def transform_scalars(dataset, resampling_factor=[1, 1, 1]):
"""Resample dataset"""
from tomviz import utils
import scipy.ndimage
import numpy as np
array = utils.get_array(dataset)
# Transform the dataset.
result_shape = utils.zoom_shape(array, resampling_factor)
result = np.empty(result_shape, array.dtype, order='F')
scipy.ndimage.interpolation.zoom(array, resampling_factor, output=result)
# Set the result as the new scalars.
utils.set_array(dataset, result)
# Update tilt angles if dataset is a tilt series.
if resampling_factor[2] != 1:
try:
tilt_angles = utils.get_tilt_angles(dataset)
result_shape = utils.zoom_shape(tilt_angles, resampling_factor[2])
result = np.empty(result_shape, array.dtype, order='F')
scipy.ndimage.interpolation.zoom(
tilt_angles, resampling_factor[2], output=result)
utils.set_tilt_angles(dataset, result)
except: # noqa
# TODO What exception are we ignoring?
pass
def transform(dataset, resampling_factor=[1, 1, 1]):
"""Resample dataset"""
from tomviz import utils
import scipy.ndimage
import numpy as np
array = dataset.active_scalars
# Transform the dataset.
result_shape = utils.zoom_shape(array, resampling_factor)
result = np.empty(result_shape, array.dtype, order='F')
scipy.ndimage.interpolation.zoom(array, resampling_factor, output=result)
# Set the result as the new scalars.
dataset.active_scalars = result
# Update tilt angles if dataset is a tilt series.
if resampling_factor[2] != 1:
try:
tilt_angles = dataset.tilt_angles
result_shape = utils.zoom_shape(tilt_angles, resampling_factor[2])
result = np.empty(result_shape, array.dtype, order='F')
scipy.ndimage.interpolation.zoom(tilt_angles,
resampling_factor[2],
output=result)
dataset.tilt_angles = result
except: # noqa
# TODO What exception are we ignoring?
pass
def transform_scalars(dataset):
"""Downsample volume by a factor of 2"""
from tomviz import utils
import scipy.ndimage
import numpy as np
array = utils.get_array(dataset)
# Downsample the dataset x2 using order 1 spline (linear)
# Calculate out array shape
zoom = (0.5, 0.5, 0.5)
result_shape = utils.zoom_shape(array, zoom)
result = np.empty(result_shape, array.dtype, order='F')
scipy.ndimage.interpolation.zoom(array, zoom,
output=result, order=1,
mode='constant', cval=0.0,
prefilter=False)
# Set the result as the new scalars.
utils.set_array(dataset, result)
# Update tilt angles if dataset is a tilt series.
try:
tilt_angles = utils.get_tilt_angles(dataset)
result_shape = utils.zoom_shape(tilt_angles, 0.5)
result = np.empty(result_shape, array.dtype, order='F')
tilt_angles = scipy.ndimage.interpolation.zoom(tilt_angles, 0.5,
output=result)
utils.set_tilt_angles(dataset, result)
except: # noqa
# TODO What exception are we ignoring?
pass
def transform_scalars(dataset):
"""Downsample tilt images by a factor of 2"""
from tomviz import utils
import scipy.ndimage
import numpy as np
import warnings
array = utils.get_array(dataset)
zoom = (0.5, 0.5, 1)
result_shape = utils.zoom_shape(array, zoom)
result = np.empty(result_shape, array.dtype, order='F')
# Downsample the dataset x2 using order 1 spline (linear)
warnings.filterwarnings('ignore', '.*output shape of zoom.*')
scipy.ndimage.interpolation.zoom(array,
zoom,
output=result,
order=1,
mode='constant',
cval=0.0,
prefilter=False)
# Set the result as the new scalars.
utils.set_array(dataset, result)
def transform_scalars(dataset):
"""Downsample volume by a factor of 2"""
from tomviz import utils
import scipy.ndimage
import numpy as np
array = utils.get_array(dataset)
# Downsample the dataset x2 using order 1 spline (linear)
# Calculate out array shape
def dimensions(array):
if array.ndim == 4:
zoom = (0.5,0.5,0.5,1)
else:
zoom = (0.5, 0.5, 0.5)
return zoom
zoom = dimensions(array)
result_shape = utils.zoom_shape(array, zoom)
result = np.empty(result_shape, array.dtype, order='F')
scipy.ndimage.interpolation.zoom(array, zoom,
output=result, order=1,
mode='constant', cval=0.0,
prefilter=False)
# Set the result as the new scalars.
utils.set_array(dataset, result)
# Update tilt angles if dataset is a tilt series.
try:
tilt_angles = utils.get_tilt_angles(dataset)
result_shape = utils.zoom_shape(tilt_angles, 0.5)
result = np.empty(result_shape, array.dtype, order='F')
tilt_angles = scipy.ndimage.interpolation.zoom(tilt_angles, 0.5,
output=result)
utils.set_tilt_angles(dataset, result)
except: # noqa
# TODO What exception are we ignoring?
pass
def transform(dataset):
"""Downsample volume by a factor of 2"""
from tomviz import utils
import scipy.ndimage
import numpy as np
array = dataset.active_scalars
# Downsample the dataset x2 using order 1 spline (linear)
# Calculate out array shape
zoom = (0.5, 0.5, 0.5)
result_shape = utils.zoom_shape(array, zoom)
result = np.empty(result_shape, array.dtype, order='F')
scipy.ndimage.interpolation.zoom(array,
zoom,
output=result,
order=1,
mode='constant',
cval=0.0,
prefilter=False)
# Set the result as the new scalars.
dataset.active_scalars = result
# Update tilt angles if dataset is a tilt series.
try:
tilt_angles = dataset.tilt_angles
result_shape = utils.zoom_shape(tilt_angles, 0.5)
result = np.empty(result_shape, array.dtype, order='F')
tilt_angles = scipy.ndimage.interpolation.zoom(tilt_angles,
0.5,
output=result)
dataset.tilt_angles = result
except: # noqa
# TODO What exception are we ignoring?
pass
def transform_scalars(dataset):
"""Downsample tilt images by a factor of 2"""
from tomviz import utils
import scipy.ndimage
import numpy as np
array = utils.get_array(dataset)
zoom = (0.5, 0.5, 1)
result_shape = utils.zoom_shape(array, zoom)
result = np.empty(result_shape, array.dtype, order='F')
# Downsample the dataset x2 using order 1 spline (linear)
scipy.ndimage.interpolation.zoom(array, zoom,
output=result,
order=1,
mode='constant',
cval=0.0, prefilter=False)
# Set the result as the new scalars.
utils.set_array(dataset, result)