+1:]=numpy.nan; indexes=numpy.argpartition(numpy.roll(mismatchMap,tuple(x//2 for x in template.shape),(0,1)).flat,math.ceil(k)); return indexes[int(math.floor(numpy.random.uniform(k)))];
def _initialize_fft(self):
""" Define the two-dimensional FFT methods.
"""
if self.use_mkl:
import mkl
mkl.set_num_threads(self.nthreads)
import mkl_fft
self.fft = (lambda x : mkl_fft.fft2(x))
self.ifft = (lambda x : mkl_fft.ifft2(x))
else:
self.fft = (lambda x : np.fft.fft2(x))
self.ifft = (lambda x : np.fft.ifft2(x))
def fft2c(img):
"""
it works on last two dimensions. takes image domain data and do the
fft2 to return kspace data
"""
shp = img.shape
nimg = int(np.prod(shp[0:-2]))
scale = 1 / np.sqrt(np.prod(shp[-2:]))
img = np.reshape(img, (nimg, shp[-2], shp[-1]))
tmp = np.empty_like(img, dtype=np.complex64)
for i in range(nimg):
#tmp[i]=scale*np.fft.fftshift(np.fft.fft2(np.fft.ifftshift(img[i])))
tmp[i] = scale * np.fft.fftshift(mkl_fft.fft2(np.fft.ifftshift(
img[i])))
kspace = np.reshape(tmp, shp)
return kspace
def fft2(x, s=None, axes=(-2, -1), overwrite_input=False, extra_info=None):
return mkl_fft.fft2(x, shape=s, axes=axes, overwrite_x=overwrite_input)
def fft2(x, s=None, axes=(-2,-1), overwrite_input=False, extra_info=None):
return mkl_fft.fft2(x, shape=s, axes=axes, overwrite_x=overwrite_input)
def __call__(self, src, ch):
op, u = self.local_func(src, ch, self.xv, self.yv)
u += ifft2(fft2(op) * self.inverse_symbol).real
return u
def complex_fft2(img: np.ndarray, pad_fft: bool = False) -> np.ndarray:
"""Returns the complex conjugate of the fft-transformed 2D array 'img', optionally padded for speed."""
Ly, Lx = img.shape
return np.conj(fft2(img, (next_fast_len(Ly),
next_fast_len(Lx)))) if pad_fft else np.conj(
fft2(img))
def convolve(mov: np.ndarray, img: np.ndarray) -> np.ndarray:
"""Returns the 3D array 'mov' convolved by a 2D array 'img'."""
return ifft2(apply_dotnorm(fft2(mov), img))
def __init__(self,
gf,
fs,
ifs,
h,
spread_width,
kernel_kwargs=None,
funcgen_tol=1e-10,
inline_core=True):
"""
Backend class for re-usable 'ewald' sum grid evaluation
for reusability, the grid must have the same h
and the ewald sum must use the same spread width
gf: numba callable greens function, gf(r)
fs: Fourier symbol for operator, fs(kx, ky)
ifs: Inverse Fourier symbol for operator, ifs(kx, ky)
h: grid spacing
spread_width: width to do spreading on
for Laplace, 15 gives ~7 digits
20 gives ~10 digits
can't seem to do much better than that, right now
kernel_kwargs: dict of arguments to be passed to gf, fs, ifs, tsgf functions
funcgen_tol: tolerance for function generator representation of
functions used in interior spread funciton. can't seem to beat
~10 digits overall now, so no real reason to do more than that
inline_core: whether to inline the function generator functions into
the compiled ewald functions
(inlining may speed things up but slows compilation time,
sometimes dramatically)
"""
self.kernel_kwargs = {} if kernel_kwargs is None else kernel_kwargs
self.gf = lambda r: gf(r, **self.kernel_kwargs)
self.fourier_symbol = lambda kx, ky: fs(kx, ky, **self.kernel_kwargs)
self.inverse_fourier_symbol = lambda kx, ky: ifs(
kx, ky, **self.kernel_kwargs)
self.h = h
self.spread_width = spread_width
self.funcgen_tol = funcgen_tol
self.inline_core = inline_core
# construct mollifier
self.mollifier = SlepianMollifier(2 * self.spread_width)
self.ssw = self.spread_width * self.h
# screened greens function
_excisor_gf = lambda d: excisor(d, 0.0, self.ssw, self.mollifier
) * self.gf(d)
try:
self.ex_funcgen = FunctionGenerator(_excisor_gf,
0.0,
self.ssw,
tol=self.funcgen_tol,
inline_core=self.inline_core)
self.excisor_gf = self.ex_funcgen.get_base_function(check=False)
except:
raise Exception(
'Failed constructing FunctionGenerator function for mollifier')
# construct differential operator applied to residual of screened greens function
_sn = 4 * self.spread_width
_sgv = np.linspace(0, 4 * self.ssw, _sn, endpoint=False)
_sgx, _sgy = np.meshgrid(_sgv, _sgv, indexing='ij')
_skv = np.fft.fftfreq(_sn, self.h / (2 * np.pi))
_skx, _sky = np.meshgrid(_skv, _skv, indexing='ij')
_slap = self.fourier_symbol(_skx, _sky)
pt = np.array([[2 * self.ssw], [2 * self.ssw]])
targ = np.row_stack([_sgx.ravel(), _sgy.ravel()])
u = gf_apply(self.gf, pt[0], pt[1], targ[0], targ[1], np.array([
1.0,
])).reshape(_sn, _sn)
u[_sn // 2, _sn // 2] = 0.0
dist = np.hypot(_sgx - 2 * self.ssw, _sgy - 2 * self.ssw)
dec1 = excisor(dist, 0.0, self.ssw, self.mollifier)
dec2 = excisor(dist, self.ssw, 2 * self.ssw, self.mollifier)
uf = u * (1 - dec1) * dec2
self.do_ufd = ifft2(fft2(uf) * _slap).real
# get an interpolater for this
_ax = np.linspace(np.pi, 1.5 * np.pi, 1000)
_ay = np.repeat(np.pi, _ax.size)
_ar = np.linspace(0, self.ssw, _ax.size)
_fh = fft2(self.do_ufd) / (_sn * _sn)
out = finufft.nufft2d2(_ax, _ay, _fh, isign=1, eps=1e-15, modeord=1)
self._do_ufd_interpolater = sp.interpolate.InterpolatedUnivariateSpline(
_ar, out.real, k=5, bbox=[0, self.ssw], ext=1)
try:
self.do_ufd_funcgen = FunctionGenerator(
self._do_ufd_interpolater,
0.0,
self.ssw,
tol=self.funcgen_tol,
inline_core=self.inline_core)
self.do_ufd_interpolater = self.do_ufd_funcgen.get_base_function(
check=False)
except:
raise Exception(
'Failed constructing FunctionGenerator function for laplacian of greens function times mollifier'
)
def phasecorr(data, refAndMasks, ops):
""" compute phase correlations for each block
Parameters
-------------
data : int16 or float32, 3D array
size [nimg x Ly x Lx]
refAndMasks : list
gaussian filter, mask offset, FFT of reference image
ops : dictionary
'Ly', 'Lx', 'nblocks', 'yblock', 'xblock' <- indices of blocks
ymax1 : 2D array
size [nimg x nblocks], y shifts of blocks
xmax1 : 2D array
size [nimg x nblocks], y shifts of blocks
cmax1 : 2D array
size [nimg x nblocks], value of peak of phase correlation
ccsm : 4D array
size [nimg x nblocks x ly x lx], smoothed phase correlations
"""
nimg, Ly, Lx = data.shape
maskMul = refAndMasks[0].squeeze()
maskOffset = refAndMasks[1].squeeze()
cfRefImg = refAndMasks[2].squeeze()
LyMax = np.diff(np.array(ops['yblock']))
ly, lx = cfRefImg.shape[-2:]
lyhalf = int(np.floor(ly / 2))
lxhalf = int(np.floor(lx / 2))
# maximum registration shift allowed
maxregshift = np.round(ops['maxregshiftNR'])
lcorr = int(
np.minimum(maxregshift,
np.floor(np.minimum(ly, lx) / 2.) - lpad))
nb = len(ops['yblock'])
nblocks = ops['nblocks']
# preprocessing for 1P recordings
if ops['1Preg']:
X = utils.one_photon_preprocess(data.copy().astype(np.float32), ops)
# shifts and corrmax
ymax1 = np.zeros((nimg, nb), np.float32)
cmax1 = np.zeros((nimg, nb), np.float32)
xmax1 = np.zeros((nimg, nb), np.float32)
cc0 = np.zeros(
(nimg, nb, 2 * lcorr + 2 * lpad + 1, 2 * lcorr + 2 * lpad + 1),
np.float32)
ymax = np.zeros((nb, ), np.int32)
xmax = np.zeros((nb, ), np.int32)
Y = np.zeros((nimg, nb, ly, lx), 'int16')
for n in range(nb):
yind, xind = ops['yblock'][n], ops['xblock'][n]
Y[:, n] = data[:, yind[0]:yind[-1], xind[0]:xind[-1]]
Y = addmultiply(Y, maskMul, maskOffset)
for n in range(nb):
for t in range(nimg):
fft2(Y[t, n], overwrite_x=True)
Y = utils.apply_dotnorm(Y, cfRefImg)
for n in range(nb):
for t in range(nimg):
ifft2(Y[t, n], overwrite_x=True)
x00, x01, x10, x11 = clip(Y, lcorr + lpad)
cc0 = np.real(np.block([[x11, x10], [x01, x00]]))
cc0 = np.transpose(cc0, (1, 0, 2, 3))
cc0 = cc0.reshape((cc0.shape[0], -1))
cc2 = []
R = ops['NRsm']
cc2.append(cc0)
for j in range(2):
cc2.append(R @ cc2[j])
for j in range(len(cc2)):
cc2[j] = cc2[j].reshape(
(nb, nimg, 2 * lcorr + 2 * lpad + 1, 2 * lcorr + 2 * lpad + 1))
ccsm = cc2[0]
for n in range(nb):
snr = np.ones((nimg, ), 'float32')
for j in range(len(cc2)):
ism = snr < ops['snr_thresh']
if np.sum(ism) == 0:
break
cc = cc2[j][n, ism, :, :]
if j > 0:
ccsm[n, ism, :, :] = cc
snr[ism] = getSNR(cc, (lcorr, lpad), ops)
ccmat = np.zeros((nb, 2 * lpad + 1, 2 * lpad + 1), np.float32)
for t in range(nimg):
ccmat = np.zeros((nb, 2 * lpad + 1, 2 * lpad + 1), np.float32)
for n in range(nb):
ix = np.argmax(ccsm[n, t][lpad:-lpad, lpad:-lpad], axis=None)
ym, xm = np.unravel_index(ix, (2 * lcorr + 1, 2 * lcorr + 1))
ccmat[n] = ccsm[n, t][ym:ym + 2 * lpad + 1, xm:xm + 2 * lpad + 1]
ymax[n], xmax[n] = ym - lcorr, xm - lcorr
ccmat = np.reshape(ccmat, (nb, -1))
ccb = np.dot(ccmat, Kmat)
imax = np.argmax(ccb, axis=1)
cmax = np.amax(ccb, axis=1)
ymax1[t], xmax1[t] = np.unravel_index(imax, (nup, nup))
cmax1[t] = cmax
mdpt = np.floor(nup / 2)
ymax1[t], xmax1[t] = (ymax1[t] - mdpt) / subpixel, (xmax1[t] -
mdpt) / subpixel
ymax1[t], xmax1[t] = ymax1[t] + ymax, xmax1[t] + xmax
#ccmat = np.reshape(ccmat, (nb, 2*lpad+1, 2*lpad+1))
return ymax1, xmax1, cmax1, ccsm
def qs(ti,dst,path,template,n,k): dist=numpy.zeros(shape=template.shape); dist[math.floor(dist.shape[0]/2),math.floor(dist.shape[1]/2)]=1; dist=ndimage.morphology.distance_transform_edt(1-dist); return runsim((fft.fft2(ti), fft.fft2(ti**2),ti.shape, dist, n, k),ti, dst,path,template,qsSample);
def mklfft(it):
mkl_fft.fft2(it)
