tomopy.misc.corr

Module for data correction and masking functions.

Functions:

adjust_range(arr[, dmin, dmax]) Change dynamic range of values in an array.
circ_mask(arr, axis[, ratio, val, ncore]) Apply circular mask to a 3D array.
gaussian_filter(arr[, sigma, order, axis, ncore]) Apply Gaussian filter to 3D array along specified axis.
median_filter(arr[, size, axis, ncore]) Apply median filter to 3D array along specified axis.
median_filter_cuda(arr[, size, axis]) Apply median filter to 3D array along 0 axis with GPU support.
sobel_filter(arr[, axis, ncore]) Apply Sobel filter to 3D array along specified axis.
remove_nan(arr[, val, ncore]) Replace NaN values in array with a given value.
remove_neg(arr[, val, ncore]) Replace negative values in array with a given value.
remove_outlier(arr, dif[, size, axis, …]) Remove high intensity bright spots from a N-dimensional array by chunking along the specified dimension, and performing (N-1)-dimensional median filtering along the other dimensions.
remove_outlier_cuda(arr, dif[, size, axis]) Remove high intensity bright spots from a 3D array along axis 0 dimension using GPU.
remove_ring(rec[, center_x, center_y, …]) Remove ring artifacts from images in the reconstructed domain.
tomopy.misc.corr.adjust_range(arr, dmin=None, dmax=None)[source]

Change dynamic range of values in an array.

Parameters:
  • arr (ndarray) – Input array.
  • dmin, dmax (float, optional) – Mininum and maximum values to rescale data.
Returns:

ndarray – Output array.

tomopy.misc.corr.circ_mask(arr, axis, ratio=1, val=0.0, ncore=None)[source]

Apply circular mask to a 3D array.

Parameters:
  • arr (ndarray) – Arbitrary 3D array.
  • axis (int) – Axis along which mask will be performed.
  • ratio (int, optional) – Ratio of the mask’s diameter in pixels to the smallest edge size along given axis.
  • val (int, optional) – Value for the masked region.
Returns:

ndarray – Masked array.

tomopy.misc.corr.gaussian_filter(arr, sigma=3, order=0, axis=0, ncore=None)[source]

Apply Gaussian filter to 3D array along specified axis.

Parameters:
  • arr (ndarray) – Input array.
  • sigma (scalar or sequence of scalars) – Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
  • order ({0, 1, 2, 3} or sequence from same set, optional) – Order of the filter along each axis is given as a sequence of integers, or as a single number. An order of 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented
  • axis (int, optional) – Axis along which median filtering is performed.
  • ncore (int, optional) – Number of cores that will be assigned to jobs.
Returns:

ndarray – 3D array of same shape as input.

tomopy.misc.corr.median_filter(arr, size=3, axis=0, ncore=None)[source]

Apply median filter to 3D array along specified axis.

Parameters:
  • arr (ndarray) – Input array.
  • size (int, optional) – The size of the filter.
  • axis (int, optional) – Axis along which median filtering is performed.
  • ncore (int, optional) – Number of cores that will be assigned to jobs.
Returns:

ndarray – Median filtered 3D array.

tomopy.misc.corr.median_filter_cuda(arr, size=3, axis=0)[source]

Apply median filter to 3D array along 0 axis with GPU support. The winAllow is for A6000, Tian X support 3 to 8

Parameters:
  • arr (ndarray) – Input array.
  • size (int, optional) – The size of the filter.
  • axis (int, optional) – Axis along which median filtering is performed.
Returns:

ndarray – Median filtered 3D array.

Example

import tomocuda tomocuda.remove_outlier_cuda(arr, dif, 5)

For more information regarding install and using tomocuda, check https://github.com/kyuepublic/tomocuda for more information

tomopy.misc.corr.sobel_filter(arr, axis=0, ncore=None)[source]

Apply Sobel filter to 3D array along specified axis.

Parameters:
  • arr (ndarray) – Input array.
  • axis (int, optional) – Axis along which sobel filtering is performed.
  • ncore (int, optional) – Number of cores that will be assigned to jobs.
Returns:

ndarray – 3D array of same shape as input.

tomopy.misc.corr.remove_nan(arr, val=0.0, ncore=None)[source]

Replace NaN values in array with a given value.

Parameters:
  • arr (ndarray) – Input array.
  • val (float, optional) – Values to be replaced with NaN values in array.
  • ncore (int, optional) – Number of cores that will be assigned to jobs.
Returns:

ndarray – Corrected array.

tomopy.misc.corr.remove_neg(arr, val=0.0, ncore=None)[source]

Replace negative values in array with a given value.

Parameters:
  • arr (ndarray) – Input array.
  • val (float, optional) – Values to be replaced with negative values in array.
  • ncore (int, optional) – Number of cores that will be assigned to jobs.
Returns:

ndarray – Corrected array.

tomopy.misc.corr.remove_outlier(arr, dif, size=3, axis=0, ncore=None, out=None)[source]

Remove high intensity bright spots from a N-dimensional array by chunking along the specified dimension, and performing (N-1)-dimensional median filtering along the other dimensions.

Parameters:
  • arr (ndarray) – Input array.
  • dif (float) – Expected difference value between outlier value and the median value of the array.
  • size (int) – Size of the median filter.
  • axis (int, optional) – Axis along which to chunk.
  • ncore (int, optional) – Number of cores that will be assigned to jobs.
  • out (ndarray, optional) – Output array for result. If same as arr, process will be done in-place.
Returns:

ndarray – Corrected array.

tomopy.misc.corr.remove_outlier1d(arr, dif, size=3, axis=0, ncore=None, out=None)[source]

Remove high intensity bright spots from an array, using a one-dimensional median filter along the specified axis.

Parameters:
  • arr (ndarray) – Input array.
  • dif (float) – Expected difference value between outlier value and the median value of the array.
  • size (int) – Size of the median filter.
  • axis (int, optional) – Axis along which median filtering is performed.
  • ncore (int, optional) – Number of cores that will be assigned to jobs.
  • out (ndarray, optional) – Output array for result. If same as arr, process will be done in-place.
Returns:

ndarray – Corrected array.

tomopy.misc.corr.remove_outlier_cuda(arr, dif, size=3, axis=0)[source]

Remove high intensity bright spots from a 3D array along axis 0 dimension using GPU.

Parameters:
  • arr (ndarray) – Input array.
  • dif (float) – Expected difference value between outlier value and the median value of the array.
  • size (int) – Size of the median filter.
  • axis (int, optional) – Axis along which outlier removal is performed.
Returns:

ndarray – Corrected array.

Example

>>> import tomocuda
>>> tomocuda.remove_outlier_cuda(arr, dif, 5)

For more information regarding install and using tomocuda, check https://github.com/kyuepublic/tomocuda for more information

tomopy.misc.corr.remove_ring(rec, center_x=None, center_y=None, thresh=300.0, thresh_max=300.0, thresh_min=-100.0, theta_min=30, rwidth=30, int_mode=u'WRAP', ncore=None, nchunk=None, out=None)[source]

Remove ring artifacts from images in the reconstructed domain. Descriptions of parameters need to be more clear for sure.

Parameters:
  • arr (ndarray) – Array of reconstruction data
  • center_x (float, optional) – abscissa location of center of rotation
  • center_y (float, optional) – ordinate location of center of rotation
  • thresh (float, optional) – maximum value of an offset due to a ring artifact
  • thresh_max (float, optional) – max value for portion of image to filter
  • thresh_min (float, optional) – min value for portion of image to filer
  • theta_min (int, optional) – minimum angle in degrees (int) to be considered ring artifact
  • rwidth (int, optional) – Maximum width of the rings to be filtered in pixels
  • int_mode (str, optional) – ‘WRAP’ for wrapping at 0 and 360 degrees, ‘REFLECT’ for reflective boundaries at 0 and 180 degrees.
  • ncore (int, optional) – Number of cores that will be assigned to jobs.
  • nchunk (int, optional) – Chunk size for each core.
  • out (ndarray, optional) – Output array for result. If same as arr, process will be done in-place.
Returns:

ndarray – Corrected reconstruction data

tomopy.misc.corr.enhance_projs_aps_1id(imgstack, median_ks=5, ncore=None)[source]

Enhance the projection images with weak contrast collected at APS 1ID

This filter uses a median fileter (will be switched to enhanced recursive median fileter, ERMF, in the future) for denoising, and a histogram equalization for dynamic range adjustment to bring out the details.

Parameters:
  • imgstack (np.ndarray) – tomopy images stacks (axis_0 is the oemga direction)
  • median_ks (int, optional) – 2D median filter kernel size for local noise suppresion
  • ncore (int, optional) – number of cores used for speed up
Returns:

ndarray – 3D enhanced image stacks.