### SPDHG Usage Example

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/algorithms/SPDHG

Example demonstrating how to set up and run the SPDHG algorithm with partitioned data.

```python
>>> data = dataexample.SIMULATED_PARALLEL_BEAM_DATA.get()
>>> data.reorder('astra')
>>> data = data.get_slice(vertical='centre')
>>> ig = ag.get_ImageGeometry()

>>> data_partitioned = data.partition(num_batches=10, mode='staggered')
>>> A_partitioned = ProjectionOperator(ig, data_partitioned.geometry, device = "cpu")


>>> F = BlockFunction(*[L2NormSquared(b=data_partitioned[i])
                            for i in range(10)])

>>> alpha = 0.025
>>> G = alpha * TotalVariation()
>>> spdhg = SPDHG(f=F, g=G, operator=A_partitioned, sampler=Sampler.sequential(len(A)),
                      initial=A.domain_geometry().allocate(1), update_objective_interval=10)
>>> spdhg.run(100)
```

--------------------------------

### Setup Preconditioning Weights

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/algorithms/SIRT

Calculates the preconditioning arrays M and D.

```python
def _set_up_weights(self):
        """Set up the preconditioning arrays M and D"""
        self.M = 1. /
            self.operator.direct(
                self.operator.domain_geometry().allocate(value=1.0))
        self._Dscaled = 1. /
            self.operator.adjoint(
                self.operator.range_geometry().allocate(value=1.0))

        for arr in [self.M, self._Dscaled]:
            self._remove_nan_or_inf(arr, replace_with=1.0)
```

--------------------------------

### Download example dataset

Source: https://tomographicimaging.github.io/CIL/nightly/demos/ZeissDataReader

Downloads the WALNUT dataset to a local directory.

```python
from cil.utilities import dataexample
dataexample.WALNUT.download_data(data_dir='../data', prompt=False)
```

--------------------------------

### Define SumFunction examples

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/functions/Function

Examples demonstrating how to instantiate SumFunction with specific functions or a list of functions.

```python
>>> from cil.optimisation.functions import L2NormSquared
>>> from cil.framework import ImageGeometry
>>> f1 = L2NormSquared()
>>> f2 = 0.5 * L2NormSquared(b = ig.allocate(1))
>>> F = SumFunction(f1, f2)
```

```python
>>> F = SumFunction(*[L2NormSquared(b=i) for i in range(50)])
```

--------------------------------

### ZEISSDataReader ROI Examples

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/io/ZEISSDataReader

Examples demonstrating equivalent ROI configurations for the ZEISSDataReader using negative indexing.

```python
>>> reader1 = ZeissDataReader(file_name, roi={'horizontal': (10, -10)})

>>> reader2 = ZeissDataReader(file_name, roi={'horizontal': (10, 1990)})
```

--------------------------------

### Install memory-profiler

Source: https://tomographicimaging.github.io/CIL/nightly/demos/memory_profiling_sandstone

Command to install the memory-profiler package via pip.

```bash
pip install -U memory_profiler
```

--------------------------------

### Install build tools on WSL

Source: https://tomographicimaging.github.io/CIL/nightly/developer_guide

Install necessary C++ build tools within a Windows Subsystem for Linux environment.

```bash
apt install build-essential
```

--------------------------------

### Initialize ZEISSDataReader with ROI

Source: https://tomographicimaging.github.io/CIL/nightly/io

Examples demonstrating equivalent ROI configurations using negative indexing and explicit slicing.

```python
>>> reader1 = ZeissDataReader(file_name, roi={'horizontal': (10, -10)})
```

```python
>>> reader2 = ZeissDataReader(file_name, roi={'horizontal': (10, 1990)})
```

--------------------------------

### Setup image geometry and data

Source: https://tomographicimaging.github.io/CIL/nightly/demos/controlledWaveletSparsity_2d

Configures the image geometry and creates the AcquisitionData object.

```python
# Setup image geometry
n = num_pixels - 1
ig = ag.get_ImageGeometry()
ig.voxel_num_x = n
ig.voxel_num_y = n
print(ig)

# Setup data
data = AcquisitionData(sinogram, deep_copy=False, geometry=ag)
show2D(data);
```

--------------------------------

### Setup Processor Configuration

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/processors/Padder

Configures internal parameters and dimensions based on the input data and padding settings.

```python
def _set_up(self):

    data = self.get_input()
    offset = 4-data.ndim

    #set defaults
    self._labels_in = [None]*4
    self._labels_in[offset::] = data.dimension_labels

    self._shape_in = [1]*4
    self._shape_in[offset::] = data.shape

    self._shape_out_full = self._shape_in.copy()

    self._processed_dims = [0,0,0,0]

    self._pad_width_param = [(0,0)]*4
    self._pad_values_param = [(0,0)]*4


    # if pad_width or set_values is passed a dictionary these keys specify the axes to run over
    if isinstance(self.pad_width, dict) and isinstance(self.pad_values, dict):

        if self.pad_width.keys() != self.pad_values.keys():
            raise ValueError('Dictionaries must contain the same axes')

        dimensions = self._get_dimensions_from_dict(self.pad_width)

    elif isinstance(self.pad_width, dict):
        dimensions = self._get_dimensions_from_dict(self.pad_width)

    elif isinstance(self.pad_values, dict):
        dimensions = self._get_dimensions_from_dict(self.pad_values)

    else:
        spatial_dimensions =[
                    'vertical',
                    'horizontal',
                    'horizontal_y',
                    'horizontal_x'
                ]

        dimensions = list(set(spatial_dimensions) & set(self._labels_in))


    # get pad_widths for these dimensions
    for dim in dimensions:
        try:
            values = self.pad_width[dim]
        except TypeError:
            values = self.pad_width
```

--------------------------------

### FISTA.__init__

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/algorithms/FISTA

Initializes the FISTA algorithm instance with the starting point, objective functions, and optional step size or preconditioner.

```APIDOC
## FISTA(initial, f, g, step_size=None, preconditioner=None, **kwargs)

### Description
Initializes the FISTA algorithm. The algorithm solves the minimization problem by iteratively applying a gradient step followed by a proximal operator.

### Parameters
- **initial** (DataContainer) - Required - Starting point of the algorithm.
- **f** (Function) - Required - Differentiable function. If None, uses ZeroFunction.
- **g** (Function or None) - Required - Convex function with simple proximal operator. If None, uses ZeroFunction.
- **step_size** (float or StepSizeRule) - Optional - Step size for the gradient step. Defaults to 1/L.
- **preconditioner** (object) - Optional - A class with an apply method to modify the gradient update.
```

--------------------------------

### Configure processor setup

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/processors/Slicer

Parses the ROI and configures the processor dimensions and output shapes.

```python
    def _set_up(self):
        """
        This parses the input roi generically and then configures the processor according to its class.
        """
        #read input
        data = self.get_input()
        self._parse_roi(data.ndim, data.shape, data.dimension_labels)
        #processor specific configurations
        self._configure()
        # set boolean of dimensions to process
        self._processed_dims = [0 if self._shape_out_full[i] == self._shape_in[i] else 1 for i in range(4)]
        self._shape_out = tuple([i for i in self._shape_out_full if i > 1])
        self._labels_out = [self._labels_in[i] for i,x in enumerate(self._shape_out_full) if x > 1]
```

--------------------------------

### Compare FGP_TV and TotalVariation

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/plugins/ccpi_regularisation/functions/regularisers

Example showing how to rescale FGP_TV parameters to match TotalVariation results.

```python
>>> G1 = (alpha/ig.voxel_size_x) * FGP_TV(max_iteration=100, device='gpu')
>>> G2 = alpha * TotalVariation(max_iteration=100, lower=0.)
```

--------------------------------

### Binning Geometry with Binner

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/processors/Binner

Example showing how to apply the Binner processor to a geometry object.

```python
>>> from cil.processors import Binner
>>> roi = {'horizontal':(None,None,2),'vertical':(None,None,2)}
>>> processor = Binner(roi)
>>> processor.set_input(data.geometry)
>>> geometry_binned = processor.get_output()
```

--------------------------------

### Initialize NikonDataReader with ROI

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/io/NikonDataReader

Examples demonstrating equivalent ways to specify a horizontal region of interest using the NikonDataReader.

```python
>>> reader1 = NikonDataReader(file_name, roi={'horizontal': (10, -10)})
```

```python
>>> reader2 = NikonDataReader(file_name, roi={'horizontal': (10, 1990)})
```

--------------------------------

### Setup Acquisition Geometry

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/io/ZEISSDataReader

Configures the AcquisitionData container based on beam geometry.

```python
def _setup_acq_geometry(self):
    '''
    Setup AcquisitionData container
    '''
    if self._metadata['beam geometry'] == 'cone':
        self._geometry = AcquisitionGeometry.create_Cone3D(
            [0,-self._metadata['dist_source_center'],0],[0,self._metadata['dist_center_detector'],0] \
            ) \
                .set_panel([self._metadata['image_width'], self._metadata['image_height']],\
                    pixel_size=[self._metadata['detector_pixel_size']/1000,self._metadata['detector_pixel_size']/1000])\
                .set_angles(self._metadata['thetas'],angle_unit=AngleUnit.RADIAN)
    else:
        self._geometry = AcquisitionGeometry.create_Parallel3D()\
                .set_panel([self._metadata['image_width'], self._metadata['image_height']])\
                .set_angles(self._metadata['thetas'],angle_unit=AngleUnit.RADIAN)
    self._geometry.dimension_labels =  ['angle', 'vertical', 'horizontal']
```

--------------------------------

### Initialize SPDHG Algorithm

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/algorithms/SPDHG

Constructor for the SPDHG class, handling initial parameters and calling the setup method.

```python
    def __init__(self, f=None, g=None, operator=None, tau=None, sigma=None,
                 initial=None, sampler=None, prob_weights=None,   **kwargs):

        update_objective_interval = kwargs.pop('update_objective_interval', 1)
        super(SPDHG, self).__init__(
            update_objective_interval=update_objective_interval)

        self.set_up(f=f, g=g, operator=operator, sigma=sigma, tau=tau,
                    initial=initial,  sampler=sampler, prob_weights=prob_weights)
```

--------------------------------

### FBP Reconstruction Usage

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/plugins/astra/processors/FBP

Example demonstrating how to initialize the FBP processor, set the input data, and retrieve the reconstructed image.

```python
>>> from cil.plugins.astra import FBP
>>> fbp = FBP(image_geometry, data.geometry)
>>> fbp.set_input(data)
>>> reconstruction = fbp.get_output()
```

--------------------------------

### Binning Data with Binner

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/processors/Binner

Example showing how to define a region-of-interest and apply the Binner processor to data.

```python
>>> from cil.processors import Binner
>>> roi = {'horizontal':(10,-10,2),'vertical':(10,-10,2)}
>>> processor = Binner(roi)
>>> processor.set_input(data)
>>> data_binned = processor.get_output()
```

--------------------------------

### Demonstrate ImageGeometry resolution scaling

Source: https://tomographicimaging.github.io/CIL/nightly/demos/00_CIL_geometry

Full example showing the creation of an AcquisitionGeometry and subsequent ImageGeometry objects at different resolutions.

```python
ag = AcquisitionGeometry.create_Cone3D(source_position=[0,-500,0],detector_position=[0,500,0])\
    .set_panel(num_pixels=[2048,2048], pixel_size = 0.2)\
    .set_angles(angles=range(0,180))

print("ImageGeometry - default")
ig = ag.get_ImageGeometry()
print(ig)

print("ImageGeometry - 0.5x resolution")
ig = ag.get_ImageGeometry(resolution=0.5)
print(ig)

print("ImageGeometry - 2x resolution")
ig = ag.get_ImageGeometry(resolution=2)
print(ig)
```

--------------------------------

### Initialize SIRT Algorithm

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/algorithms/SIRT

Example of initializing the SIRT algorithm with an initial guess, operator, and data.

```python
>>> sirt = SIRT(initial = ig.allocate(0), operator = A, data = d)
```

--------------------------------

### Build and Serve CIL Documentation

Source: https://tomographicimaging.github.io/CIL/nightly/developer_guide

Commands to clone the repository, set up the development environment, and build the documentation locally.

```bash
git clone git@github.com:TomographicImaging/CIL
cd CIL
conda env create -f ./scripts/cil_development.yml
conda activate ENVIRONMENT_NAME
pip install -e .
cd docs
conda update -n base -c defaults conda
conda env update -f docs_environment.yml # with the name field set to ENVIRONMENT_NAME
make web-deps  # install dependencies (requires ruby 3.2)
make dirhtml web serve
```

--------------------------------

### Warm Start Approximate Gradients

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/functions/SAGFunction

Initializes all gradients at a specific point to warm start the algorithm.

```python
def warm_start_approximate_gradients(self, initial):
        r"""A function to warm start SAG or SAGA algorithms by initialising all the gradients at an initial point. Equivalently setting :math:`g_i^0 = \nabla f_i(x_0)` for initial point :math:`x_0`. 
        
        Parameters
        ----------
        initial: DataContainer,
            The initial point to warmstart the calculation
            
        Note
        ------
        When using SAG or SAGA with a deterministic algorithm, you should warm start the SAG-SAGA Function with the same initial point that you initialise the algorithm
        
        
        """
        self._list_stored_gradients = [
            fi.gradient(initial) for fi in self.functions]
        self._full_gradient_at_iterate = np.sum(self._list_stored_gradients)
        self._update_data_passes_indices(list(range(self.num_functions)))
        self._sampled_grad = initial.copy()
        self._stochastic_grad_difference = initial.copy()
```

--------------------------------

### set_up(initial, operator, data)

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/algorithms/LSQR

Configures the LSQR algorithm with the problem definition, including the initial guess, forward operator, and measured data.

```APIDOC
## LSQR.set_up

### Description
Sets up the LSQR algorithm with the problem definition and performs the initial Golub-Kahan bidiagonalisation.

### Parameters
- **initial** (DataContainer) - Initial guess for the solution.
- **operator** (Operator) - Linear operator representing the forward model.
- **data** (DataContainer) - Measured data.
```

--------------------------------

### Configure SPDHG Algorithm

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/algorithms/SPDHG

The set_up method initializes the algorithm parameters, operators, and variables required for execution.

```python
[docs]
    def set_up(self, f, g, operator, sigma=None, tau=None,
               initial=None,   sampler=None, prob_weights=None):
        '''set-up of the algorithm
        '''
        log.info("%s setting up", self.__class__.__name__)

        # algorithmic parameters
        self.f = f
        self.g = g
        self.operator = operator

        if not isinstance(operator, BlockOperator):
            raise TypeError("operator should be a BlockOperator")

        self._ndual_subsets = len(self.operator)

        self._prob_weights = getattr(sampler, 'prob_weights', prob_weights)

        if self._prob_weights is None:
            self._prob_weights = [1/self._ndual_subsets]*self._ndual_subsets

        if prob_weights is not None and self._prob_weights != prob_weights:
            raise ValueError(
                ' You passed a `prob_weights` argument and a sampler with a different attribute `prob_weights`, please remove the `prob_weights` argument.')

        if sampler is None:
            self._sampler = Sampler.random_with_replacement(
                len(operator), prob=self._prob_weights)
        else:
            self._sampler = sampler

        # Set the norms of the operators
        self._norms = operator.get_norms_as_list()

        self.set_step_sizes(sigma=sigma, tau=tau)

        # initialize primal variable
        if initial is None:
            self.x = self.operator.domain_geometry().allocate(0)
        else:
            self.x = initial.copy()

        self._x_tmp = self.operator.domain_geometry().allocate(0)

        # initialize dual variable to 0
        self._y_old = operator.range_geometry().allocate(0)
        # This can be removed once #1863 is fixed
        if not isinstance(self._y_old, BlockDataContainer):
            self._y_old = BlockDataContainer(self._y_old)

        # initialize variable z corresponding to back-projected dual variable
        self._z = operator.domain_geometry().allocate(0)
        self._zbar = operator.domain_geometry().allocate(0)
        # relaxation parameter
        self._theta = 1

        self.configured = True
        logging.info("{} configured".format(self.__class__.__name__, ))
```

--------------------------------

### set_up(file_name=None)

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/io/NEXUSDataReader

Initializes the reader by validating the file path and ensuring the h5py dependency is available.

```APIDOC
## set_up(file_name=None)

### Description
Configures the reader with the specified file path and performs validation checks.

### Parameters
- **file_name** (str) - Required - Full path to the NeXus file.
```

--------------------------------

### Initialize and use a sequential sampler

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/utilities/sampler

Demonstrates creating a sequential sampler and tracking previous samples.

```python
>>> sampler = Sampler.sequential(10)
>>> print(sampler.get_previous_samples())
[]
>>> print(sampler.view_samples(5))
[0 1 2 3 4]
>>> print(next(sampler))
0
>>> print(sampler.next())
1
>>> print(sampler.get_current_sample())
1
>>> print(sampler.get_previous_samples())
[0 1]
```

--------------------------------

### CentreOfRotationCorrector xcorrelation usage examples

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/processors/CentreOfRotationCorrector

Examples demonstrating how to initialize and run the cross-correlation based centre of rotation correction processor.

```python
>>> from cil.processors import CentreOfRotationCorrector
>>> processor = CentreOfRotationCorrector.xcorrelation('centre')
>>> processor.set_input(data)
>>> data_centred = processor.get_ouput()
```

```python
>>> from cil.processors import CentreOfRotationCorrector
>>> processor = CentreOfRotationCorrector.xcorrelation(slice_index=120)
>>> processor.set_input(data)
>>> processor.get_ouput(out=data)
```

```python
>>> from cil.processors import CentreOfRotationCorrector
>>> import logging
>>> logging.basicConfig(level=logging.WARNING)
>>> cil_log_level = logging.getLogger('cil.processors')
>>> cil_log_level.setLevel(logging.DEBUG)

>>> processor = CentreOfRotationCorrector.xcorrelation(slice_index=120)
>>> processor.set_input(data)
>>> data_centred = processor.get_output()
```

--------------------------------

### Initialize and run FBP reconstruction

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/plugins/tigre/FBP

Demonstrates the basic workflow for configuring the FBP processor, setting the input data, and retrieving the reconstructed output.

```python
>>> from cil.plugins.tigre import FBP
>>> fbp = FBP(image_geometry, data.geometry)
>>> fbp.set_input(data)
>>> reconstruction = fbp.get_output()
```

--------------------------------

### Initialize and use a random sampler with replacement

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/utilities/sampler

Demonstrates creating a random sampler with replacement and retrieving samples.

```python
>>> sampler = Sampler.random_with_replacement(5)
>>> print(sampler.view_samples(20))
[3 4 0 0 2 3 3 2 2 1 1 4 4 3 0 2 4 4 2 4]
>>> print(next(sampler))
3
>>> print(sampler.next())
4
>>> print(sampler.get_current_sample())
4
```

--------------------------------

### set_up

Source: https://tomographicimaging.github.io/CIL/nightly/io

Configures the reader with a file name and optional ROI.

```APIDOC
## set_up(file_name, roi=None)

### Description
Set up the reader with the specified file and region of interest.

### Parameters
- **file_name** (str) - Required - file name to read
- **roi** (dict) - Optional - dictionary with roi to load for each axis.
```

--------------------------------

### Check CIL Version

Source: https://tomographicimaging.github.io/CIL/nightly/demos/deriv2_cgls

Verify the installed version of the CIL library.

```python
import cil
print(cil.__version__)
```

--------------------------------

### Instantiate BlockDataContainer

Source: https://tomographicimaging.github.io/CIL/nightly/framework

Example of creating a BlockDataContainer from multiple DataContainer objects.

```python
bdc = BlockDataContainer(DataContainer0, DataContainer1)
```

--------------------------------

### Initialize and run VolumeShrinker

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/utilities/shrink_volume

Demonstrates how to instantiate the VolumeShrinker and execute the volume reduction process using the 'otsu' method.

```python
>>> cil_log_level = logging.getLogger()
>>> cil_log_level.setLevel(logging.DEBUG)
>>> vs = VolumeShrinker(data, recon_backend='astra')
>>> ig_reduced = vs.run(method='otsu', otsu_classes=3)
```

--------------------------------

### get(data_dir)

Source: https://tomographicimaging.github.io/CIL/nightly/utilities

Loads a dataset from the specified local data directory.

```APIDOC
## get(data_dir)

### Description
Loads a dataset that has been previously downloaded to the specified data directory.

### Parameters
- **data_dir** (string) - Required - The directory path containing the dataset to be loaded.
```

--------------------------------

### Get Algorithm Output

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/algorithms/FISTA

Returns the current solution stored in the algorithm.

```python
[docs]
def get_output(self):
    " Returns the current solution. "
    return self.x_old
```

--------------------------------

### Import CIL and profiling dependencies

Source: https://tomographicimaging.github.io/CIL/nightly/demos/memory_profiling_sandstone

Initializes the environment by appending local paths and importing necessary CIL modules, algorithms, and profiling utilities.

```python
import sys
sys.path.append("./Algorithms")
sys.path.append("./Scripts")

from LSQR import *
from LSQR_Tikhonov import *

from LSQRLP import *
from LSQRMP import *

from CGLSLP import *
from CGLSMP import *

# CIL core components needed
from cil.processors import Normaliser, TransmissionAbsorptionConverter, Padder, CentreOfRotationCorrector
from cil.framework import AcquisitionGeometry, AcquisitionData, BlockDataContainer

# CIL optimisation algorithms and linear operators
from cil.optimisation.algorithms import CGLS
from cil.optimisation.operators import BlockOperator, IdentityOperator

# CIL example synthetic test image
from cil.utilities import dataexample

# CIL display tools
from cil.utilities.display import show2D

# CIL FBP
from cil.recon import FBP

# Forward/backprojector from CIL ASTRA plugin
from cil.plugins.astra import ProjectionOperator

# Utility functions for this dataset & notebook
from Utilities import timed_iterations, mem_prof_process, line_prof_process, line_peak_process, plot_mem, print_output_subset
from IPython.display import display, HTML

# Third-party imports
import numpy as np
import scipy
import matplotlib.pyplot as plt
import os

MB = 1/(1024*1024)
dtypeSize = 4
```

--------------------------------

### CIL version output

Source: https://tomographicimaging.github.io/CIL/nightly/demos/stempo2d_v2_1

Displays the installed version of the Core Imaging Library.

```text
23.0.1
```

--------------------------------

### Initialize Reconstruction

Source: https://tomographicimaging.github.io/CIL/nightly/demos/dynamic_mr_recon

Compute the starting image using the adjoint of the block operator.

```python
# Starting image
x_init = A.adjoint(acq_dyn_block)
```

--------------------------------

### Setup Image Geometry

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/io/ZEISSDataReader

Configures the ImageGeometry container using metadata parameters.

```python
def _setup_image_geometry(self):
    '''
    Setup ImageData container
    '''
    slices = self._metadata['number_of_images']
    width = self._metadata['image_width']
    height = self._metadata['image_height']
    voxel_size = self._metadata['pixel_size']
    self._geometry = ImageGeometry(voxel_num_x=width,
                                voxel_size_x=voxel_size,
                                voxel_num_y=height,
                                voxel_size_y=voxel_size,
                                voxel_num_z=slices,
                                voxel_size_z=voxel_size)
```

--------------------------------

### Get Slice

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/framework/acquisition_geometry

Returns a new AcquisitionGeometry of a single slice in the requested direction.

```python
def get_slice(self, *args, **kwargs):
        '''
        Returns a new AcquisitionGeometry of a single slice in the requested direction.
```

--------------------------------

### Initialize and use a Herman-Meyer sampler

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/utilities/sampler

Demonstrates creating a Herman-Meyer sampler.

```python
>>> sampler = Sampler.herman_meyer(12)
>>> print(sampler.view_samples(16))
[ 0  6  3  9  1  7  4 10  2  8  5 11  0  6  3  9]
```

--------------------------------

### Get individual norms

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/operators/BlockOperator

Returns a list containing the norms of all operators in the block.

```python
    def get_norms_as_list(self, ):
        '''Returns a list of the individual norms of the Operators in the BlockOperator
        '''
        return [op.norm() for op in self.operators]
```

--------------------------------

### Apply RingRemover processor

Source: https://tomographicimaging.github.io/CIL/nightly/demos/bruker2cil

Example usage for removing ring artefacts from projection data.

```python
#processor = RingRemover(decNum=4, wname='db35', sigma=1.5, info=True)
#processor.set_input(proj)
#proj = processor.get_output()
```

--------------------------------

### Initialize FBP reconstructor

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/recon/FBP

Example usage of the FBP class for parallel-beam data reconstruction.

```python
>>> from cil.utilities.dataexample import SIMULATED_PARALLEL_BEAM_DATA
>>> from cil.recon import FBP
>>> data = SIMULATED_PARALLEL_BEAM_DATA.get()
>>> fbp = FBP(data)
>>> out = fbp.run()
```

--------------------------------

### Build CIL using Docker

Source: https://tomographicimaging.github.io/CIL/nightly/developer_guide

Initialize submodules and build the CIL Docker image from the repository root.

```bash
git submodule update --init --recursive
docker build . -t ghcr.io/tomographicimaging/cil
```

--------------------------------

### Memory Profiler output for LSQR set_up

Source: https://tomographicimaging.github.io/CIL/nightly/demos/memory_profiling_sandstone

Detailed memory usage report for the set_up method of the LSQR algorithm.

```text
Dataset folder already exists in ..
Filename: /home/efv97572/cil-showcase/CIL-User-Showcase/015_Memory_Profiling_LSQR_CGLS/Algorithms/LSQRMP.py

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
    92    681.6 MiB    681.6 MiB           1       @profile
    93                                             def set_up(self, initial, operator, data):
    94                                                 r'''Initialisation of the algorithm
    95                                                 Parameters
    96                                                 ------------
    97                                                 operator : Operator
    98                                                     Linear operator for the inverse problem
    99                                                 initial : (optional) DataContainer in the domain of the operator, default is a DataContainer filled with zeros.
   100                                                     Initial guess
   101                                                 data : DataContainer in the range of the operator
   102                                                     Acquired data to reconstruct
   103
   104                                                 '''
   105
   106    681.6 MiB      0.0 MiB           1           log.info("%s setting up", self.__class__.__name__)
   107    735.3 MiB     53.7 MiB           1           self.x = initial.copy() #1 domain
   108    735.3 MiB      0.0 MiB           1           self.operator = operator
   109
   110                                                 # Initialise Golub-Kahan bidiagonalisation (GKB)
   111
   112                                                 #self.u = data - self.operator.direct(self.x)
   113    758.2 MiB     22.9 MiB           1           self.u = self.operator.direct(self.x) #1 range
   114    758.2 MiB      0.0 MiB           1           self.u.sapyb(-1, data, 1, out=self.u)
   115    758.3 MiB      0.0 MiB           1           self.beta = self.u.norm()
   116    758.4 MiB      0.1 MiB           1           self.u /= self.beta
   117
   118    813.2 MiB     54.9 MiB           1           self.v = self.operator.adjoint(self.u) #2 domain
   119    813.2 MiB      0.0 MiB           1           self.alpha = self.v.norm()
   120    813.2 MiB      0.0 MiB           1           self.v /= self.alpha
   121
   122    813.2 MiB      0.0 MiB           1           self.rhobar = self.alpha
   123    813.2 MiB      0.0 MiB           1           self.phibar = self.beta
   124    813.2 MiB      0.0 MiB           1           self.normr = self.beta
   125    813.2 MiB      0.0 MiB           1           self.regalphasq = self.regalpha**2
   126
   127    866.9 MiB     53.7 MiB           1           self.d = self.v.copy() #3 domain
   128    888.5 MiB     21.6 MiB           1           self.tmp_range = data.geometry.allocate(None) #2 range
   129    888.5 MiB      0.0 MiB           1           self.tmp_domain = self.x.geometry.allocate(None) #4 domain
   130
   131    888.5 MiB      0.0 MiB           1           self.res2 = 0
   132
   133    888.5 MiB      0.0 MiB           1           self.configured = True
   134    888.5 MiB      0.0 MiB           1           log.info("%s configured", self.__class__.__name__)


run 1
Filename: /home/efv97572/cil-showcase/CIL-User-Showcase/015_Memory_Profiling_LSQR_CGLS/Algorithms/LSQRMP.py
```

--------------------------------

### Initialize FDK Reconstructor

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/recon/FBP

Example usage of the FDK reconstructor with simulated cone-beam data.

```python
>>> from cil.utilities.dataexample import SIMULATED_CONE_BEAM_DATA
>>> from cil.recon import FDK
>>> data = SIMULATED_CONE_BEAM_DATA.get()
>>> fdk = FDK(data)
>>> out = fdk.run()
```

--------------------------------

### Run CIL unit tests

Source: https://tomographicimaging.github.io/CIL/nightly/developer_guide

Execute the test suite to verify the installation. Optionally enable GPU testing by setting the environment variable.

```bash
export TESTS_FORCE_GPU=1  # optional, makes GPU test failures noisy
python -m unittest discover -v ./Wrappers/Python/test
```

--------------------------------

### FBP Usage Example

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/plugins/astra/processors/FBP

Standard workflow for using the FBP processor to reconstruct data.

```APIDOC
from cil.plugins.astra import FBP

# Initialize the processor
fbp = FBP(image_geometry, data.geometry)

# Set the input dataset
fbp.set_input(data)

# Retrieve the reconstruction
reconstruction = fbp.get_output()
```

--------------------------------

### Build CIL from source

Source: https://tomographicimaging.github.io/CIL/nightly/developer_guide

Install the package in editable mode. Use the second variant to specify custom paths for optional libraries like IPP or OpenMP.

```bash
pip install -e .
```

```bash
pip install -e . -Ccmake.define.IPP_ROOT="<path_to_ipp>" -Ccmake.define.OpenMP_ROOT="<path_to_openmp>" -Ccmake.define.CMAKE_BUILD_TYPE=RelWithDebInfo
```

--------------------------------

### Initialize L2NormSquared

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/functions/L2NormSquared

Examples of initializing the L2NormSquared function with or without a translation parameter b.

```python
>>> F = L2NormSquared()
>>> F = L2NormSquared(b=b)
>>> F = L2NormSquared().centered_at(b)
```

--------------------------------

### Initialize and use a staggered sampler

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/optimisation/utilities/sampler

Demonstrates creating a staggered sampler with a specific stride.

```python
>>> sampler = Sampler.staggered(num_indices=21, stride=4)
>>> print(next(sampler))
0
>>> print(sampler.next())
4
>>> print(sampler.view_samples(5))
[ 0  4  8 12 16]
>>> print(sampler.get_previous_samples())
[0 4]
```

--------------------------------

### Setup and Run CGLS Reconstruction

Source: https://tomographicimaging.github.io/CIL/nightly/demos/stempo2d_v2_1

Initializes and executes the CGLS algorithm for image reconstruction.

```python
# initialisation of the optimisation algorithm - zero array in this case
initial = ig.allocate(0)

# setup CGLS
cgls = CGLS(initial=initial,
            operator=A,
            data=stempoData,
            max_iteration = 100,
            update_objective_interval = 10 )
```

```python
cgls.run(50,verbose=True)
# get and visualise the results
recon_cgls = cgls.solution
```

--------------------------------

### Configure and Run FISTA Optimisation

Source: https://tomographicimaging.github.io/CIL/nightly/demos/dynamic_mr_recon

Sets up the objective function with LeastSquares, initializes the FGP_TV_BDC regulariser, and executes the FISTA algorithm for reconstruction.

```python
# Set up optimisation
x_init_zero = x_init.copy()

# Objective function
f = LeastSquares(A, acq_dyn_block, c=1)
f.calculate_Lipschitz()

alpha = 2e-5
G = FGP_TV_BDC(x_init_zero, alpha)

# Set up FISTA for least squares
fista = FISTA(initial=x_init_zero, f=f, g=G)
fista.max_iteration = 50
fista.update_objective_interval = 10

# Run FISTA
fista.run(verbose=True)

# Get result
im_fista_tv = fista.get_output()
```

--------------------------------

### Setup Image Geometry

Source: https://tomographicimaging.github.io/CIL/nightly/demos/controlledWaveletSparsity_3d

Configures the image geometry based on the processed data dimensions.

```python
# Setup image geometry
(_, nz, nx) = data.shape
ig = data.geometry.get_ImageGeometry()
ig.voxel_num_x = nx
ig.voxel_num_y = nx
ig.voxel_num_z = nz
print(ig)
```

--------------------------------

### Initialize and use ProjectionOperator

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/plugins/astra/operators/ProjectionOperator

Demonstrates basic instantiation of the ProjectionOperator and execution of direct and adjoint projections.

```python
>>> from cil.plugins.astra import ProjectionOperator
>>> PO = ProjectionOperator(image.geometry, data.geometry)
>>> forward_projection = PO.direct(image)
>>> backward_projection = PO.adjoint(data)
```

--------------------------------

### Get Dimensions from Dictionary

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/processors/Padder

Extracts dimension labels from a dictionary and validates them against the input geometry.

```python
def _get_dimensions_from_dict(self, dict):

    dimensions = []
    for k in dict.keys():
        if k not in self._labels_in:
            raise ValueError('Dimension label not found in data. Expected labels from {0}. Got {1}'.format(self._geometry.dimension_labels, k))

        dimensions.append(k)
    return dimensions
```

--------------------------------

### Correct Laminography Geometry

Source: https://tomographicimaging.github.io/CIL/nightly/processors

Example of fitting tilt and center-of-rotation for parallel beam laminography data.

```python
>>> processor = LaminographyGeometryCorrector(parameter_bounds=(tilt_bounds, CoR_bounds),
    parameter_tolerance=(tilt_tol, CoR_tol))
>>> processor.set_input(data)
>>> data_corrected = processor.get_output()
```

--------------------------------

### CentreOfRotationCorrector Usage

Source: https://tomographicimaging.github.io/CIL/nightly/processors

Examples of using the CentreOfRotationCorrector to adjust image sharpness using different backends.

```python
from cil.processors import CentreOfRotationCorrector
processor = CentreOfRotationCorrector.image_sharpness(‘centre’, ‘tigre’) processor.set_input(data) data_centred = processor.get_output()
```

```python
from cil.processors import CentreOfRotationCorrector
processor = CentreOfRotationCorrector.image_sharpness(slice_index=120, ‘astra’) processor.set_input(data) processor.get_output(out=data)
```

--------------------------------

### ZEISSDataReader.set_up

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/io/ZEISSDataReader

Configures the reader with a specific file and ROI, validating the file existence and type.

```APIDOC
## set_up(file_name, roi=None)

### Description
Sets up the reader by validating the provided file path and ensuring the file extension is either .txrm or .txm.

### Parameters
- **file_name** (str) - Required - The path to the file to read.
- **roi** (dict) - Optional - A dictionary defining the region of interest for each axis.
```

--------------------------------

### Compute Proximal Operator with FGP_TV

Source: https://tomographicimaging.github.io/CIL/nightly/_modules/cil/plugins/ccpi_regularisation/functions/regularisers

Example of initializing the FGP_TV regulariser and computing the proximal operator.

```python
>>> G = alpha * FGP_TV(max_iteration=100, device='gpu')
>>> sol = G.proximal(b)
```

--------------------------------

### Import CIL and Numerical Dependencies

Source: https://tomographicimaging.github.io/CIL/nightly/demos/deriv2_cgls

Initializes the environment by importing necessary libraries for optimization, operators, and data handling.

```python
import numpy as np
import matplotlib.pyplot as plt
from cil.optimisation.algorithms import CGLS
from cil.optimisation.operators import MatrixOperator
from cil.framework import VectorData, BlockDataContainer
from deriv2 import deriv2
from cil.optimisation.operators import BlockOperator,IdentityOperator
from cil.optimisation.utilities import callbacks
```