def _smoothBorder(arr, start, stop, smooth, value):
"""
start, stop: [z,y,x]
"""
# prepare coordinates
shape = N.array(arr.shape)
start = N.ceil(start).astype(N.int16)
stop = N.ceil(stop).astype(N.int16)
smooth_start = start - smooth
smooth_stop = stop + smooth
smooth_start = N.where(smooth_start < 0, 0, smooth_start)
smooth_stop = N.where(smooth_stop > shape, shape, smooth_stop)
#print smooth_start, smooth_stop
import copy
sliceTemplate = [slice(None, None, None)] * arr.ndim
shapeTemplate = list(shape)
for d in range(arr.ndim):
smooth_shape = shapeTemplate[:d] + shapeTemplate[d + 1:]
# make an array containing the edge value
edges = N.empty([2] + smooth_shape, N.float32)
# start side
slc = copy.copy(sliceTemplate)
slc[d] = slice(start[d], start[d] + 1, None)
edges[0] = arr[slc].reshape(smooth_shape)
# stop side
slc = copy.copy(sliceTemplate)
slc[d] = slice(stop[d] - 1, stop[d], None)
edges[1] = arr[slc].reshape(smooth_shape)
edges = (edges - value) / float(
smooth + 1) # this value can be array??
# both side
for s, side in enumerate([start, stop]):
if s == 0:
rs = list(range(smooth_start[d], start[d]))
rs.sort(reverse=True)
elif s == 1:
rs = list(range(stop[d], smooth_stop[d]))
# smoothing
for f, i in enumerate(rs):
slc = copy.copy(sliceTemplate)
slc[d] = slice(i, i + 1, None)
edgeArr = edges[s].reshape(arr[slc].shape)
#arr[slc] += edgeArr * (smooth - f)
arr[slc] = arr[slc] + edgeArr * (smooth - f) # casting rule
arr = N.ascontiguousarray(arr)
return arr
def _smoothBorder(arr, start, stop, smooth, value):
"""
start, stop: [z,y,x]
"""
# prepare coordinates
shape = N.array(arr.shape)
start = N.ceil(start).astype(N.int16)
stop = N.ceil(stop).astype(N.int16)
smooth_start = start - smooth
smooth_stop = stop + smooth
smooth_start = N.where(smooth_start < 0, 0, smooth_start)
smooth_stop = N.where(smooth_stop > shape, shape, smooth_stop)
#print smooth_start, smooth_stop
import copy
sliceTemplate = [slice(None,None,None)] * arr.ndim
shapeTemplate = list(shape)
for d in range(arr.ndim):
smooth_shape = shapeTemplate[:d] + shapeTemplate[d+1:]
# make an array containing the edge value
edges = N.empty([2] + smooth_shape, N.float32)
# start side
slc = copy.copy(sliceTemplate)
slc[d] = slice(start[d], start[d]+1, None)
edges[0] = arr[slc].reshape(smooth_shape)
# stop side
slc = copy.copy(sliceTemplate)
slc[d] = slice(stop[d]-1, stop[d], None)
edges[1] = arr[slc].reshape(smooth_shape)
edges = (edges - value) / float(smooth + 1) # this value can be array??
# both side
for s, side in enumerate([start, stop]):
if s == 0:
rs = list(range(smooth_start[d], start[d]))
rs.sort(reverse=True)
elif s == 1:
rs = list(range(stop[d], smooth_stop[d]))
# smoothing
for f,i in enumerate(rs):
slc = copy.copy(sliceTemplate)
slc[d] = slice(i,i+1,None)
edgeArr = edges[s].reshape(arr[slc].shape)
#arr[slc] += edgeArr * (smooth - f)
arr[slc] = arr[slc] + edgeArr * (smooth - f) # casting rule
arr = N.ascontiguousarray(arr)
return arr
def img2polar2D(img,
center,
final_radius=None,
initial_radius=None,
phase_width=360,
return_idx=False):
"""
img: array
center: coordinate y, x
final_radius: ending radius
initial_radius: starting radius
phase_width: npixles / circle
return_idx: return transformation coordinates (y,x)
"""
if img.ndim > 2 or len(center) > 2:
raise ValueError(
'this function only support 2D, you entered %i-dim array and %i-dim center coordinate'
% (img.ndim, len(center)))
if initial_radius is None:
initial_radius = 0
if final_radius is None:
rad0 = N.ceil(N.array(img.shape) - center)
final_radius = min((int(min(rad0)), int(min(N.ceil(center)))))
if phase_width is None:
phase_width = N.sum(img.shape[-2:]) * 2
theta, R = np.meshgrid(np.linspace(0, 2 * np.pi, phase_width),
np.arange(initial_radius, final_radius))
Ycart, Xcart = polar2cart2D(R, theta, center)
Ycart = N.where(Ycart >= img.shape[0], img.shape[0] - 1, Ycart)
Xcart = N.where(Xcart >= img.shape[1], img.shape[1] - 1, Xcart)
Ycart = Ycart.astype(int)
Xcart = Xcart.astype(int)
polar_img = img[Ycart, Xcart]
polar_img = np.reshape(polar_img,
(final_radius - initial_radius, phase_width))
if return_idx:
return polar_img, Ycart, Xcart
else:
return polar_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 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 mask_gaussianND(arr, zyx, v, sigma=2., ret=None, rot=0, clipZero=True):
'''
subtract elliptical gaussian at y,x with peakVal v
if ret, return arr, else, arr itself is edited
'''
from . import imgGeo
zyx = N.asarray(zyx)
ndim = arr.ndim
shape = N.array(arr.shape)
try:
if len(sigma) != ndim:
raise ValueError('len(sigma) must be the same as len(shape)')
else:
sigma = N.asarray(sigma)
except TypeError:#(TypeError, ValueError):
sigma = N.asarray([sigma]*ndim)
# prepare small window
slc = imgGeo.nearbyRegion(shape, N.floor(zyx), sigma * 10)
inds, LD = imgFit.rotateIndicesND(slc, dtype=N.float32, rot=rot)
param = (0, v,) + tuple(zyx) + tuple(sigma)
sidx = 2 + ndim
g = imgFit.yGaussianND(N.asarray(param), inds, sidx).astype(arr.dtype.type)
roi = arr[slc]
if clipZero:
g = N.where(g > roi, roi, g)
if ret:
e = N.zeros_like(arr)
e[slc] = g # this may be faster than copy()
return arr - e
else:
arr[slc] -= g
def nanFilter(af, kernel=3):
"""
3D phase contrast filter often creates 'nan'
this filter removes nan by averaging surrounding pixels
return af
"""
af = af.copy()
shape = N.array(af.shape)
radius = N.subtract(kernel, 1) // 2
box = kernel * af.ndim
nan = N.isnan(af)
nids = N.array(N.nonzero(nan)).T
for nidx in nids:
slc = [slice(idx,idx+1) for idx in nidx]
slices = []
for dim in range(af.ndim):
slc2 = slice(slc[dim].start - radius, slc[dim].stop + radius)
while slc2.start < 0:
slc2 = slice(slc2.start + 1, slc2.stop)
while slc2.stop > shape[dim]:
slc2 = slice(slc2.start, slc2.stop -1)
slices.append(slc2)
val = af[slices]
nanlocal = N.isnan(val)
ss = N.sum(N.where(nanlocal, 0, val)) / float((box - N.sum(nanlocal)))
af[slc] = ss
return af
def nanFilter(af, kernel=3):
"""
3D phase contrast filter often creates 'nan'
this filter removes nan by averaging surrounding pixels
return af
"""
af = af.copy()
shape = N.array(af.shape)
radius = N.subtract(kernel, 1) // 2
box = kernel * af.ndim
nan = N.isnan(af)
nids = N.array(N.nonzero(nan)).T
for nidx in nids:
slc = [slice(idx, idx + 1) for idx in nidx]
slices = []
for dim in range(af.ndim):
slc2 = slice(slc[dim].start - radius, slc[dim].stop + radius)
while slc2.start < 0:
slc2 = slice(slc2.start + 1, slc2.stop)
while slc2.stop > shape[dim]:
slc2 = slice(slc2.start, slc2.stop - 1)
slices.append(slc2)
val = af[slices]
nanlocal = N.isnan(val)
ss = N.sum(N.where(nanlocal, 0, val)) / float((box - N.sum(nanlocal)))
af[slc] = ss
return af
def img2polar2D(img, center, final_radius=None, initial_radius = None, phase_width = 360, return_idx=False):
"""
img: array
center: coordinate y, x
final_radius: ending radius
initial_radius: starting radius
phase_width: npixles / circle
return_idx: return transformation coordinates (y,x)
"""
if img.ndim > 2 or len(center) > 2:
raise ValueError('this function only support 2D, you entered %i-dim array and %i-dim center coordinate' % (img.ndim, len(center)))
if initial_radius is None:
initial_radius = 0
if final_radius is None:
rad0 = N.ceil(N.array(img.shape) - center)
final_radius = min((int(min(rad0)), int(min(N.ceil(center)))))
if phase_width is None:
phase_width = N.sum(img.shape[-2:]) * 2
theta , R = np.meshgrid(np.linspace(0, 2*np.pi, phase_width),
np.arange(initial_radius, final_radius))
Ycart, Xcart = polar2cart2D(R, theta, center)
Ycart = N.where(Ycart >= img.shape[0], img.shape[0]-1, Ycart)
Xcart = N.where(Xcart >= img.shape[1], img.shape[1]-1, Xcart)
Ycart = Ycart.astype(int)
Xcart = Xcart.astype(int)
polar_img = img[Ycart,Xcart]
polar_img = np.reshape(polar_img,(final_radius-initial_radius,phase_width))
if return_idx:
return polar_img, Ycart, Xcart
else:
return polar_img
def keepShape(a, shape, difmod=None):
canvas = N.zeros(shape, a.dtype.type)
if difmod is None:
dif = (shape - N.array(a.shape, N.float32)) / 2.
mod = N.ceil(N.mod(dif, 1))
else:
dif, mod = difmod
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]
if difmod is None:
return canvas, mod
else:
return canvas
def keepShape(a, shape, difmod=None):
canvas = N.zeros(shape, a.dtype.type)
if difmod is None:
dif = (shape - N.array(a.shape, N.float32)) / 2.
mod = N.ceil(N.mod(dif, 1))
else:
dif, mod = difmod
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]
if difmod is None:
return canvas, mod
else:
return canvas
def mask_gaussianND(arr, zyx, v, sigma=2., ret=None, rot=0, clipZero=True):
'''
subtract elliptical gaussian at y,x with peakVal v
if ret, return arr, else, arr itself is edited
'''
from . import imgGeo
zyx = N.asarray(zyx)
ndim = arr.ndim
shape = N.array(arr.shape)
try:
if len(sigma) != ndim:
raise ValueError('len(sigma) must be the same as len(shape)')
else:
sigma = N.asarray(sigma)
except TypeError: #(TypeError, ValueError):
sigma = N.asarray([sigma] * ndim)
# prepare small window
slc = imgGeo.nearbyRegion(shape, N.floor(zyx), sigma * 10)
inds, LD = imgFit.rotateIndicesND(slc, dtype=N.float32, rot=rot)
param = (
0,
v,
) + tuple(zyx) + tuple(sigma)
sidx = 2 + ndim
g = imgFit.yGaussianND(N.asarray(param), inds, sidx).astype(arr.dtype.type)
roi = arr[slc]
if clipZero:
g = N.where(g > roi, roi, g)
if ret:
e = N.zeros_like(arr)
e[slc] = g # this may be faster than copy()
return arr - e
else:
arr[slc] -= g
def rotateIndicesND(slicelist, dtype=N.float64, rot=0, mode=2):
"""
slicelist: even shape works much better than odd shape
rot: counter-clockwise, xy-plane
mode: testing different ways of doing, (1 or 2 and the same result)
return inds, LD
"""
global INDS_DIC
shape = []
LD = []
for sl in slicelist:
if isinstance(sl, slice):
shape.append(sl.stop - sl.start)
LD.append(sl.start)
shapeTuple = tuple(shape + [rot])
if shapeTuple in INDS_DIC:
inds = INDS_DIC[shapeTuple]
else:
shape = N.array(shape)
ndim = len(shape)
odd_even = shape % 2
s2 = N.ceil(shape * (2**0.5))
if mode == 1: # everything is even
s2 = N.where(s2 % 2, s2 + 1, s2)
elif mode == 2: # even & even or odd & odd
for d, s in enumerate(shape):
if (s % 2 and not s2[d] % 2) or (not s % 2 and s2[d] % 2):
s2[d] += 1
cent = s2 / 2.
dif = (s2 - shape) / 2.
dm = dif % 1
# print s2, cent, dif, dm
slc = [Ellipsis] + [
slice(int(d),
int(d) + shape[i]) for i, d in enumerate(dif)
]
# This slice is float which shift array when cutting out!!
s2 = tuple([int(ss)
for ss in s2]) # numpy array cannot use used for slice
inds = N.indices(s2, N.float32)
ind_shape = inds.shape
nz = N.product(ind_shape[:-2])
nsec = nz / float(ndim)
if ndim > 2:
inds = N.reshape(inds, (nz, ) + ind_shape[-2:])
irs = N.empty_like(inds)
for d, ind in enumerate(inds):
idx = int(d // nsec)
c = cent[idx]
if rot and inds.ndim > 2:
U.trans2d(ind - c, irs[d], (0, 0, rot, 1, 0, 1))
irs[d] += c - dif[idx]
else:
irs[d] = ind - dif[idx]
if len(ind_shape) > 2:
irs = N.reshape(irs, ind_shape)
irs = irs[slc]
if mode == 1 and N.sometrue(dm):
inds = N.empty_like(irs)
# print 'translate', dm
for d, ind in enumerate(irs):
U.trans2d(ind, inds[d], (-dm[1], -dm[0], 0, 1, 0, 1))
else:
inds = irs
INDS_DIC[shapeTuple] = inds
r_inds = N.empty_like(inds)
for d, ld in enumerate(LD):
r_inds[d] = inds[d] + ld
return r_inds, LD
def Xcorr(a, b, highpassSigma=2.5, wiener=0.2, cutoffFreq=3,
forceSecondPeak=None, acceptOrigin=True, maskSigmaFact=1., removeY=None, removeX=None, ret=None, normalize=True, gFit=True, lap=None, win=11):
"""
returns (y,x), image
if ret is True, returns [v, yx, image]
to get yx cordinate of the image,
yx += N.divide(picture.shape, 2)
a, b: 2D array
highpassSigma: sigma value used for highpass pre-filter
wiener: wiener value used for highpass pre-filter
cutoffFreq: kill lowest frequency component from 0 to this level
forceSecondPeak: If input is n>0 (True is 1), pick up n-th peak
acceptOrigin: If None, result at origin is rejected, look for the next peak
maskSigmaFact: Modifier to remove previous peak to look for another peak
removeYX: Rremove given number of pixel high intensity lines of the Xcorr
Y: Vertical, X: Horizontal
normalize: intensity normalized
gFit: peak is fitted to 2D gaussian array, if None use center of mass
win: window for gFit
if b is a + (y,x) then, answer is (-y,-x)
"""
shapeA = N.asarray(a.shape)
shapeB = N.asarray(b.shape)
shapeM = N.max([shapeA, shapeB], axis=0)
shapeM = N.where(shapeM % 2, shapeM+1, shapeM)
center = shapeM / 2.
arrs = [a,b]
arrsS = ['a','b']
arrsF = []
for i, arr in enumerate(arrs):
if arr.dtype not in [N.float32, N.float64]:
arr = N.asarray(arr, N.float32)
# this convolution has to be done beforehand to remove 2 pixels at the edge
if lap == 'nothing':
pass
elif lap:
arr = arr_Laplace(arr, mask=2)
else:
arr = arr_sorbel(arr, mask=1)
if N.sometrue(shapeA < shapeM):
arr = paddingMed(arr, shapeM)
if normalize:
mi, ma, me, sd = U.mmms(arr)
arr = (arr - me) / sd
if i ==1:
arr = F.shift(arr)
af = F.rfft(arr)
af = highPassF(af, highpassSigma, wiener, cutoffFreq)
arrsF.append(af)
# start cross correlation
af, bf = arrsF
bf = bf.conjugate()
cf = af * bf
# go back to space domain
c = F.irfft(cf)
# c = _changeOrigin(cr)
# removing lines
if removeX:
yi, xi = N.indices((removeX, shapeM[-1]))#sx))
yi += center[-2] - removeX/2.#sy/2 - removeX/2
c[yi, xi] = 0
if removeY:
yi, xi = N.indices((shapeM[-2], removeY))#sy, removeY))
xi += center[-1] - removeY/2.#sx/2 - removeY/2
c[yi, xi] = 0
# find the first peak
if gFit:
v, yx, s = findMaxWithGFit(c, win=win)#, window=win, gFit=gFit)
if v == 0:
v, yx, s = findMaxWithGFit(c, win=win+2)#, window=win+2, gFit=gFit)
if v == 0:
v = U.findMax(c)[0]
yx = N.add(yx, 0.5)
#yx += 0.5
else:
vzyx = U.findMax(c)
v = vzyx[0]
yx = vzyx[-2:]
s = 2.5
yx -= center
if N.alltrue(N.abs(yx) < 1.0) and not acceptOrigin:
forceSecondPeak = True
# forceSecondPeak:
if not forceSecondPeak:
forceSecondPeak = 0
for i in range(int(forceSecondPeak)):
print('%i peak was removed' % (i+1)) #, sigma: %.2f' % (i+1, s)
yx += center
g = gaussianArr2D(c.shape, sigma=s/maskSigmaFact, peakVal=v, orig=yx)
c = c - g
#c = mask_gaussian(c, yx[0], yx[1], v, s)
if gFit:
v, yx, s = findMaxWithGFit(c, win=win)#, window=win, gFit=gFit)
if v == 0:
v, yx, s = findMaxWithGFit(c, win=win+2)#, window=win+2, gFit=gFit)
if v == 0:
v = U.findMax(c)[0]
yx -= (center - 0.5)
else:
vzyx = U.findMax(c)
v = vzyx[0]
if not gFit:
yx = centerOfMass(c, vzyx[-2:]) - center
if lap is not 'nothing':
c = paddingValue(c, shapeM+2)
if ret == 2:
return yx, af, bf.conjugate()
elif ret:
return v, yx, c
else:
return yx, c
def pointsCutOut3D(arr, posList, windowSize=100, d2=None, interpolate=True, removeWrongShape=True):
"""
array: nd array
posList: ([(z,)y,x]...)
windowSize: scalar (in pixel or as percent < 1.) or ((z,)y,x)
if arr.ndim > 2, and len(windowSize) == 2, then
cut out section-wise (higher dimensions stay the same)
d2: conern only XY of windowSize (higher dimensions stay the same)
interpolate: shift array by subpixel interpolation to adjust center
return: list of array centered at each pos in posList
"""
shape = N.array(arr.shape)
center = shape / 2.
if arr.ndim <= 2:
ndim_arr = arr.ndim
else:
ndim_arr = 3
# prepare N-dimensional window size
try:
len(windowSize) # seq
if d2:
windowSize = windowSize[-2:]
if len(windowSize) != arr.ndim:
dim = len(windowSize)
windowSize = tuple(shape[:-dim]) + tuple(windowSize)
except TypeError: # scaler
if windowSize < 1 and windowSize > 0: # percentage
w = shape * windowSize
if d2:
w[:-2] = shape[:-2]
elif ndim_arr > 3:
w[:-3] = shape[:-3]
windowSize = w.astype(N.uint)
else:
windowSize = N.where(shape >= windowSize, windowSize, shape)
if d2:
windowSize[:-2] = shape[:-2]
windowSize = N.asarray(windowSize)
halfWin = windowSize / 2
#ndim_win = len(windowSize)
margin = int(interpolate)
# cutout individual position
arrList = []
for pos in posList:
ndim_pos = len(pos)
# calculate idx
dif = pos +0.5 - halfWin[-ndim_pos:]
dif = N.round_(dif)
dif = dif.astype(N.int)
starts = dif - margin
starts = N.where(starts < 0, 0, starts)
stops = dif + windowSize[-ndim_pos:] + margin
stops = N.where(stops > shape[-ndim_pos:], shape[-ndim_pos:], stops)
#if removeWrongShape and (N.any((starts) < 0) or N.any((stops) > shape[-ndim_pos:])):
# continue
slc = [Ellipsis]
for dim, s0 in enumerate(starts):
s1 = stops[dim]
slc.append(slice(s0, s1))
cpa = arr[slc]
if interpolate:
dif = pos + 0.5 - halfWin[-ndim_pos:]
sub = (arr.ndim - len(dif)) * [0] + list(dif)
sub = N.array(sub)
sar = U.nd.shift(cpa, sub % 1)
slc = [Ellipsis]
for d in range(ndim_arr):
slc.append(slice(margin, -margin))
cpa = sar[slc]
arrList.append(cpa)
if removeWrongShape:
if d2 and arr.ndim == 2:
arrList = N.array([a for a in arrList if N.all(a.shape[-2:] == windowSize[-2:])])
else:
arrList = N.array([a for a in arrList if N.all(a.shape[-3:] == windowSize[-3:])])
return arrList
def pointsCutOutND(arr, posList, windowSize=100, sectWise=None, interpolate=True):
"""
array: nd array
posList: ([(z,)y,x]...)
windowSize: scalar (in pixel or as percent < 1.) or ((z,)y,x)
if arr.ndim > 2, and len(windowSize) == 2, then
cut out section-wise (higher dimensions stay the same)
sectWise: conern only XY of windowSize (higher dimensions stay the same)
interpolate: shift array by subpixel interpolation to adjust center
return: list of array centered at each pos in posList
"""
shape = N.array(arr.shape)
center = shape / 2.
# prepare N-dimensional window size
try:
len(windowSize) # seq
if sectWise:
windowSize = windowSize[-2:]
if len(windowSize) != arr.ndim:
dim = len(windowSize)
windowSize = tuple(shape[:-dim]) + tuple(windowSize)
except TypeError: # scaler
if windowSize < 1 and windowSize > 0: # percentage
w = shape * windowSize
if sectWise:
w[:-2] = shape[:-2]
windowSize = w.astype(N.uint16)
else:
windowSize = N.where(shape >= windowSize, windowSize, shape)
if sectWise:
windowSize = arr.shape[:-2] + tuple(windowSize[-2:])
windowSize = N.asarray(windowSize)
# cutout individual position
arrList=[]
for pos in posList:
# prepare N-dimensional coordinate
n = len(pos)
if n != len(windowSize):
temp = center.copy()
center[-n:] = pos
pos = center
# calculate idx
ori = pos - (windowSize / 2.) # float value
oidx = N.ceil(ori) # idx
subpxl = oidx - ori # subpixel mod
if interpolate and N.sometrue(subpxl): # comit to make shift
SHIFT = 1
else:
SHIFT = 0
# prepare slice
# when comitted to make shift, first cut out window+1,
# then make subpixle shift, and then cutout 1 edge
slc = [Ellipsis] # Ellipsis is unnecessary, just in case...
slc_edge = [slice(1,-1,None)] * arr.ndim
for d in range(arr.ndim):
start = oidx[d] - SHIFT
if start < 0:
start = 0
slc_edge[d] = slice(0, slc_edge[d].stop, None)
stop = oidx[d] + windowSize[d] + SHIFT
if stop > shape[d]:
stop = shape[d]
slc_edge[d] = slice(slc_edge[d].start, shape[d], None)
slc += [slice(int(start), int(stop), None)]
# cutout, shift and cutout
#print(slc, slc_edge)
try:
canvas = arr[slc]
if SHIFT:
# 20180214 subpixel shift +0.5 was fixed
#raise RuntimeError('check')
if sectWise:
subpxl[-2:] = N.where(windowSize[-2:] > 1, subpxl[-2:]-0.5, subpxl[-2:])
else:
subpxl[-n:] = N.where(windowSize[-n:] > 1, subpxl[-n:]-0.5, subpxl[-n:])
canvas = U.nd.shift(canvas, subpxl)
canvas = canvas[slc_edge]
check = 1
except IndexError:
print('position ', pos, ' was skipped')
check = 0
raise
if check:
arrList += [N.ascontiguousarray(canvas)]
return arrList
def pointsCutOutND(arr,
posList,
windowSize=100,
sectWise=None,
interpolate=True):
"""
array: nd array
posList: ([(z,)y,x]...)
windowSize: scalar (in pixel or as percent < 1.) or ((z,)y,x)
if arr.ndim > 2, and len(windowSize) == 2, then
cut out section-wise (higher dimensions stay the same)
sectWise: conern only XY of windowSize (higher dimensions stay the same)
interpolate: shift array by subpixel interpolation to adjust center
return: list of array centered at each pos in posList
"""
shape = N.array(arr.shape)
center = shape / 2.
# prepare N-dimensional window size
try:
len(windowSize) # seq
if sectWise:
windowSize = windowSize[-2:]
if len(windowSize) != arr.ndim:
dim = len(windowSize)
windowSize = tuple(shape[:-dim]) + tuple(windowSize)
except TypeError: # scaler
if windowSize < 1 and windowSize > 0: # percentage
w = shape * windowSize
if sectWise:
w[:-2] = shape[:-2]
windowSize = w.astype(N.uint16)
else:
windowSize = N.where(shape >= windowSize, windowSize, shape)
if sectWise:
windowSize = arr.shape[:-2] + windowSize[-2:]
windowSize = N.asarray(windowSize)
# cutout individual position
arrList = []
for pos in posList:
# prepare N-dimensional coordinate
n = len(pos)
if n != len(windowSize):
temp = center.copy()
center[-n:] = pos
pos = center
# calculate idx
ori = pos - (windowSize / 2.) # float value
oidx = N.ceil(ori) # idx
subpxl = oidx - ori # subpixel mod
if interpolate and N.sometrue(subpxl): # comit to make shift
SHIFT = 1
else:
SHIFT = 0
# prepare slice
# when comitted to make shift, first cut out window+1,
# then make subpixle shift, and then cutout 1 edge
slc = [Ellipsis] # Ellipsis is unnecessary, just in case...
slc_edge = [slice(1, -1, None)] * arr.ndim
for d in range(arr.ndim):
start = oidx[d] - SHIFT
if start < 0:
start = 0
slc_edge[d] = slice(0, slc_edge[d].stop, None)
stop = oidx[d] + windowSize[d] + SHIFT
if stop > shape[d]:
stop = shape[d]
slc_edge[d] = slice(slc_edge[d].start, shape[d], None)
slc += [slice(int(start), int(stop), None)]
# cutout, shift and cutout
try:
canvas = arr[slc]
if SHIFT:
canvas = U.nd.shift(canvas, subpxl)
canvas = canvas[slc_edge]
check = 1
except IndexError:
print('position ', pos, ' was skipped')
check = 0
raise
if check:
arrList += [N.ascontiguousarray(canvas)]
return arrList
def getShift(shift, ZYX, erosionZYX=0):
"""
shift: zyxrmm
return [zmin,zmax,ymin,ymax,xmin,xmax]
"""
# erosion
try:
if len(erosionZYX) == 3:
erosionZ = erosionZYX[0]
erosionYX = erosionZYX[1:]
elif len(erosionZYX) == 2:
erosionZ = 0
erosionYX = erosionZYX
elif len(erosionZYX) == 1:
erosionZ = 0
erosionYX = erosionZYX[0]
except TypeError: # scalar
erosionZ = erosionZYX
erosionYX = erosionZYX
# magnification
magZYX = N.ones((3, ), N.float32)
magZYX[3 - len(shift[4:]):] = shift[4:]
if len(shift[4:]) == 1:
magZYX[1] = shift[4]
# rotation
r = shift[3]
# target shape
ZYX = N.asarray(ZYX, N.float32)
ZYXm = ZYX * magZYX
# Z
z = N.where(shift[0] < 0, N.floor(shift[0]), N.ceil(shift[0]))
ztop = ZYXm[0] + z
nz = N.ceil(N.where(ztop > ZYX[0], ZYX[0], ztop))
z += erosionZ
nz -= erosionZ
if z < 0:
z = 0
if nz < 0:
nz = z + 1
zyx0 = N.ceil((ZYX - ZYXm) / 2.)
#print zyx0
#if zyx0[0] > 0:
# z -= zyx0[0]
# nz += zyx0[0]
zs = N.array([z, nz])
# YX
#try:
# if len(erosionYX) != 2:
# raise ValueError, 'erosion is only applied to lateral dimension'
#except TypeError:
# erosionYX = (erosionYX, erosionYX)
yxShift = N.where(shift[1:3] < 0, N.floor(shift[1:3]), N.ceil(shift[1:3]))
# rotate the magnified center
xyzm = N.ceil(ZYXm[::-1]) / 2.
xyr = imgGeo.RotateXY(xyzm[:-1], r)
xyr -= xyzm[:-1]
yx = xyr[::-1]
leftYX = N.ceil(N.abs(yx))
rightYX = -N.ceil(N.abs(yx))
# then translate
leftYXShift = (leftYX + yxShift) + zyx0[1:]
leftYXShift = N.where(leftYXShift < 0, 0, leftYXShift)
rightYXShift = (rightYX + yxShift) - zyx0[1:]
YXmax = N.where(ZYXm[1:] > ZYX[1:], ZYXm[1:], ZYX[1:])
rightYXShift = N.where(rightYXShift > 0, YXmax,
rightYXShift + YXmax) # deal with - idx
rightYXShift = N.where(rightYXShift > ZYX[1:], ZYX[1:], rightYXShift)
leftYXShift += erosionYX
rightYXShift -= erosionYX
# (z0,z1,y0,y1,x0,x1)
tempZYX = N.array(
(zs[0], zs[1], int(N.ceil(leftYXShift[0])), int(rightYXShift[0]),
int(N.ceil(leftYXShift[1])), int(rightYXShift[1])))
return tempZYX
def Xcorr(a,
b,
highpassSigma=2.5,
wiener=0.2,
cutoffFreq=3,
forceSecondPeak=None,
acceptOrigin=True,
maskSigmaFact=1.,
removeY=None,
removeX=None,
ret=None,
normalize=True,
gFit=True,
lap=None,
win=11):
"""
returns (y,x), image
if ret is True, returns [v, yx, image]
to get yx cordinate of the image,
yx += N.divide(picture.shape, 2)
a, b: 2D array
highpassSigma: sigma value used for highpass pre-filter
wiener: wiener value used for highpass pre-filter
cutoffFreq: kill lowest frequency component from 0 to this level
forceSecondPeak: If input is n>0 (True is 1), pick up n-th peak
acceptOrigin: If None, result at origin is rejected, look for the next peak
maskSigmaFact: Modifier to remove previous peak to look for another peak
removeYX: Rremove given number of pixel high intensity lines of the Xcorr
Y: Vertical, X: Horizontal
normalize: intensity normalized
gFit: peak is fitted to 2D gaussian array, if None use center of mass
win: window for gFit
if b is a + (y,x) then, answer is (-y,-x)
"""
shapeA = N.asarray(a.shape)
shapeB = N.asarray(b.shape)
shapeM = N.max([shapeA, shapeB], axis=0)
shapeM = N.where(shapeM % 2, shapeM + 1, shapeM)
center = shapeM / 2.
arrs = [a, b]
arrsS = ['a', 'b']
arrsF = []
for i, arr in enumerate(arrs):
if arr.dtype not in [N.float32, N.float64]:
arr = N.asarray(arr, N.float32)
# this convolution has to be done beforehand to remove 2 pixels at the edge
if lap == 'nothing':
pass
elif lap:
arr = arr_Laplace(arr, mask=2)
else:
arr = arr_sorbel(arr, mask=1)
if N.sometrue(shapeA < shapeM):
arr = paddingMed(arr, shapeM)
if normalize:
mi, ma, me, sd = U.mmms(arr)
arr = (arr - me) / sd
if i == 1:
arr = F.shift(arr)
af = F.rfft(arr)
af = highPassF(af, highpassSigma, wiener, cutoffFreq)
arrsF.append(af)
# start cross correlation
af, bf = arrsF
bf = bf.conjugate()
cf = af * bf
# go back to space domain
c = F.irfft(cf)
# c = _changeOrigin(cr)
# removing lines
if removeX:
yi, xi = N.indices((removeX, shapeM[-1])) #sx))
yi += center[-2] - removeX / 2. #sy/2 - removeX/2
c[yi, xi] = 0
if removeY:
yi, xi = N.indices((shapeM[-2], removeY)) #sy, removeY))
xi += center[-1] - removeY / 2. #sx/2 - removeY/2
c[yi, xi] = 0
# find the first peak
if gFit:
v, yx, s = findMaxWithGFit(c, win=win) #, window=win, gFit=gFit)
if v == 0:
v, yx, s = findMaxWithGFit(c, win=win +
2) #, window=win+2, gFit=gFit)
if v == 0:
v = U.findMax(c)[0]
yx = N.add(yx, 0.5)
#yx += 0.5
else:
vzyx = U.findMax(c)
v = vzyx[0]
yx = vzyx[-2:]
s = 2.5
yx -= center
if N.alltrue(N.abs(yx) < 1.0) and not acceptOrigin:
forceSecondPeak = True
# forceSecondPeak:
if not forceSecondPeak:
forceSecondPeak = 0
for i in range(int(forceSecondPeak)):
print('%i peak was removed' % (i + 1)) #, sigma: %.2f' % (i+1, s)
yx += center
g = gaussianArr2D(c.shape, sigma=s / maskSigmaFact, peakVal=v, orig=yx)
c = c - g
#c = mask_gaussian(c, yx[0], yx[1], v, s)
if gFit:
v, yx, s = findMaxWithGFit(c, win=win) #, window=win, gFit=gFit)
if v == 0:
v, yx, s = findMaxWithGFit(c, win=win +
2) #, window=win+2, gFit=gFit)
if v == 0:
v = U.findMax(c)[0]
yx -= (center - 0.5)
else:
vzyx = U.findMax(c)
v = vzyx[0]
if not gFit:
yx = centerOfMass(c, vzyx[-2:]) - center
if lap is not 'nothing':
c = paddingValue(c, shapeM + 2)
if ret == 2:
return yx, af, bf.conjugate()
elif ret:
return v, yx, c
else:
return yx, c
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 arr_log(arr):
logArr = N.log(arr)
return N.where(logArr < 0, 0, logArr)
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 arr_log(arr):
logArr = N.log(arr)
return N.where(logArr < 0, 0, logArr)
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 rotateIndicesND(slicelist, dtype=N.float64, rot=0, mode=2):
"""
slicelist: even shape works much better than odd shape
rot: counter-clockwise, xy-plane
mode: testing different ways of doing, (1 or 2 and the same result)
return inds, LD
"""
global INDS_DIC
shape = []
LD = []
for sl in slicelist:
if isinstance(sl, slice):
shape.append(sl.stop - sl.start)
LD.append(sl.start)
shapeTuple = tuple(shape+[rot])
if shapeTuple in INDS_DIC:
inds = INDS_DIC[shapeTuple]
else:
shape = N.array(shape)
ndim = len(shape)
odd_even = shape % 2
s2 = N.ceil(shape * (2**0.5))
if mode == 1: # everything is even
s2 = N.where(s2 % 2, s2 + 1, s2)
elif mode == 2: # even & even or odd & odd
for d, s in enumerate(shape):
if (s % 2 and not s2[d] % 2) or (not s % 2 and s2[d] % 2):
s2[d] += 1
cent = s2 / 2.
dif = (s2 - shape) / 2.
dm = dif % 1
# print s2, cent, dif, dm
slc = [Ellipsis] + [slice(int(d), int(d)+shape[i]) for i, d in enumerate(dif)]
# This slice is float which shift array when cutting out!!
s2 = tuple([int(ss) for ss in s2]) # numpy array cannot use used for slice
inds = N.indices(s2, N.float32)
ind_shape = inds.shape
nz = N.product(ind_shape[:-2])
nsec = nz / float(ndim)
if ndim > 2:
inds = N.reshape(inds, (nz,)+ind_shape[-2:])
irs = N.empty_like(inds)
for d, ind in enumerate(inds):
idx = int(d//nsec)
c = cent[idx]
if rot and inds.ndim > 2:
U.trans2d(ind - c, irs[d], (0,0,rot,1,0,1))
irs[d] += c - dif[idx]
else:
irs[d] = ind - dif[idx]
if len(ind_shape) > 2:
irs = N.reshape(irs, ind_shape)
irs = irs[slc]
if mode == 1 and N.sometrue(dm):
inds = N.empty_like(irs)
# print 'translate', dm
for d, ind in enumerate(irs):
U.trans2d(ind, inds[d], (-dm[1], -dm[0], 0, 1, 0, 1))
else:
inds = irs
INDS_DIC[shapeTuple] = inds
r_inds = N.empty_like(inds)
for d, ld in enumerate(LD):
r_inds[d] = inds[d] + ld
return r_inds, LD
