def transform_scalars(dataset, SHIFT=None, rotation_angle=90.0):
from tomviz import utils
from scipy import ndimage
import numpy as np
data_py = utils.get_array(dataset) # Get data as numpy array.
if data_py is None: #Check if data exists
raise RuntimeError("No data array found!")
if SHIFT is None:
SHIFT = np.zeros(len(data_py.shape), dtype=np.int)
data_py_return = np.empty_like(data_py)
ndimage.interpolation.shift(data_py, SHIFT, order=0, output=data_py_return)
rotation_axis = 2 # This operator always assumes the rotation axis is Z
if rotation_angle == []: # If tilt angle not given, assign it to 90 degrees.
rotation_angle = 90
axis1 = (rotation_axis + 1) % 3
axis2 = (rotation_axis + 2) % 3
axes = (axis1, axis2)
shape = utils.rotate_shape(data_py_return, rotation_angle, axes=axes)
data_py_return2 = np.empty(shape, data_py_return.dtype, order='F')
ndimage.interpolation.rotate(data_py_return,
rotation_angle,
output=data_py_return2,
axes=axes)
utils.set_array(dataset, data_py_return2)
def transform_scalars(dataset, rotation_angle=90.0, rotation_axis=0):
from tomviz import utils
import numpy as np
from scipy import ndimage
data_py = utils.get_array(dataset) #get data as numpy array
if data_py is None: #Check if data exists
raise RuntimeError("No data array found!")
if rotation_axis == []: #If tilt axis is not given, assign one.
# Find the smallest array dimension, assume it is the tilt angle axis.
if data_py.ndim >= 2:
rotation_axis = np.argmin(data_py.shape)
else:
raise RuntimeError("Data Array is not 2 or 3 dimensions!")
if rotation_angle == []: # If tilt angle not given, assign it to 90 degrees.
rotation_angle = 90
print('Rotating Dataset...')
axis1 = (rotation_axis + 1) % 3
axis2 = (rotation_axis + 2) % 3
axes = (axis1, axis2)
shape = utils.rotate_shape(data_py, rotation_angle, axes=axes)
data_py_return = np.empty(shape, data_py.dtype, order='F')
ndimage.interpolation.rotate(data_py,
rotation_angle,
output=data_py_return,
axes=axes)
utils.set_array(dataset, data_py_return)
print('Rotation Complete')
def transform_scalars(dataset, SHIFT=None, rotation_angle=90.0):
from tomviz import utils
from scipy import ndimage
import numpy as np
data_py = utils.get_array(dataset) # Get data as numpy array.
if data_py is None: #Check if data exists
raise RuntimeError("No data array found!")
if SHIFT is None:
SHIFT = np.zeros(len(data_py.shape), dtype=np.int)
data_py_return = np.empty_like(data_py)
ndimage.interpolation.shift(data_py, SHIFT, order=0, output=data_py_return)
rotation_axis = 2 # This operator always assumes the rotation axis is Z
if rotation_angle == []: # If tilt angle not given, assign it to 90 degrees.
rotation_angle = 90
axis1 = (rotation_axis + 1) % 3
axis2 = (rotation_axis + 2) % 3
axes = (axis1, axis2)
shape = utils.rotate_shape(data_py_return, rotation_angle, axes=axes)
data_py_return2 = np.empty(shape, data_py_return.dtype, order='F')
ndimage.interpolation.rotate(
data_py_return, rotation_angle, output=data_py_return2, axes=axes)
utils.set_array(dataset, data_py_return2)
def transform_scalars(self, dataset):
"""
Automatic align the tilt axis to horizontal direction (x-axis)
"""
self.progress.maximum = 1
# Get Tilt Series
tiltSeries = utils.get_array(dataset)
if tiltSeries is None: #Check if data exists
raise RuntimeError("No data array found!")
(Nslice, Nray, Nproj) = tiltSeries.shape
Intensity = np.zeros(tiltSeries.shape)
self.progress.maximum = Nproj + 131 #coarse(90)+fine(41)
step = 0
self.progress.message = 'Initialization'
#take FFT of all projections
for i in range(Nproj):
self.progress.message = ('Taking Fourier transform of tilt image'
'No.%d/%d' % (i + 1, Nproj))
tiltImage = tiltSeries[:, :, i]
tiltImage_F = np.abs(np.fft.fft2(tiltImage))
if (i == 0):
temp = tiltImage_F[0, 0]
Intensity[:, :, i] = np.fft.fftshift(tiltImage_F / temp)
step += 1
self.progress.value = step
#rescale intensity
Intensity = np.power(Intensity, 0.2)
#calculate variation itensity image
Intensity_var = np.var(Intensity, axis=2)
#search angles
coarseStep = 2
fineStep = 0.1
coarseAngles = np.arange(-90, 90, coarseStep)
Nx = Intensity_var.shape[0]
Ny = Intensity_var.shape[1]
N = np.round(np.min([Nx, Ny]) // 3)
#coarse search
I = np.zeros((coarseAngles.size, N))
for a in range(coarseAngles.size):
if self.canceled:
return
self.progress.message = ('Calculating line intensity at angle %f'
'degree' % (coarseAngles[a]))
I[a, :] = calculateLineIntensity(Intensity_var, coarseAngles[a], N)
step += 1
self.progress.value = step
I_sum = np.sum(I, axis=1)
minIntensityIndex = np.argmin(I_sum)
rot_ang = coarseAngles[minIntensityIndex]
#now refine
fineAngles = np.arange(rot_ang - coarseStep,
rot_ang + coarseStep + fineStep, fineStep)
#fine search
I = np.zeros((fineAngles.size, N))
for a in range(fineAngles.size):
if self.canceled:
return
self.progress.message = ('Calculating line intensity at angle %f'
'degree' % (fineAngles[a]))
I[a, :] = calculateLineIntensity(Intensity_var, fineAngles[a], N)
step += 1
self.progress.value = step
I_sum = np.sum(I, axis=1)
minIntensityIndex = np.argmin(I_sum)
rot_ang = fineAngles[minIntensityIndex]
self.progress.message = 'Rotating tilt series'
axes = ((0, 1))
shape = utils.rotate_shape(tiltSeries, -rot_ang, axes=axes)
result = np.empty(shape, tiltSeries.dtype, order='F')
ndimage.interpolation.rotate(
tiltSeries, -rot_ang, axes=axes, output=result)
print("rotate tilt series by %f degrees" % -rot_ang)
# Set the result as the new scalars.
utils.set_array(dataset, result)
def transform_scalars(self, dataset):
"""
Automatic align the tilt axis to horizontal direction (x-axis)
"""
self.progress.maximum = 1
# Get Tilt Series
tiltSeries = utils.get_array(dataset)
if tiltSeries is None: #Check if data exists
raise RuntimeError("No data array found!")
(Nslice, Nray, Nproj) = tiltSeries.shape
Intensity = np.zeros(tiltSeries.shape)
self.progress.maximum = Nproj + 131 #coarse(90)+fine(41)
step = 0
self.progress.message = 'Initialization'
#take FFT of all projections
for i in range(Nproj):
self.progress.message = ('Taking Fourier transform of tilt image'
'No.%d/%d' % (i + 1, Nproj))
tiltImage = tiltSeries[:, :, i]
tiltImage_F = np.abs(np.fft.fft2(tiltImage))
if (i == 0):
temp = tiltImage_F[0, 0]
Intensity[:, :, i] = np.fft.fftshift(tiltImage_F / temp)
step += 1
self.progress.value = step
#rescale intensity
Intensity = np.power(Intensity, 0.2)
#calculate variation itensity image
Intensity_var = np.var(Intensity, axis=2)
#search angles
coarseStep = 2
fineStep = 0.1
coarseAngles = np.arange(-90, 90, coarseStep)
Nx = Intensity_var.shape[0]
Ny = Intensity_var.shape[1]
N = np.round(np.min([Nx, Ny]) // 3)
#coarse search
I = np.zeros((coarseAngles.size, N))
for a in range(coarseAngles.size):
if self.canceled:
return
self.progress.message = ('Calculating line intensity at angle %f'
'degree' % (coarseAngles[a]))
I[a, :] = calculateLineIntensity(Intensity_var, coarseAngles[a], N)
step += 1
self.progress.value = step
I_sum = np.sum(I, axis=1)
minIntensityIndex = np.argmin(I_sum)
rot_ang = coarseAngles[minIntensityIndex]
#now refine
fineAngles = np.arange(rot_ang - coarseStep,
rot_ang + coarseStep + fineStep, fineStep)
#fine search
I = np.zeros((fineAngles.size, N))
for a in range(fineAngles.size):
if self.canceled:
return
self.progress.message = ('Calculating line intensity at angle %f'
'degree' % (fineAngles[a]))
I[a, :] = calculateLineIntensity(Intensity_var, fineAngles[a], N)
step += 1
self.progress.value = step
I_sum = np.sum(I, axis=1)
minIntensityIndex = np.argmin(I_sum)
rot_ang = fineAngles[minIntensityIndex]
self.progress.message = 'Rotating tilt series'
axes = ((0, 1))
shape = utils.rotate_shape(tiltSeries, -rot_ang, axes=axes)
result = np.empty(shape, tiltSeries.dtype, order='F')
ndimage.interpolation.rotate(tiltSeries,
-rot_ang,
axes=axes,
output=result)
print("rotate tilt series by %f degrees" % -rot_ang)
# Set the result as the new scalars.
utils.set_array(dataset, result)
