def findMaxWithGFit(img, sigma=0.5, win=11):
'''
find sub-pixel peaks from input img using n-dimensional Guassian fitting
sigma: scaler or [simgaZ,sigmaY..]
window: a window where the Guassian fit is performed on
return [v, zyx, sigma]
'''
vzyx = N.array(U.findMax(img))
ndim = img.ndim
try:
ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma, win)
except IndexError: # too close to the edge
imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:]))
sigma = imgFit._scalerToSeq(sigma, ndim)
return vzyx[0:1], vzyx[-ndim:], list(sigma)
if check == 5 or N.any(ret[2:2 + ndim] > (vzyx[-ndim:] + win)) or N.any(
ret[2:2 + ndim] < (vzyx[-ndim:] - win)):
imgFit.fitFailedAppend("at %s, %s, check=%i" %
(str(vzyx[-ndim:]), str(ret), check))
sigma = imgFit._scalerToSeq(sigma, ndim)
return [vzyx[0:1], vzyx[-ndim:], sigma]
#x= (vzyx[0:1] + [vzyx[-ndim:]] + [sigma])
# print x
# return x
else:
v = ret[1]
zyx = ret[2:2 + ndim]
sigma = ret[2 + ndim:2 + ndim * 2]
return [v, zyx, sigma]
def _doBilinear2D(a2d, tyx=(0,0), r=0, mag=1, anismag=1, dyx=(0,0), mr=0, b2d=None):
if b2d is None:
b2d = N.empty_like(a2d)
else:
b2d = N.ascontiguousarray(b2d)
a2d = a2d.copy() # otherwise, the following code will mess up the input
if N.any(dyx[-2:]) or mr:
temp = b2d
target = a2d
U.trans2d(target, temp, (dyx[-1], dyx[-2], mr, 1, 0, 1))
else:
temp = a2d
target = b2d
# rot mag first to make consistent with affine
magaxis = 1 # only this axis works
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
# then translate
tyx2 = N.array(tyx) # copy
tyx2[-2:] -= dyx[-2:]
if N.any(tyx2[-2:]) or mr:
U.trans2d(target, temp, (tyx2[-1], tyx2[-2], -mr, 1, 0, 1))
else:
temp[:] = target[:]
#a[z] = temp[:]
return temp
def findMaxWithGFit(img, sigma=0.5, win=11):
'''
find sub-pixel peaks from input img using n-dimensional Guassian fitting
sigma: scaler or [simgaZ,sigmaY..]
window: a window where the Guassian fit is performed on
return [v, zyx, sigma]
'''
vzyx = N.array(U.findMax(img))
ndim = img.ndim
try:
ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma, win)
except IndexError: # too close to the edge
imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:]))
sigma = imgFit._scalerToSeq(sigma, ndim)
return vzyx[0:1], vzyx[-ndim:], list(sigma)
if check == 5 or N.any(ret[2:2+ndim] > (vzyx[-ndim:] + win)) or N.any(ret[2:2+ndim] < (vzyx[-ndim:] - win)):
imgFit.fitFailedAppend("at %s, %s, check=%i" % (str(vzyx[-ndim:]), str(ret), check))
sigma = imgFit._scalerToSeq(sigma, ndim)
return [vzyx[0:1], vzyx[-ndim:], sigma]
#x= (vzyx[0:1] + [vzyx[-ndim:]] + [sigma])
# print x
# return x
else:
v = ret[1]
zyx = ret[2:2+ndim]
sigma = ret[2+ndim:2+ndim*2]
return [v,zyx,sigma]
def _doBilinear2D(a2d, tyx=(0,0), r=0, mag=1, anismag=1, dyx=(0,0), mr=0, b2d=None):
if b2d is None:
b2d = N.empty_like(a2d)
else:
b2d = N.ascontiguousarray(b2d)
a2d = a2d.copy() # otherwise, the following code will mess up the input
if N.any(dyx[-2:]) or mr:
temp = b2d
target = a2d
U.trans2d(target, temp, (dyx[-1], dyx[-2], mr, 1, 0, 1))
else:
temp = a2d
target = b2d
# rot mag first to make consistent with affine
magaxis = 1 # only this axis works
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
# then translate
tyx2 = N.array(tyx) # copy
tyx2[-2:] -= dyx[-2:]
if N.any(tyx2[-2:]) or mr:
U.trans2d(target, temp, (tyx2[-1], tyx2[-2], -mr, 1, 0, 1))
else:
temp[:] = target[:]
#a[z] = temp[:]
return temp
def _findMaxXcor(c, win, gFit=True, niter=2):
if gFit:
for i in range(niter):
v, zyx, s = findMaxWithGFit(c, win=win + (i * 2))
if v:
if N.any(zyx > N.array(c.shape)) or N.any(zyx < 0):
vzyx = N.array(U.findMax(c)) #continue
v = vzyx[0]
zyx = vzyx[-c.ndim:] + 0.5 # pixel center
s = 2.5
else:
break
if not v:
v = U.findMax(c)[0]
else:
vzyx = U.findMax(c)
v = vzyx[0]
zyx = N.array(vzyx[-c.ndim:]) + 0.5 # pixel center
s = 2.5
return v, zyx, s
def _findMaxXcor(c, win, gFit=True, niter=2):
if gFit:
for i in range(niter):
v, zyx, s = findMaxWithGFit(c, win=win+(i*2))
if v:
if N.any(zyx > N.array(c.shape)) or N.any(zyx < 0):
vzyx = N.array(U.findMax(c))#continue
v = vzyx[0]
zyx = vzyx[-c.ndim:] + 0.5 # pixel center
s = 2.5
else:
break
if not v:
v = U.findMax(c)[0]
else:
vzyx = U.findMax(c)
v = vzyx[0]
zyx = N.array(vzyx[-c.ndim:]) + 0.5 # pixel center
s = 2.5
return v, zyx, s
def findMaxWithGFitAll(img, thre=0, sigma_peak=0.5, win=11, mask_npxls=3):
"""
find peaks until either
1. any pxls becomes below thre
2. the same peak was found as the maximum
mask_npxls: number of pixels to mask when Gaussian fitting failed
return poslist
"""
img = img.copy()
imgFit.fitFailedClear()
ndim = img.ndim
sigma_peak = imgFit._scalerToSeq(sigma_peak, ndim)
poses = []
vzyx = U.findMax(img)
while vzyx[0] > thre:
prev = vzyx
try:
ret, check = imgFit.fitGaussianND(img, vzyx[-ndim:], sigma_peak, win)
except IndexError: # too close to the edge
imgFit.fitFailedAppend("at %s" % str(vzyx[-ndim:]))
mask_value(img, vzyx[-ndim:], r=mask_npxls, value=img.min())
poses.append(list(vzyx)[0:1] + [vzyx[-ndim:]] + [list(sigma_peak)])
if check == 5:
imgFit.fitFailedAppend("at %s, %s, check=%i" % (str(vzyx[-ndim:]), str(ret), check))
mask_value(img, vzyx[-ndim:], r=mask_npxls, value=img.min())
poses.append(list(vzyx)[0:1] + [vzyx[-ndim:]] + [sigma_peak])
else:
v = ret[1]
zyx = ret[2:2+ndim]
if N.any(N.abs(zyx - vzyx[1:]) > win/2.):#zyx < 0 or zyx > img.shape or ):
mask_value(img, vzyx[-ndim:], r=mask_npxls, value=img.min())
poses.append(list(vzyx)[0:1] + [vzyx[-ndim:]] + [sigma_peak])
else:
sigma = ret[2+ndim:2+ndim*2]
mask_gaussianND(img, zyx, v, sigma)
poses.append([v,zyx,sigma])
vzyx = U.findMax(img)
if N.all(vzyx == prev):
break
return poses#, img
def trans3D_affine(arr,
tzyx=(0, 0, 0),
r=0,
mag=1,
dzyx=(0, 0, 0),
rzy=0,
ncpu=NCPU,
order=ORDER): #**kwds):
"""
return array
"""
dtype = arr.dtype.type
arr = arr.astype(N.float32)
ndim = arr.ndim
if ndim == 2:
arr = arr.reshape((1, ) + arr.shape)
elif ndim == 3:
if len(tzyx) < ndim:
tzyx = (0, ) * (ndim - len(tzyx)) + tuple(tzyx)
if len(dzyx) < ndim:
dzyx = (0, ) * (ndim - len(dzyx)) + tuple(dzyx)
dzyx = N.asarray(dzyx)
magz = 1
try:
if len(mag) == 3:
magz = mag[0]
mag = mag[1:]
except TypeError:
pass
if ndim == 3 and (magz != 1 or tzyx[-3] or rzy):
#print magz, arr.shape
# because, mergins introduced after 2D transformation may interfere the result of this vertical transform, vertical axis was processed first, since rzy is 0 usually.
arrT = arr.T # zyx -> xyz
magzz = (1, magz)
canvas = N.zeros_like(arrT)
tzy = (0, tzyx[-3])
dzy = (dzyx[-2], dzyx[-3])
#if ncpu > 1 and mp:
ret = ppro.pmap(_dothat, arrT, ncpu, tzy, rzy, magzz, dzy, order)
for x, a in enumerate(ret):
canvas[x] = a
#else:
# for x, a in enumerate(arrT):
# canvas[x] = _dothat(a, tzy, rzy, magzz, dzy, order)
arr = canvas.T
#del arrT
if N.any(tzyx[-2:]) or r or N.any(mag):
#print ndim, arr.shape
canvas = N.zeros_like(arr)
if ndim == 3: # and ncpu > 1 and mp:
# dividing XY into pieces did not work for rotation and magnification
# here parallel processing is done section-wise since affine works only for 2D
ret = ppro.pmap(_dothat, arr, ncpu, tzyx[-2:], r, mag, dzyx[-2:],
order)
for z, a in enumerate(ret):
canvas[z] = a
else:
for z, a in enumerate(arr):
canvas[z] = _dothat(a, tzyx[-2:], r, mag, dzyx[-2:], order)
if ndim == 2:
canvas = canvas[0]
arr = canvas
if dtype in (N.int, N.uint8, N.uint16, N.uint32):
arr = N.where(arr < 0, 0, arr)
return arr.astype(dtype)
def trans3D_spline(a,
tzyx=(0, 0, 0),
r=0,
mag=1,
dzyx=(0, 0),
rzy=0,
mr=0,
reshape=False,
ncpu=1,
**splinekwds):
"""
mag: scalar_for_yx or [y,x] or [z,y,x]
mr: rotational direction of yx-zoom in degrees
ncpu: no usage
"""
splinekwds['prefilter'] = splinekwds.get('prefilter', True)
splinekwds['order'] = splinekwds.get('order', 3)
ndim = a.ndim
shape = N.array(a.shape, N.float32)
tzyx = N.asarray(tzyx, N.float32)
# rotation axis
if ndim == 3:
axes = (1, 2)
else:
axes = (1, 0)
# magnification
try:
if len(mag) == 1: # same as scalar
mag = [1] * (ndim - 2) + list(mag) * 2
else:
mag = [1] * (ndim - 2) * (3 - len(mag)) + list(mag)
except: # scalar -> convert to yx mag only
mag = [1] * (ndim - 2) + ([mag] * ndim)[:2]
mag = N.asarray(mag)
try:
dzyx = N.array([0] * (ndim - 2) * (3 - len(dzyx)) + list(dzyx))
except: # scalar
pass
if mr:
a = U.nd.rotate(a, mr, axes=axes, reshape=reshape, **splinekwds)
splinekwds['prefilter'] = False
if N.any(dzyx):
a = U.nd.shift(a, -dzyx, **splinekwds)
splinekwds['prefilter'] = False
if r:
a = U.nd.rotate(a, -r, axes=axes, reshape=reshape, **splinekwds)
splinekwds['prefilter'] = False
if N.any(mag != 1):
a = U.nd.zoom(a, zoom=mag, **splinekwds)
splinekwds['prefilter'] = False
if not reshape:
dif = (shape - N.array(a.shape, N.float32)) / 2.
mod = N.ceil(N.mod(dif, 1))
tzyx[-ndim:] -= (mod / 2.)
if rzy and ndim >= 3: # is this correct?? havn't tried yet
a = U.nd.rotate(a, -rzy, axes=(0, 1), reshape=reshape, **splinekwds)
if N.any(dzyx):
a = U.nd.shift(a, dzyx, **splinekwds)
if mr:
a = U.nd.rotate(a, -mr, axes=axes, reshape=reshape, **splinekwds)
if reshape:
a = U.nd.shift(a, tzyx[-ndim:], **splinekwds)
else:
tzyx0 = N.where(mag >= 1, tzyx[-ndim:], 0)
if N.any(tzyx0[-ndim:]):
a = U.nd.shift(a, tzyx0[-ndim:], **splinekwds)
if N.any(mag != 1) and not reshape:
a = keepShape(a, shape, (dif, mod))
old = """
canvas = N.zeros(shape, a.dtype.type)
#dif = (shape - N.array(a.shape, N.float32)) / 2
#mod = N.ceil(N.mod(dif, 1))
dif = N.where(dif > 0, N.ceil(dif), N.floor(dif))
# smaller
aoff = N.where(dif < 0, 0, dif)
aslc = [slice(dp, shape[i]-dp+mod[i]) for i, dp in enumerate(aoff)]
# larger
coff = N.where(dif > 0, 0, -dif)
cslc = [slice(dp, a.shape[i]-dp+mod[i]) for i, dp in enumerate(coff)]
canvas[aslc] = a[cslc]
a = canvas"""
if not reshape:
tzyx0 = N.where(mag < 1, tzyx[-ndim:], 0)
if N.any(mag != 1):
tzyx0[-ndim:] -= (mod / 2.)
if N.any(tzyx0[-ndim:]):
a = U.nd.shift(a, tzyx0[-ndim:], **splinekwds)
return a
def trans3D_bilinear(a,
tzyx=(0, 0, 0),
r=0,
mag=1,
dzyx=(0, 0, 0),
b=None,
rzy=0,
**kwds):
"""
magyx: scalar or [y,x] or [y,x, direction in degrees]
"""
a = a.copy()
ndim = a.ndim
if ndim == 2:
a = a.reshape((1, ) + a.shape)
try:
if len(magyx) == 3:
mr = magyx[-1]
magyx = magyx[:2]
else:
mr = 0
except:
mr = 0
if b is None:
b2d = N.empty_like(a[0])
dzyx = N.asarray(dzyx)
tzyx = N.asarray(tzyx)
tzyx[-2:] += dzyx[-2:]
magaxis = 1 # only this axis works
magz = 1
try:
if len(mag) == 3:
magz, magy, magx = mag
elif len(mag) == 2:
magy, magx = mag
else:
magy = magx = mag[0]
except:
magy = magx = mag
mag = magy
anismag = magx / magy
for z, a2d in enumerate(a):
if N.any(dzyx[-2:]) or mr:
temp = N.ascontiguousarray(b2d)
target = N.ascontiguousarray(a2d)
#U.trans2d(target, temp, (-dyx[1], -dyx[0], -mr, 1, 0, 1))
U.trans2d(target, temp, (-dzyx[-1], -dzyx[-2], -mr, 1, 0, 1))
else:
temp = N.ascontiguousarray(a2d)
target = N.ascontiguousarray(b2d)
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
#if rzx: # is this correct?? havn't tried yet
# target = U.nd.rotate(target, rzx, axes=(0,2), order=1, prefilter=False)
if N.any(tzyx[-2:]) or mr: #N.any(dyx) or mr:
#U.trans2d(target, temp, (dyx[1], dyx[0], mr, 1, 0, 1))
U.trans2d(target, temp, (tzyx[-1], tzyx[-2], mr, 1, 0, 1))
else:
temp[:] = target[:]
a[z] = temp[:]
if ndim == 2:
a = a[0]
elif ndim == 3 and (magz != 1 or tzyx[-3]):
at = a.T # zyx -> xyz
mag = 1 #magz
anismag = magz #magy / magz
canvas = N.empty_like(at)
target = b2d.T
for x, a2d in enumerate(at):
if dzyx[-3]:
U.trans2d(a2d, target, (0, -dyzx[-3], 0, 1, 1, 1))
else:
target = a2d
canvas[x] = U.trans2d(target, None,
(tzyx[-3], 0, rzy, mag, magaxis, anismag))
#canvas = canvas.T
a = canvas.T
return a
def trans3D_affine(arr, tzyx=(0,0,0), r=0, mag=1, dzyx=(0,0,0), rzy=0, ncpu=NCPU, order=ORDER):#**kwds):
"""
return array
"""
dtype = arr.dtype.type
arr = arr.astype(N.float32)
ndim = arr.ndim
if ndim == 2:
arr = arr.reshape((1,)+arr.shape)
elif ndim == 3:
if len(tzyx) < ndim:
tzyx = (0,)*(ndim-len(tzyx)) + tuple(tzyx)
if len(dzyx) < ndim:
dzyx = (0,)*(ndim-len(dzyx)) + tuple(dzyx)
dzyx = N.asarray(dzyx)
magz = 1
try:
if len(mag) == 3:
magz = mag[0]
mag = mag[1:]
except TypeError:
pass
if ndim == 3 and (magz != 1 or tzyx[-3] or rzy):
#print magz, arr.shape
# because, mergins introduced after 2D transformation may interfere the result of this vertical transform, vertical axis was processed first, since rzy is 0 usually.
arrT = arr.T # zyx -> xyz
magzz = (1,magz)
canvas = N.zeros_like(arrT)
tzy = (0,tzyx[-3])
dzy = (dzyx[-2], dzyx[-3])
#if ncpu > 1 and mp:
ret = ppro.pmap(_dothat, arrT, ncpu, tzy, rzy, magzz, dzy, order)
for x, a in enumerate(ret):
canvas[x] = a
#else:
# for x, a in enumerate(arrT):
# canvas[x] = _dothat(a, tzy, rzy, magzz, dzy, order)
arr = canvas.T
#del arrT
if N.any(tzyx[-2:]) or r or N.any(mag):
#print ndim, arr.shape
canvas = N.zeros_like(arr)
if ndim == 3:# and ncpu > 1 and mp:
# dividing XY into pieces did not work for rotation and magnification
# here parallel processing is done section-wise since affine works only for 2D
ret = ppro.pmap(_dothat, arr, ncpu, tzyx[-2:], r, mag, dzyx[-2:], order)
for z, a in enumerate(ret):
canvas[z] = a
else:
for z, a in enumerate(arr):
canvas[z] = _dothat(a, tzyx[-2:], r, mag, dzyx[-2:], order)
if ndim == 2:
canvas = canvas[0]
arr = canvas
if dtype in (N.int, N.uint8, N.uint16, N.uint32):
arr = N.where(arr < 0, 0, arr)
return arr.astype(dtype)
def trans3D_spline(a, tzyx=(0,0,0), r=0, mag=1, dzyx=(0,0), rzy=0, mr=0, reshape=False, ncpu=1, **splinekwds):
"""
mag: scalar_for_yx or [y,x] or [z,y,x]
mr: rotational direction of yx-zoom in degrees
ncpu: no usage
"""
splinekwds['prefilter'] = splinekwds.get('prefilter', True)
splinekwds['order'] = splinekwds.get('order', 3)
ndim = a.ndim
shape = N.array(a.shape, N.float32)
tzyx = N.asarray(tzyx, N.float32)
# rotation axis
if ndim == 3:
axes = (1,2)
else:
axes = (1,0)
# magnification
try:
if len(mag) == 1: # same as scalar
mag = [1] * (ndim-2) + list(mag) * 2
else:
mag = [1] * (ndim-2) * (3-len(mag)) + list(mag)
except: # scalar -> convert to yx mag only
mag = [1] * (ndim-2) + ([mag] * ndim)[:2]
mag = N.asarray(mag)
try:
dzyx = N.array([0] * (ndim-2) * (3-len(dzyx)) + list(dzyx))
except: # scalar
pass
if mr:
a = U.nd.rotate(a, mr, axes=axes, reshape=reshape, **splinekwds)
splinekwds['prefilter'] = False
if N.any(dzyx):
a = U.nd.shift(a, -dzyx, **splinekwds)
splinekwds['prefilter'] = False
if r:
a = U.nd.rotate(a, -r, axes=axes, reshape=reshape, **splinekwds)
splinekwds['prefilter'] = False
if N.any(mag != 1):
a = U.nd.zoom(a, zoom=mag, **splinekwds)
splinekwds['prefilter'] = False
if not reshape:
dif = (shape - N.array(a.shape, N.float32)) / 2.
mod = N.ceil(N.mod(dif, 1))
tzyx[-ndim:] -= (mod / 2.)
if rzy and ndim >= 3: # is this correct?? havn't tried yet
a = U.nd.rotate(a, -rzy, axes=(0,1), reshape=reshape, **splinekwds)
if N.any(dzyx):
a = U.nd.shift(a, dzyx, **splinekwds)
if mr:
a = U.nd.rotate(a, -mr, axes=axes, reshape=reshape, **splinekwds)
if reshape:
a = U.nd.shift(a, tzyx[-ndim:], **splinekwds)
else:
tzyx0 = N.where(mag >= 1, tzyx[-ndim:], 0)
if N.any(tzyx0[-ndim:]):
a = U.nd.shift(a, tzyx0[-ndim:], **splinekwds)
if N.any(mag != 1) and not reshape:
a = keepShape(a, shape, (dif, mod))
old="""
canvas = N.zeros(shape, a.dtype.type)
#dif = (shape - N.array(a.shape, N.float32)) / 2
#mod = N.ceil(N.mod(dif, 1))
dif = N.where(dif > 0, N.ceil(dif), N.floor(dif))
# smaller
aoff = N.where(dif < 0, 0, dif)
aslc = [slice(dp, shape[i]-dp+mod[i]) for i, dp in enumerate(aoff)]
# larger
coff = N.where(dif > 0, 0, -dif)
cslc = [slice(dp, a.shape[i]-dp+mod[i]) for i, dp in enumerate(coff)]
canvas[aslc] = a[cslc]
a = canvas"""
if not reshape:
tzyx0 = N.where(mag < 1, tzyx[-ndim:], 0)
if N.any(mag != 1):
tzyx0[-ndim:] -= (mod / 2.)
if N.any(tzyx0[-ndim:]):
a = U.nd.shift(a, tzyx0[-ndim:], **splinekwds)
return a
def trans3D_bilinear(a, tzyx=(0,0,0), r=0, mag=1, dzyx=(0,0,0), b=None, rzy=0, **kwds):
"""
magyx: scalar or [y,x] or [y,x, direction in degrees]
"""
a = a.copy()
ndim = a.ndim
if ndim == 2:
a = a.reshape((1,)+a.shape)
try:
if len(magyx) == 3:
mr = magyx[-1]
magyx = magyx[:2]
else:
mr = 0
except:
mr = 0
if b is None:
b2d = N.empty_like(a[0])
dzyx = N.asarray(dzyx)
tzyx = N.asarray(tzyx)
tzyx[-2:] += dzyx[-2:]
magaxis = 1 # only this axis works
magz = 1
try:
if len(mag) == 3:
magz, magy, magx = mag
elif len(mag) == 2:
magy, magx = mag
else:
magy = magx = mag[0]
except:
magy = magx = mag
mag = magy
anismag = magx / magy
for z, a2d in enumerate(a):
if N.any(dzyx[-2:]) or mr:
temp = N.ascontiguousarray(b2d)
target = N.ascontiguousarray(a2d)
#U.trans2d(target, temp, (-dyx[1], -dyx[0], -mr, 1, 0, 1))
U.trans2d(target, temp, (-dzyx[-1], -dzyx[-2], -mr, 1, 0, 1))
else:
temp = N.ascontiguousarray(a2d)
target = N.ascontiguousarray(b2d)
if r or mag != 1 or anismag != 1:
U.trans2d(temp, target, (0, 0, r, mag, magaxis, anismag))
else:
target[:] = temp[:]
#if rzx: # is this correct?? havn't tried yet
# target = U.nd.rotate(target, rzx, axes=(0,2), order=1, prefilter=False)
if N.any(tzyx[-2:]) or mr:#N.any(dyx) or mr:
#U.trans2d(target, temp, (dyx[1], dyx[0], mr, 1, 0, 1))
U.trans2d(target, temp, (tzyx[-1], tzyx[-2], mr, 1, 0, 1))
else:
temp[:] = target[:]
a[z] = temp[:]
if ndim == 2:
a = a[0]
elif ndim == 3 and (magz != 1 or tzyx[-3]):
at = a.T # zyx -> xyz
mag = 1#magz
anismag = magz #magy / magz
canvas = N.empty_like(at)
target = b2d.T
for x, a2d in enumerate(at):
if dzyx[-3]:
U.trans2d(a2d, target, (0, -dyzx[-3], 0, 1, 1, 1))
else:
target = a2d
canvas[x] = U.trans2d(target, None, (tzyx[-3], 0, rzy, mag, magaxis, anismag))
#canvas = canvas.T
a = canvas.T
return a