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.

median_filter_nonfinite(arr[, size, callback])

Remove nonfinite values from a 3D array using an in-place 2D median filter.

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.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.

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_filter3d(arr, size=3, ncore=None)[source]

Apply 3D median filter to a 3D array.

New in version 1.13.

Parameters:

  • arr (ndarray) – Input 3D array either float32 or uint16 data type.

  • size (int, optional) – The size of the filter’s kernel.

  • ncore (int, optional) – Number of cores that will be assigned to jobs. All cores will be used if unspecified.

Returns:

ndarray – Median filtered 3D array either float32 or uint16 data type.

Raises:

ValueError – If the input array is not three dimensional.

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.median_filter_nonfinite(arr, size=3, callback=None)[source]

Remove nonfinite values from a 3D array using an in-place 2D median filter.

The 2D selective median filter is applied along the last two axes of the array.

New in version 1.11.

Parameters:

  • arr (ndarray) – The 3D array with nonfinite values in it.

  • size (int, optional) – The size of the filter.

  • callback (func(total, description, unit)) – A function called after every internal loop iteration. total is number of loop iterations. description is ‘Nonfinite median filter’. unit is ‘ prjs’.

Returns:

ndarray – The corrected 3D array with all nonfinite values removed based upon the local median value defined by the kernel size.

Raises:

ValueError – If the filter comes across a kernel only containing non-finite values a ValueError is raised for the user to increase their kernel size.

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_outlier3d(arr, dif, size=3, ncore=None)[source]

Selectively applies 3D median filter to a 3D array to remove outliers. Also called a dezinger.

New in version 1.13.

Parameters:

  • arr (ndarray) – Input 3D array either float32 or uint16 data type.

  • dif (float) – Expected difference value between outlier value and the median value of the array.

  • size (int, optional) – The size of the filter’s kernel.

  • ncore (int, optional) – Number of cores that will be assigned to jobs. All cores will be used if unspecified.

Returns:

ndarray – Dezingered 3D array either float32 or uint16 data type.

Raises:

ValueError – If the input array is not three dimensional.

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='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) – Features larger than twice this angle (degrees) will be considered a ring artifact. Must be less than 180 degrees.

  • 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.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.