def apodize(img, napodize=10, doZ=True):
"""
softens the edges of a singe xy section to reduce edge artifacts and improve the fits
return copy of the img
"""
img = img.copy()
img = img.astype(N.float32) # casting rule
# determine napodize
shape = N.array(img.shape) // 2
napodize = N.where(shape < napodize, shape, napodize)
if doZ and img.ndim >= 3 and img.shape[0] > 3:
rr = range(-3, 0)
else:
rr = range(-2, 0)
for idx in rr:
fact = N.arange(1. / napodize[idx],
napodize[idx],
1. / napodize[idx],
dtype=N.float32)[:napodize[idx]]
for napo in range(napodize[idx]):
slc0 = [Ellipsis, slice(napo, napo + 1)
] + [slice(None)] * abs(idx + 1)
if not napo:
slc1 = [Ellipsis, slice(-(napo + 1), None)
] + [slice(None)] * abs(idx + 1)
else:
slc1 = [Ellipsis, slice(-(napo + 1), -(napo))
] + [slice(None)] * abs(idx + 1)
img[slc0] *= fact[napo]
img[slc1] *= fact[napo]
#img[slc0] = img[slc0] * fact[napo] # casting rule
#img[slc1] = img[scl1] * fact[napo]
return img
def apodize(img, napodize=10, doZ=True):
"""
softens the edges of a singe xy section to reduce edge artifacts and improve the fits
return copy of the img
"""
img = img.copy()
img = img.astype(N.float32) # casting rule
# determine napodize
shape = N.array(img.shape) // 2
napodize = N.where(shape < napodize, shape, napodize)
if doZ and img.ndim >= 3 and img.shape[0] > 3:
rr = list(range(-3,0))
else:
rr = list(range(-2,0))
for idx in rr:
fact = N.arange(1./napodize[idx],napodize[idx],1./napodize[idx], dtype=N.float32)[:napodize[idx]]
for napo in range(napodize[idx]):
slc0 = [Ellipsis,slice(napo, napo+1)] + [slice(None)] * abs(idx+1)
if not napo:
slc1 = [Ellipsis,slice(-(napo+1),None)] + [slice(None)] * abs(idx+1)
else:
slc1 = [Ellipsis,slice(-(napo+1),-(napo))] + [slice(None)] * abs(idx+1)
img[slc0] *= fact[napo]
img[slc1] *= fact[napo]
#img[slc0] = img[slc0] * fact[napo] # casting rule
#img[slc1] = img[scl1] * fact[napo]
return img
def selectZsecs(self, start=0, stop=None, pattern=[1], pickWhich=1):
"""
stores section indices to be picked up when doing copy region
pattern: smallest pattern of elements eg. [1,1,0,2]
pickWich: sections corresponding this number in the pattern is picked up
"""
if stop is None:
stop = self.cropbox_u[0]
if start is None:
start = self.cropbox_l[0]
unit = len(pattern)
idx = N.arange(self.nz)
repeat = N.ceil(self.nz / float(unit))
repPat = N.tile(pattern, repeat)[:self.nz]
ids = N.compress(repPat == pickWhich, idx)
self._zIdx = [i for i in ids if i >= start and i < stop]
if not self._zIdx:
self._zIdx = [0]
print self._zIdx
self.cropbox_l[0] = start
self.cropbox_u[0] = stop
if self.cropbox_l[0] == self.cropbox_u[0]:
self.cropbox_u[0] += 1
self._zPtrn = pattern
self._zPick = pickWhich
def logpolar(image, center=None, angles=None, radii=None):
"""Return log-polar transformed image and log base."""
shape = image.shape
if center is None:
center = shape[0] / 2, shape[1] / 2
if angles is None:
angles = shape[0]
if radii is None:
radii = shape[1]
theta = N.zeros((angles, radii), dtype=N.float64)
theta.T[:] = -N.linspace(0, N.pi, angles, endpoint=False)
#d = radii
d = N.hypot(shape[0] - center[0], shape[1] - center[1])
log_base = 10.0**(N.log10(d) / (radii))
radius = N.empty_like(theta)
radius[:] = N.power(log_base, N.arange(radii, dtype=N.float64)) - 1.0
x = radius * N.sin(theta) + center[0]
y = radius * N.cos(theta) + center[1]
output = N.zeros_like(x)
ndii.map_coordinates(image, [x, y], output=output)
return output, log_base
def selectTimes(self, start=None, stop=None, pattern=[1], pickWhich=1):
"""
stores section indices to be picked up when doing copy region
pattern: smallest pattern of elements eg. [1,1,0,2]
pickWich: sections corresponding this number in the pattern is picked up
"""
if stop is None:
stop = self.nt
if start is None:
start = 0
unit = len(pattern)
idx = N.arange(self.nt)
repeat = N.ceil(self.nt / float(unit))
repPat = N.tile(pattern, repeat)[:len(idx)]
ids = N.compress(repPat == pickWhich, idx)
self._tIdx = [i for i in ids if i >= start and i < stop]
self._tPtrn = pattern
self._tPick = pickWhich
def logpolar(image, center=None, angles=None, radii=None):
"""Return log-polar transformed image and log base."""
shape = image.shape
if center is None:
center = shape[0] / 2, shape[1] / 2
if angles is None:
angles = shape[0]
if radii is None:
radii = shape[1]
theta = N.zeros((angles, radii), dtype=N.float64)
theta.T[:] = -N.linspace(0, N.pi, angles, endpoint=False)
#d = radii
d = N.hypot(shape[0]-center[0], shape[1]-center[1])
log_base = 10.0 ** (N.log10(d) / (radii))
radius = N.empty_like(theta)
radius[:] = N.power(log_base, N.arange(radii,
dtype=N.float64)) - 1.0
x = radius * N.sin(theta) + center[0]
y = radius * N.cos(theta) + center[1]
output = N.zeros_like(x)
ndii.map_coordinates(image, [x, y], output=output)
return output, log_base
