def _cutoutForAlign2(fn, py, outFn=''):
"""
if not outFn, default with 'cut' extention
resulting array is imgSequence=0 (t,w,z,y,x)
return outFn
"""
h = imgManager.ImageManager(fn)
slc, shiftZYX, ZYX = makeSlice(h, py)
# input
arr = N.empty((h.nt, h.nw, h.nz, h.ny, h.nx), h.dtype)
for t in range(h.nt):
for w in range(h.nw):
arr[t, w] = h.get3DArr(w=w, t=t)
canvas = N.squeeze(arr[slc].astype(arr.dtype.type))
newNum = (canvas.shape[-1], canvas.shape[-2], N.prod(canvas.shape[:-3]))
if not outFn:
outFn = '_'.join((h.filePath, EXT_CUTOUT)) #arr.Mrc.path, EXT_CUTOUT))
hdr = Mrc.makeHdrArray()
Mrc.initHdrArrayFrom(hdr, h.hdr) #arr.Mrc.hdr)
hdr.ImgSequence = 2
hdr.Num[:] = newNum
mstart = [sl.start for sl in slc[::-1][:3] if isinstance(sl, slice)]
hdr.mst[:len(mstart)] += mstart
Mrc.save(canvas, outFn, ifExists='overwrite', hdr=hdr)
return outFn
def __getitem__(self, idx):
try:
idx = int(idx)
if idx < 0:
idx = self.shape[0] + idx
ws = range(self.nw)
ts = range(self.nt)
zs = range(self.nz)
if self.maxaxs == 'w' and not self.ignor_color_axis:
ws = [idx]
self.sld_axs = 'w'
elif self.maxaxs == 't' or (self.ignor_color_axis
and self.ndim > 4):
ts = [idx]
self.sld_axs = 't'
elif self.maxaxs == 'z' or (self.ignor_color_axis
and self.ndim == 4):
zs = [idx]
self.sld_axs = 'z'
else:
raise ValueError, 'section axis not determined'
if self.hdr.ImgSequence == 0:
e = N.empty((len(ws), len(ts), len(zs), self.ny, self.nx),
self.dtype)
for wo, wi in enumerate(ws):
for to, ti in enumerate(ts):
for zo, zi in enumerate(zs):
e[wo, to, zo] = self.img.getArr(w=wi, t=ti, z=zi)
elif self.hdr.ImgSequence == 1:
e = N.empty((len(ts), len(zs), len(ws), self.ny, self.nx),
self.dtype)
for to, ti in enumerate(ts):
for zo, zi in enumerate(zs):
for wo, wi in enumerate(ws):
e[to, zo, wo] = self.img.getArr(w=wi, t=ti, z=zi)
elif self.hdr.ImgSequence == 2:
e = N.empty((len(ts), len(ws), len(zs), self.ny, self.nx),
self.dtype)
for to, ti in enumerate(ts):
for wo, wi in enumerate(ws):
for zo, zi in enumerate(zs):
e[to, wo, zo] = self.img.getArr(w=wi, t=ti, z=zi)
except TypeError:
pass
return N.squeeze(e)
def Xcorr(a,
b,
phaseContrast=PHASE,
nyquist=NYQUIST,
removeEdge=0,
gFit=True,
win=11,
ret=None,
searchRad=None):
"""
sigma uses F.gaussianArr in the Fourier domain
if ret is None:
return zyx, xcf
elif ret is 2:
return s, v, zyx, xcf
elif ret:
return v, zyx, xcf
"""
#print 'phase contrast: %s' % str(phaseContrast)
#global DATA
# correct odd shape particularly Z axis
a = N.squeeze(a)
b = N.squeeze(b)
a = imgFilters.evenShapeArr(a)
b = imgFilters.evenShapeArr(b)
shape = N.array(a.shape)
# apodize
a = apodize(a)
b = apodize(b)
# fourier transform
af = F.rfft(a.astype(N.float32))
bf = F.rfft(b.astype(N.float32))
del a, b
# phase contrast filter (removing any intensity information)
if phaseContrast:
afa = phaseContrastFilter(af, True, nyquist=nyquist)
bfa = phaseContrastFilter(bf, True, nyquist=nyquist)
else:
afa = af
bfa = bf
del af, bf
# removing edge if gaussian is not sufficient
targetShape = shape - N.multiply(removeEdge, 2)
if removeEdge:
ap = imgFilters.cutOutCenter(F.irfft(afa), targetShape)
bp = imgFilters.cutOutCenter(F.irfft(bfa), targetShape)
afa = F.rfft(ap)
bfa = F.rfft(bp)
del ap, bp
# shift array
delta = targetShape / 2.
shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta)
bfa *= shiftarr
# cross correlation
bfa = bfa.conjugate()
c = cc = F.irfft(afa * bfa)
center = N.divide(c.shape, 2)
if searchRad:
slc = imgGeo.nearbyRegion(c.shape, center, searchRad)
cc = N.zeros_like(c)
cc[slc] = c[slc]
v, zyx, s = _findMaxXcor(cc, win, gFit=gFit)
zyx -= center
if ret == 2:
return s, v, zyx, c
elif ret:
return v, zyx, c
else:
return zyx, c
def Xcorr(a, b, phaseContrast=PHASE, nyquist=NYQUIST, gFit=True, win=11, ret=None, searchRad=None, npad=4):
"""
sigma uses F.gaussianArr in the Fourier domain
if ret is None:
return zyx, xcf
elif ret is 2:
return s, v, zyx, xcf
elif ret is 3:
return zyx, xcf, a_phase_cotrast, b_phase_contrast
elif ret:
return v, zyx, xcf
"""
#print 'phase contrast: %s' % str(phaseContrast)
#global DATA
# correct odd shape particularly Z axis
a = N.squeeze(a)
b = N.squeeze(b)
a = imgFilters.evenShapeArr(a)
b = imgFilters.evenShapeArr(b)
shape = N.array(a.shape)
# padding strange shape
#nyx = max(shape[-2:])
#pshape = N.array(a.shape[:-2] + (nyx,nyx))
# apodize
a = paddAndApo(a, npad)#, pshape) #apodize(a)
b = paddAndApo(b, npad)#, pshape) #apodize(b)
# fourier transform
af = F.rfft(a.astype(N.float32))
bf = F.rfft(b.astype(N.float32))
del a, b
# phase contrast filter (removing any intensity information)
if phaseContrast:
afa = phaseContrastFilter(af, True, nyquist=nyquist)
bfa = phaseContrastFilter(bf, True, nyquist=nyquist)
else:
afa = af
bfa = bf
del af, bf
#targetShape = shape + (npad * 2)
targetShape = shape + (npad * 2)
# shift array
delta = targetShape / 2.
shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta)
bfa *= shiftarr
# cross correlation
bfa = bfa.conjugate()
#c = cc = F.irfft(afa * bfa)
c = F.irfft(afa * bfa)
# 20180214 the padded region was cutout before finding the peak.
c = cc = imgFilters.cutOutCenter(c, N.array(c.shape) - (npad * 2), interpolate=False)
#cc = c
center = N.divide(c.shape, 2)
if searchRad:
slc = imgGeo.nearbyRegion(c.shape, center, searchRad)
cc = N.zeros_like(c)
cc[slc] = c[slc]
v, zyx, s = _findMaxXcor(cc, win, gFit=gFit)
#return cc
#print(zyx, center)
zyx -= center
#c = imgFilters.cutOutCenter(c, N.array(c.shape) - (npad * 2), interpolate=False)
#c = imgFilters.cutOutCenter(c, shape, interpolate=False)
if ret == 3:
return zyx, c, F.irfft(afa), F.irfft(bfa)
elif ret == 2:
return s, v, zyx, c
elif ret:
return v, zyx, c
else:
return zyx, c
def Xcorr(a,
b,
phaseContrast=PHASE,
nyquist=NYQUIST,
gFit=True,
win=11,
ret=None,
searchRad=None,
npad=4):
"""
sigma uses F.gaussianArr in the Fourier domain
if ret is None:
return zyx, xcf
elif ret is 2:
return s, v, zyx, xcf
elif ret is 3:
return zyx, xcf, a_phase_cotrast, b_phase_contrast
elif ret:
return v, zyx, xcf
"""
#print 'phase contrast: %s' % str(phaseContrast)
#global DATA
# correct odd shape particularly Z axis
a = N.squeeze(a)
b = N.squeeze(b)
a = imgFilters.evenShapeArr(a)
b = imgFilters.evenShapeArr(b)
shape = N.array(a.shape)
# padding strange shape
#nyx = max(shape[-2:])
#pshape = N.array(a.shape[:-2] + (nyx,nyx))
# apodize
a = paddAndApo(a, npad) #, pshape) #apodize(a)
b = paddAndApo(b, npad) #, pshape) #apodize(b)
# fourier transform
af = F.rfft(a.astype(N.float32))
bf = F.rfft(b.astype(N.float32))
del a, b
# phase contrast filter (removing any intensity information)
if phaseContrast:
afa = phaseContrastFilter(af, True, nyquist=nyquist)
bfa = phaseContrastFilter(bf, True, nyquist=nyquist)
else:
afa = af
bfa = bf
del af, bf
#targetShape = shape + (npad * 2)
targetShape = shape + (npad * 2)
# shift array
delta = targetShape / 2.
shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta)
bfa *= shiftarr
# cross correlation
bfa = bfa.conjugate()
c = cc = F.irfft(afa * bfa)
center = N.divide(c.shape, 2)
if searchRad:
slc = imgGeo.nearbyRegion(c.shape, center, searchRad)
cc = N.zeros_like(c)
cc[slc] = c[slc]
v, zyx, s = _findMaxXcor(cc, win, gFit=gFit)
zyx -= center
c = imgFilters.cutOutCenter(c,
N.array(c.shape) - (npad * 2),
interpolate=False)
#c = imgFilters.cutOutCenter(c, shape, interpolate=False)
if ret == 3:
return zyx, c, F.irfft(afa), F.irfft(bfa)
elif ret == 2:
return s, v, zyx, c
elif ret:
return v, zyx, c
else:
return zyx, c
