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)