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 Ndimensional array by chunking along the specified dimension, and performing (N1)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 Ndimensional array by chunking along the specified dimension, and performing (N1)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 inplace.
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 onedimensional 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 inplace.
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='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 inplace.
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.