# Framework¶

The goal of the CCPi Framework is to allow the user to simply create iterative reconstruction methods which go beyond the standard filter back projection technique and which better suit the data characteristics. The framework comprises:

• ccpi.framework module which allows to simply translate real world CT systems into software.
• ccpi.optimisation module allows the user to quickly create iterative methods to reconstruct acquisition data applying different types of regularisation, which better suit the data characteristics.
• ccpi.io module which provides a number of loaders for real CT machines, e.g. Nikon. It also provides reader and writer to save to NeXuS file format.

## CT Geometry¶

Please refer to this notebook on the CIL-Demos repository for full description.

In conventional CT systems, an object is placed between a source emitting X-rays and a detector array measuring the X-ray transmission images of the incident X-rays. Typically, either the object is placed on a rotating sample stage and rotates with respect to the source-detector assembly, or the source-detector gantry rotates with respect to the stationary object. This arrangement results in so-called circular scanning trajectory. Depending on source and detector types, there are three conventional data acquisition geometries:

• parallel geometry (2D or 3D),
• fan-beam geometry, and
• cone-beam geometry.

### Parallel geometry¶

Parallel beams of X-rays are emitted onto 1D (single pixel row) or 2D detector array. This geometry is common for synchrotron sources. 2D parrallel geometry is illustrated below.

2D Parallel geometry

3D Parallel geometry

### Fan-beam geometry¶

A single point-like X-ray source emits a cone beam onto 1D detector pixel row. Cone-beam is typically
collimated to imaging field of view. Collimation allows greatly reduce amount of scatter radiation reaching the detector. Fan-beam geometry is used when scattering has significant influence on image quality or single-slice reconstruction is sufficient.

Fan beam geometry

### Cone-beam geometry¶

A single point-like X-ray source emits a cone beam onto 2D detector array. Cone-beam geometry is mainly used in lab-based CT instruments. Depending on where the sample is placed between the source and the detector one can achieve a different magnification factor $$F$$:

$F = \frac{r_1 + r_2}{r_1}$

where $$r_1$$ and $$r_2$$ are the distance from the source to the center of the sample and the distance from the center of the sample to the detector, respectively.

Cone beam geometry

## AcquisitonGeometry and AcquisitionData¶

In the Framework, we implemented AcquisitionGeometry class to hold acquisition parameters and ImageGeometry to hold geometry of a reconstructed volume. Corresponding data arrays are wrapped as AcquisitionData and ImageData classes, respectively.

The simplest (of course from image processing point of view, not from physical implementation) geometry is the parallel geometry. Geometrical parameters for parallel geometry are depicted below:

Parallel geometry

In the Framework, we define AcquisitionGeometry as follows.

# imports
from ccpi.framework import AcquisitionGeometry
import numpy as np

# acquisition angles
n_angles = 90
angles = np.linspace(0, np.pi, n_angles, dtype=np.float32)

# number of pixels in detector row
N = 256

# pixel size
pixel_size_h = 1

# # create AcquisitionGeometry
ag_par = AcquisitionGeometry(geom_type='parallel',
dimension='2D',
angles=angles,
pixel_num_h=N,
pixel_size_h=pixel_size_h)


AcquisitionGeometry contains only metadata, the actual data is wrapped in AcquisitionData class. AcquisitionGeometry class also holds information about arrangement of the actual acquisition data array. We use attribute dimension_labels to label axis. The expected dimension labels are shown below. The default order of dimensions for AcquisitionData is [angle, horizontal], meaning that the number of elements along 0 and 1 axes in the acquisition data array is expected to be n_angles and N, respectively.

Parallel data

To have consistent AcquisitionData and AcquisitionGeometry, we recommend to allocate AcquisitionData using allocate method of the AcquisitionGeometry instance:

# allocate AcquisitionData


## ImageGeometry and ImageData¶

To store reconstruction results, we implemented two classes: ImageGeometry and ImageData classes. Similar to AcquisitionData and AcquisitionGeometry, we first define 2D ImageGeometry and then allocate ImageData.

# imports
from ccpi.framework import ImageData, ImageGeometry

# define 2D ImageGeometry
# given AcquisitionGeometry ag_par, default parameters for corresponding ImageData
ig_par = ImageGeometry(voxel_num_y=ag_par.pixel_num_h,
voxel_size_x=ag_par.pixel_size_h,
voxel_num_x=ag_par.pixel_num_h,
voxel_size_y=ag_par.pixel_size_h)

# allocate ImageData filled with 0 values with the specific geometry
im_data1 = ig_par.allocate()
# allocate ImageData filled with random values with the specific geometry
im_data2 = ig_par.allocate('random', seed=5)


Fan-beam, cone-beam and 3D (multi-slice) parallel geometry can be set-up similar to 2D parallel geometry.

### 3D parallel geometry¶

Geometrical parameters and dimension labels for 3D parallel beam geometry

3D parallel beam AcquisitionGeometry and default ImageGeometry parameters can be set up as follows:

# set-up 3D parallel beam AcquisitionGeometry
# physical pixel size
pixel_size_h = 1
ag_par_3d = AcquisitionGeometry(geom_type='parallel',
dimension='3D',
angles=angles,
pixel_num_h=N,
pixel_size_h=pixel_size_h,
pixel_num_v=N,
pixel_size_v=pixel_size_h)
# set-up 3D parallel beam ImageGeometry
ig_par_3d = ImageGeometry(voxel_num_x=ag_par_3d.pixel_num_h,
voxel_size_x=ag_par_3d.pixel_size_h,
voxel_num_y=ag_par_3d.pixel_num_h,
voxel_size_y=ag_par_3d.pixel_size_h,
voxel_num_z=ag_par_3d.pixel_num_v,
voxel_size_z=ag_par_3d.pixel_size_v)


### Fan-beam geometry¶

Geometrical parameters and dimension labels for fan-beam geometry.

Geometrical parameters and dimension labels for fan-beam data.

Fan-beam AcquisitionGeometry and default ImageGeometry parameters can be set up as follows:

# set-up fan-beam AcquisitionGeometry
# distance from source to center of rotation
dist_source_center = 200.0
# distance from center of rotation to detector
dist_center_detector = 300.0
# physical pixel size
pixel_size_h = 2
ag_fan = AcquisitionGeometry(geom_type='cone',
dimension='2D',
angles=angles,
pixel_num_h=N,
pixel_size_h=pixel_size_h,
dist_source_center=dist_source_center,
dist_center_detector=dist_center_detector)
# calculate geometrical magnification
mag = (ag_fan.dist_source_center + ag_fan.dist_center_detector) / ag_fan.dist_source_center

ig_fan = ImageGeometry(voxel_num_x=ag_fan.pixel_num_h,
voxel_size_x=ag_fan.pixel_size_h / mag,
voxel_num_y=ag_fan.pixel_num_h,
voxel_size_y=ag_fan.pixel_size_h / mag)

class ccpi.framework.ImageGeometry(voxel_num_x=0, voxel_num_y=0, voxel_num_z=0, voxel_size_x=1, voxel_size_y=1, voxel_size_z=1, center_x=0, center_y=0, center_z=0, channels=1, **kwargs)[source]
allocate(value=0, dimension_labels=None, **kwargs)[source]

allocates an ImageData according to the size expressed in the instance

clone()[source]

returns a copy of ImageGeometry

copy()[source]

alias of clone

class ccpi.framework.AcquisitionGeometry(geom_type, dimension=None, angles=None, pixel_num_h=0, pixel_size_h=1, pixel_num_v=0, pixel_size_v=1, dist_source_center=None, dist_center_detector=None, channels=1, **kwargs)[source]
allocate(value=0, dimension_labels=None, **kwargs)[source]

allocates an AcquisitionData according to the size expressed in the instance

Parameters: value (number or string, default None allocates empty memory block) – accepts numbers to allocate an uniform array, or a string as ‘random’ or ‘random_int’ to create a random array or None. dimension_labels – labels for the dimension axis dtype (numpy type, default numpy.float32) – numerical type to allocate
clone()[source]

returns a copy of the AcquisitionGeometry

copy()[source]

alias of clone

class ccpi.framework.VectorGeometry(length)[source]

Geometry describing VectorData to contain 1D array

allocate(value=0, **kwargs)[source]

allocates an VectorData according to the size expressed in the instance

clone()[source]

returns a copy of VectorGeometry

copy()[source]

alias of clone

DataContainer and subclasses AcquisitionData and ImageData are meant to contain data and meta-data in AcquisitionGeometry and ImageGeometry respectively.

## DataContainer and subclasses¶

AcquisiionData and ImageData inherit from the same parent DataContainer class, therefore they largely behave the same way.

There are algebraic operations defined for both AcquisitionData and ImageData. Following operations are defined:

• binary operations (between two DataContainers or scalar and DataContainer)
• + addition
• - subtraction
• / division
• * multiplication
• ** power
• maximum
• minimum
• in-place operations
• +=
• -=
• *=
• **=
• /=
• unary operations
• abs
• sqrt
• sign
• conjugate
• reductions
• sum
• norm
• dot product

AcquisitionData and ImageData provide a simple method to transpose the data and to produce a subset of itself based on the axis we would like to have. This method is based on the label of the axes of the data rather than the way it is stored. We think that the user should describe what she wants and not bother with knowing the actual layout of the data in the memory.

# transpose data using subset method
data_transposed = data.subset(['horizontal_y', 'horizontal_x'])
# extract single row
data_profile = data_subset.subset(horizontal_y=100)

class ccpi.framework.DataContainer(array, deep_copy=True, dimension_labels=None, **kwargs)[source]

Generic class to hold data

Data is currently held in a numpy arrays

__init__(array, deep_copy=True, dimension_labels=None, **kwargs)[source]

Holds the data

__neg__()[source]

negation operator

__str__(representation=False)[source]

Return str(self).

__weakref__

list of weak references to the object (if defined)

as_array(dimensions=None)[source]

Returns the DataContainer as Numpy Array

Returns the pointer to the array if dimensions is not set. If dimensions is set, it first creates a new DataContainer with the subset and then it returns the pointer to the array

axpby(a, b, y, out, dtype=<class 'numpy.float32'>, num_threads=2)[source]

performs axpby with cilacc C library

Does the operation .. math:: a*x+b*y and stores the result in out, where x is self

Parameters: a (float) – scalar b (float) – scalar y – DataContainer out – DataContainer instance to store the result dtype (numpy type, optional, default numpy.float32) – data type of the DataContainers num_threads (int, optional, default 1/2 CPU of the system) – number of threads to run on
clone()[source]

returns a copy of itself

copy()[source]

alias of clone

dot(other, *args, **kwargs)[source]

return the inner product of 2 DataContainers viewed as vectors

applies to real and complex data. In such case the dot method returns

a.dot(b.conjugate())

dtype

Returns the type of the data array

exp(*args, **kwargs)[source]

Applies exp pixel-wise to the DataContainer

fill(array, **dimension)[source]

fills the internal numpy array with the one provided

Parameters: array (DataContainer, numpy array or number) – numpy array to copy into the DataContainer dimension – dictionary, optional
get_data_axes_order(new_order=None)[source]

returns the axes label of self as a list

if new_order is None returns the labels of the axes as a sorted-by-key list if new_order is a list of length number_of_dimensions, returns a list with the indices of the axes in new_order with respect to those in self.dimension_labels: i.e.

self.dimension_labels = {0:’horizontal’,1:’vertical’} new_order = [‘vertical’,’horizontal’] returns [1,0]
log(*args, **kwargs)[source]

Applies log pixel-wise to the DataContainer

max(*args, **kwargs)[source]

Returns the max pixel value in the DataContainer

min(*args, **kwargs)[source]

Returns the min pixel value in the DataContainer

norm()[source]

return the euclidean norm of the DataContainer viewed as a vector

size

Returns the number of elements of the DataContainer

squared_norm()[source]

return the squared euclidean norm of the DataContainer viewed as a vector

subset(dimensions=None, **kw)[source]

Creates a DataContainer containing a subset of self according to the labels in dimensions

class ccpi.framework.ImageData(array=None, deep_copy=False, dimension_labels=None, **kwargs)[source]

DataContainer for holding 2D or 3D DataContainer

subset(dimensions=None, **kw)[source]

returns a subset of ImageData and regenerates the geometry

class ccpi.framework.AcquisitionData(array=None, deep_copy=True, dimension_labels=None, **kwargs)[source]

DataContainer for holding 2D or 3D sinogram

subset(dimensions=None, **kw)[source]

returns a subset of the AcquisitionData and regenerates the geometry

class ccpi.framework.VectorData(array=None, **kwargs)[source]

DataContainer to contain 1D array

### Multi channel data¶

Both AcquisitionGeometry, AcquisitionData and ImageGeometry, ImageData can be defined for multi-channel (spectral) CT data using channels attribute.

# multi-channel fan-beam geometry
ag_fan_mc = AcquisitionGeometry(geom_type='cone',
dimension='2D',
angles=angles,
pixel_num_h=N,
pixel_size_h=1,
dist_source_center=200,
dist_center_detector=300,
channels=10)

# define multi-channel 2D ImageGeometry
ig3 = ImageGeometry(voxel_num_y=5,
voxel_num_x=4,
channels=2)


## Block Framework¶

The block framework allows writing more advanced optimisation problems. Consider the typical Tikhonov regularisation:

$\underset{u}{\mathrm{argmin}}\begin{Vmatrix}A u - b \end{Vmatrix}^2_2 + \alpha^2\|Lu\|^2_2$

where,

• $$A$$ is the projection operator
• $$b$$ is the acquired data
• $$u$$ is the unknown image to be solved for
• $$\alpha$$ is the regularisation parameter
• $$L$$ is a regularisation operator

The first term measures the fidelity of the solution to the data. The second term meausures the fidelity to the prior knowledge we have imposed on the system, operator $$L$$.

This can be re-written equivalently in the block matrix form:

$\underset{u}{\mathrm{argmin}}\begin{Vmatrix}\binom{A}{\alpha L} u - \binom{b}{0}\end{Vmatrix}^2_2$

With the definitions:

• $$\tilde{A} = \binom{A}{\alpha L}$$
• $$\tilde{b} = \binom{b}{0}$$

this can now be recognised as a least squares problem which can be solved by any algorithm in the ccpi.optimisation which can solve least squares problem, e.g. CGLS.

$\underset{u}{\mathrm{argmin}}\begin{Vmatrix}\tilde{A} u - \tilde{b}\end{Vmatrix}^2_2$

To be able to express our optimisation problems in the matrix form above, we developed the so-called, Block Framework comprising 4 main actors: BlockGeometry, BlockDataContainer, BlockFunction and BlockOperator.

A BlockDataContainer can be instantiated from a number of DataContainer and subclasses represents a column vector of :code:DataContainers.

bdc = BlockDataContainer(DataContainer0, DataContainer1)


. These classes are required for it to work. They provide a base class that will behave as normal DataContainer.

class ccpi.framework.BlockDataContainer(*args, **kwargs)[source]

Class to hold DataContainers as column vector

Provides basic algebra between BlockDataContainer’s, DataContainer’s and subclasses and Numbers

1. algebra between BlockDataContainers will be element-wise, only if the shape of the 2 BlockDataContainers is the same, otherwise it will fail
2. algebra between BlockDataContainers and list or numpy array will work as long as the number of rows and element of the arrays match, indipendently on the fact that the BlockDataContainer could be nested
3. algebra between BlockDataContainer and one DataContainer is possible. It will require that all the DataContainers in the block to be compatible with the DataContainer we want to algebra with. Should we require that the DataContainer is the same type? Like ImageData or AcquisitionData?
4. algebra between BlockDataContainer and a Number is possible and it will be done with each element of the BlockDataContainer even if nested

A = [ [B,C] , D] A * 3 = [ 3 * [B,C] , 3* D] = [ [ 3*B, 3*C] , 3*D ]

__iadd__(other)[source]

__idiv__(other)[source]

Inline division

__imul__(other)[source]

Inline multiplication

__isub__(other)[source]

Inline subtraction

__iter__()[source]

BlockDataContainer is Iterable

__itruediv__(other)[source]

Inline truedivision

__radd__(other)[source]

to make sure that this method is called rather than the __mul__ of a numpy array the class constant __array_priority__ must be set > 0 https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__

__rdiv__(other)[source]

Reverse division

to make sure that this method is called rather than the __mul__ of a numpy array the class constant __array_priority__ must be set > 0 https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__

__rmul__(other)[source]

Reverse multiplication

to make sure that this method is called rather than the __mul__ of a numpy array the class constant __array_priority__ must be set > 0 https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__

__rpow__(other)[source]

Reverse power

to make sure that this method is called rather than the __mul__ of a numpy array the class constant __array_priority__ must be set > 0 https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__

__rsub__(other)[source]

Reverse subtraction

to make sure that this method is called rather than the __mul__ of a numpy array the class constant __array_priority__ must be set > 0 https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__

__rtruediv__(other)[source]

Reverse truedivision

to make sure that this method is called rather than the __mul__ of a numpy array the class constant __array_priority__ must be set > 0 https://docs.scipy.org/doc/numpy-1.15.1/reference/arrays.classes.html#numpy.class.__array_priority__

__weakref__

list of weak references to the object (if defined)

add(other, *args, **kwargs)[source]

Algebra: add method of BlockDataContainer with number/DataContainer or BlockDataContainer

Param: other (number, DataContainer or subclasses or BlockDataContainer out (optional): provides a placehold for the resul.
axpby(a, b, y, out, dtype=<class 'numpy.float32'>, num_threads=2)[source]

performs axpby element-wise on the BlockDataContainer containers

Does the operation .. math:: a*x+b*y and stores the result in out, where x is self

Parameters: a – scalar b – scalar y – compatible (Block)DataContainer out – (Block)DataContainer to store the result dtype – optional, data type of the DataContainers
binary_operations(operation, other, *args, **kwargs)[source]

Algebra: generic method of algebric operation with BlockDataContainer with number/DataContainer or BlockDataContainer

Provides commutativity with DataContainer and subclasses, i.e. this class’s reverse algebric methods take precedence w.r.t. direct algebric methods of DataContainer and subclasses.

This method is not to be used directly

copy()[source]

alias of clone

divide(other, *args, **kwargs)[source]

Algebra: divide method of BlockDataContainer with number/DataContainer or BlockDataContainer

Param: other (number, DataContainer or subclasses or BlockDataContainer out (optional): provides a placehold for the resul.
is_compatible(other)[source]

basic check if the size of the 2 objects fit

maximum(other, *args, **kwargs)[source]

Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer

Param: other (number, DataContainer or subclasses or BlockDataContainer out (optional): provides a placehold for the resul.
minimum(other, *args, **kwargs)[source]

Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer

Param: other (number, DataContainer or subclasses or BlockDataContainer out (optional): provides a placehold for the resul.
multiply(other, *args, **kwargs)[source]

Algebra: multiply method of BlockDataContainer with number/DataContainer or BlockDataContainer

Param: other (number, DataContainer or subclasses or BlockDataContainer out (optional): provides a placehold for the resul.
next()[source]

python2 backwards compatibility

power(other, *args, **kwargs)[source]

Algebra: power method of BlockDataContainer with number/DataContainer or BlockDataContainer

Param: other (number, DataContainer or subclasses or BlockDataContainer out (optional): provides a placehold for the resul.
subtract(other, *args, **kwargs)[source]

Algebra: subtract method of BlockDataContainer with number/DataContainer or BlockDataContainer

Param: other (number, DataContainer or subclasses or BlockDataContainer out (optional): provides a placehold for the resul.
unary_operations(operation, *args, **kwargs)[source]

Unary operation on BlockDataContainer:

generic method of unary operation with BlockDataContainer: abs, sign, sqrt and conjugate

This method is not to be used directly

class ccpi.framework.BlockGeometry(*args, **kwargs)[source]
RANDOM_INT = 'random_int'

Class to hold Geometry as column vector

__weakref__

list of weak references to the object (if defined)

get_item(index)[source]

returns the Geometry in the BlockGeometry located at position index

## DataProcessor¶

A DataProcessor takes as an input a DataContainer or subclass and returns either another DataContainer or some number. The aim of this class is to simplify the writing of processing pipelines.

class ccpi.framework.DataProcessor(**attributes)[source]

Defines a generic DataContainer processor

accepts DataContainer as inputs and outputs DataContainer additional attributes can be defined with __setattr__

__init__(**attributes)[source]

Initialize self. See help(type(self)) for accurate signature.

__setattr__(name, value)[source]

Implement setattr(self, name, value).

__weakref__

list of weak references to the object (if defined)

check_input(dataset)[source]

Checks parameters of the input DataContainer

Should raise an Error if the DataContainer does not match expectation, e.g. if the expected input DataContainer is 3D and the Processor expects 2D.

get_input()[source]

returns the input DataContainer

It is useful in the case the user has provided a DataProcessor as input

### Resizer¶

Quite often we need either crop or downsample data; the Resizer provides a convenient way to perform these operations for both ImageData and AcquisitionData.

# imports
from ccpi.processors import Resizer
# crop ImageData along 1st dimension
# initialise Resizer
resizer_crop = Resizer(binning = [1, 1], roi = [-1, (20,180)])
# pass DataContainer
resizer_crop.input = data
data_cropped = resizer_crop.process()
# get new ImageGeometry
ig_data_cropped = data_cropped.geometry

class ccpi.processors.Resizer(roi=-1, binning=1)[source]
__init__(roi=-1, binning=1)[source]

Constructor

Input:

roi region-of-interest to crop. If roi = -1 (default), then no crop.

Otherwise roi is given by a list with ndim elements, where each element is either -1 if no crop along this dimension or a tuple with beginning and end coodinates to crop to. Example:

to crop 4D array along 2nd dimension: roi = [-1, -1, (100, 900), -1]
binning number of pixels to bin (combine) along each dimension.

If binning = 1, then projections in original resolution are loaded. Otherwise, binning is given by a list with ndim integers. Example:

to rebin 3D array along 1st direction: binning = [1, 5, 1]
check_input(data)[source]

Checks parameters of the input DataContainer

Should raise an Error if the DataContainer does not match expectation, e.g. if the expected input DataContainer is 3D and the Processor expects 2D.

### Calculation of Center of Rotation¶

In the ideal alignment of a CT instrument, orthogonal projection of an axis of rotation onto a detector has to coincide with a vertical midline of the detector. This is barely feasible in practice due to misalignment and/or kinematic errors in positioning of CT instrument components. A slight offset of the center of rotation with respect to the theoretical position will contribute to the loss of resolution; in more severe cases, it will cause severe artifacts in the reconstructed volume (double-borders). CenterOfRotationFinder allows to estimate offset of center of rotation from theoretical. In the current release CenterOfRotationFinder supports only parallel geometry.

CenterOfRotationFinder is based on Nghia Vo’s method.

class ccpi.processors.CenterOfRotationFinder(smin=None, smax=None, srad=None, step=None, ratio=None, drop=None)[source]

Processor to find the center of rotation in a parallel beam experiment

This processor read in a AcquisitionDataSet and finds the center of rotation based on Nghia Vo’s method. https://doi.org/10.1364/OE.22.019078

Input: AcquisitionDataSet Set_slice: Slice index or ‘centre’ smin, smax : int, optional

Reference to the horizontal center of the sinogram.
step : float, optional
Step of fine searching.
ratio : float, optional
The ratio between the FOV of the camera and the size of object. It’s used to generate the mask.
drop : int, optional
Drop lines around vertical center of the mask.

Output: float. center of rotation in pixel coordinate

__init__(smin=None, smax=None, srad=None, step=None, ratio=None, drop=None)[source]

Initialize self. See help(type(self)) for accurate signature.

static _search_coarse(sino, smin, smax, ratio, drop)[source]

Coarse search for finding the rotation center.

static _search_fine(sino, srad, step, init_cen, ratio, drop)[source]

Fine search for finding the rotation center.

check_input(dataset)[source]

Checks parameters of the input DataContainer

Should raise an Error if the DataContainer does not match expectation, e.g. if the expected input DataContainer is 3D and the Processor expects 2D.

static find_center_vo(tomo, ind=None, smin=-40, smax=40, srad=10, step=0.5, ratio=2.0, drop=20)[source]

Find rotation axis location using Nghia Vo’s method. :cite:Vo:14.

tomo : ndarray
3D tomographic data.
ind : int, optional
Index of the slice to be used for reconstruction.
smin, smax : int, optional
Reference to the horizontal center of the sinogram.
step : float, optional
Step of fine searching.
ratio : float, optional
The ratio between the FOV of the camera and the size of object. It’s used to generate the mask.
drop : int, optional
Drop lines around vertical center of the mask.
float
Rotation axis location.

The function may not yield a correct estimate, if:

• the sample size is bigger than the field of view of the camera. In this case the ratio argument need to be set larger than the default of 2.0.
• there is distortion in the imaging hardware. If there’s no correction applied, the center of the projection image may yield a better estimate.
• the sample contrast is weak. Paganin’s filter need to be applied to overcome this.
• the sample was changed during the scan.
set_slice(slice_index='centre')[source]

Set the slice to run over in a 3D data set. The default will use the centre slice.

Input is any valid slice index or ‘centre’

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