def shift3d(data, vt=0.0, ht=0.0, crop=True, _map=map):
'''
Shift 2-D all images
vt: float or sequence
ht: float or sequence
'''
if not isvector(vt):
vt_val = vt
vt = np.empty(data.shape[0], dtype=data.dtype)
vt.fill(vt_val)
if not isvector(ht):
ht_val = ht
ht = np.empty(data.shape[0], dtype=data.dtype)
ht.fill(ht_val)
pos = np.vstack((vt, ht)).T
map_obj = _map(ndshift, data, pos)
ret = fromiter(map_obj)
if crop:
return ret[np.index_exp[:]+crop_index(vt)+crop_index(ht)]
else:
return ret
def valign(data, _map=map):
'''
data: 3d volume data (0:2pi, top:bottom, left:right)
'''
map_obj = _map(corr2d, data, data[1:])
res = fromiter(map_obj, dtype=data.dtype)
res = np.cumsum(res.T[0])
res = -1.0 * np.insert(res, 0, 0)
return res
def valign2(data, _map=map):
'''
intensity sum
'''
d = np.sum(np.abs(data), axis=2) # axis 2 is left-right
map_obj = _map(corr1d, d, d[1:])
res = fromiter(map_obj, dtype=data.dtype)
res = np.cumsum(res)
res = -1.0 * np.insert(res, 0, 0)
return res
def shift2d(data, t=0.0):
'''
t: float or sequence
'''
ret = None
if isvector(t):
map_obj = map(ndshift, data, t)
ret = fromiter(map_obj)
else:
ret = ndshift(data, (0.0, t))
return ret
def center(data, bg, _map=map):
'''
Deprecated.
'''
if len(bg.shape) == 3:
_bg = np.average(bg, axis=0)
elif len(bg.shape) == 2:
_bg = bg
else:
raise TypeError('background should be 2d or 3d array')
map_obj = _map(lambda im: corr2d(_bg, im), data)
return fromiter(map_obj, dtype=data.dtype)
def power(data, index, _map=map):
'''
data:
index: index_exp. 2d. empty.
return normalised data
'''
# index must be tuple
if np.prod(data[np.index_exp[:] + index].shape) == 0:
raise ValueError('Cannot average. array shape')
map_obj = map(np.average, data[np.index_exp[:] + index])
iavg = fromiter(map_obj, dtype=data.dtype) # intensity average of each image
iavg = iavg - np.average(iavg) # distance from all average
return iavg
def norm_all(data, bg, dk=None, beam_power=None, beam_center=None, crop=True, _map=map):
'''
data: 3d
bg: 2d
dk: 2d
beam_power: sequence of intensity values
beam_center: sequence of [(yt0, xt0), (yt1, xt1) ...]
'''
data.flags.writeable = False
bg.flags.writeable = False
if dk is not None:
dk.flags.writeable = False
normfunc = set_normfunc(bg, dk=dk)
bp = [None]*data.shape[0] if beam_power is None else beam_power
bc = [None]*data.shape[0] if beam_center is None else beam_center
map_obj = _map(normfunc, data, bp, bc)
res = fromiter(map_obj, dtype=data.dtype)
if beam_center is not None and crop:
return res[np.index_exp[:]+crop_index(bc.T[0])+crop_index(bc.T[1])]
else:
return res
def recon3d(sinograms, method='fbp', _map=map):
mapobj = _map(lambda sg: iradon(sg.T, circle=True), sinograms)
return fromiter(mapobj)
