Merge branch 'master' into processors_out

Signed-off-by: Hannah Robarts <77114597+hrobarts@users.noreply.github.com>

.github/workflows/build.yml

@@ -122,7 +122,7 @@ jobs:
       working-directory: recipe
       run: |
         conda install boa
-        conda mambabuild . -c conda-forge -c intel -c ccpi --python=${{ matrix.python-version }} --numpy=${{ matrix.numpy-version }} --output-folder .
+        conda mambabuild . -c conda-forge -c https://software.repos.intel.com/python/conda -c ccpi --python=${{ matrix.python-version }} --numpy=${{ matrix.numpy-version }} --output-folder .
     - name: Upload artifact of the conda package
       uses: actions/upload-artifact@v4
       with:

CHANGELOG.md

@@ -1,9 +1,14 @@
-
-* XX.X.X
-
+* 24.x.x
+  - New Features:
+    - Added SAG and SAGA stochastic functions (#1624) 
+  - Enhancements:
+    - New unit tests for operators and functions to check for in place errors and the behaviour of `out` (#1805)
+
+* 24.1.0
   - New Features:
     - Added method to plot filter in `GenericFilteredBackProjection` (#1667)
-    - Added wavelet operator, wrapping PyWavelets operator as a CIL operator (#1618)
+    - Added wavelet operator, wrapping PyWavelets operator as a CIL operator (#1615)
+    - Added PaganinProcessor processor, to perform phase retrieval from phase contrast images (#1737)
     - Added L1Sparsity function, allowing calculations of `|Ax-b|_1` and its proximal, in the case of orthogonal operators, `A` (#1618)
     - Options in algorithms GD, ISTA and FISTA to pass a `cil.optimisation.utilities.StepSizeRule` or a `cil.optimisation.utilities.Preconditioner`(#1768)
     - an implementation of the Armijo Rule as a child class of  `cil.optimisation.utilities.StepSizeRule` (#1768)
@@ -13,8 +18,9 @@
     - Raises error in `BlockDataContainer.pnorm` if the shape of the containers is not the same (#1799)
     - Operators and functions now also return when out is specified (#1742)
     - The CIL function class now has a `__neg__` function, so you can write `-YourFunction(x)` rather than `-1*YourFunction(x)` (#1808)
+    - Added documentation for the Partitioner to `framework.rst` (#1828)
     - Added CIL vs SIRF tests comparing preconditioned ISTA in CIL and MLEM in SIRF (#1823)
-    - New unit tests for operators and functions to check for in place errors and the behaviour of `out` (#1805)
+    - Update to CCPi-Regularisation toolkit v24.0.1 (#1868) 
   - Bug fixes:
     - gradient descent `update_objective` called twice on the initial point.(#1789)
     - ProjectionMap operator bug fix in adjoint and added documentation (#1743)

Dockerfile

@@ -18,7 +18,7 @@ COPY --chown="${NB_USER}" scripts/requirements-test.yml environment.yml
 RUN sed -ri '/tigre|astra-toolbox| python /d' environment.yml \
   && for pkg in 'jupyter-server-proxy>4.1.0' $CIL_EXTRA_PACKAGES; do echo "  - $pkg" >> environment.yml; done \
   && conda config --env --set channel_priority strict \
-  && for ch in defaults nvidia ccpi intel conda-forge; do conda config --env --add channels $ch; done \
+  && for ch in defaults nvidia ccpi https://software.repos.intel.com/python/conda conda-forge; do conda config --env --add channels $ch; done \
   && mamba env update -n base \
   && mamba clean -a -y -f \
   && rm environment.yml \

README.md

@@ -23,13 +23,13 @@ We recommend using either [`miniconda`](https://docs.conda.io/projects/miniconda
 Install a new environment using:
 
 ```sh
-conda create --name cil -c conda-forge -c intel -c ccpi cil=24.0.0
+conda create --name cil -c conda-forge -c https://software.repos.intel.com/python/conda -c ccpi cil=24.1.0
 ```
 
 To install CIL and the additional packages and plugins needed to run the [CIL demos](https://github.com/TomographicImaging/CIL-Demos) install the environment with:
 
 ```sh
-conda create --name cil -c conda-forge -c intel -c ccpi cil=24.0.0 astra-toolbox=*=cuda* tigre ccpi-regulariser tomophantom ipywidgets
+conda create --name cil -c conda-forge -c https://software.repos.intel.com/python/conda -c ccpi cil=24.1.0 astra-toolbox=*=cuda* tigre ccpi-regulariser tomophantom ipywidgets
 ```
 
 where:
@@ -44,7 +44,7 @@ where:
 
 CIL's [optimised FDK/FBP](https://github.com/TomographicImaging/CIL/discussions/1070) `recon` module requires:
 
-1. the Intel [Integrated Performance Primitives](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ipp.html#gs.gxwq5p) Library ([license](https://www.intel.com/content/dam/develop/external/us/en/documents/pdf/intel-simplified-software-license-version-august-2021.pdf)) which can be installed via conda from the `intel` [channel](https://anaconda.org/intel/ipp).
+1. the Intel [Integrated Performance Primitives](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ipp.html#gs.gxwq5p) Library ([license](https://www.intel.com/content/dam/develop/external/us/en/documents/pdf/intel-simplified-software-license-version-august-2021.pdf)) which can be installed via conda from the `https://software.repos.intel.com/python/conda` channel.
 2. [TIGRE](https://github.com/CERN/TIGRE), which can be installed via conda from the `ccpi` channel.
 
 ### Docker

Wrappers/Python/cil/framework/framework.py

@@ -967,7 +967,22 @@ def get_centre_slice(self):
         return self
 
     def calculate_magnification(self):
-        return [None, None, 1.0]
+        '''Method to calculate magnification and distance from the sample to 
+        the detector using the detector positions and the rotation axis. 
+        For parallel beam geometry magnification = 1
+        
+        Returns
+        -------
+        list
+            A list containing the [0] distance from the source to the rotate 
+            axis, [1] distance from the rotate axis to the detector, 
+            [2] magnification of the system
+
+        '''
+        ab = (self.rotation_axis.position - self.detector.position)
+        dist_center_detector = float(numpy.sqrt(ab.dot(ab)))
+
+        return [None, dist_center_detector, 1.0]
 
 class Parallel3D(SystemConfiguration):
     r'''This class creates the SystemConfiguration of a parallel beam 3D tomographic system
@@ -1116,7 +1131,22 @@ def __eq__(self, other):
         return False
 
     def calculate_magnification(self):
-        return [None, None, 1.0]
+        '''Method to calculate magnification and distance from the sample to 
+        the detector using the detector positions and the rotation axis. 
+        For parallel beam geometry magnification = 1
+        
+        Returns
+        -------
+        list
+            A list containing the [0] distance from the source to the rotate 
+            axis, [1] distance from the rotate axis to the detector, 
+            [2] magnification of the system
+
+        '''
+        ab = (self.rotation_axis.position - self.detector.position)
+        dist_center_detector = float(numpy.sqrt(ab.dot(ab)))
+
+        return [None, dist_center_detector, 1.0]
 
     def get_centre_slice(self):
         """Returns the 2D system configuration corresponding to the centre slice

Wrappers/Python/cil/optimisation/algorithms/Algorithm.py

@@ -15,6 +15,7 @@
 #
 # Authors:
 # CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
+from itertools import count
 from numbers import Integral
 from typing import List, Optional
 from warnings import warn
@@ -237,7 +238,9 @@ def run(self, iterations=None, callbacks: Optional[List[Callback]]=None, verbose
 
         # call `__next__` upto `iterations` times or until `StopIteration` is raised
         self.max_iteration = self.iteration + iterations
-        for _ in zip(range(self.iteration, self.iteration + iterations), self):
+        iters = (count(self.iteration) if np.isposinf(self.max_iteration)
+                 else range(self.iteration, self.max_iteration))
+        for _ in zip(iters, self):
             try:
                 for callback in callbacks:
                     callback(self)

Wrappers/Python/cil/optimisation/functions/ApproximateGradientSumFunction.py

@@ -229,7 +229,7 @@ def data_passes_indices(self):
     def data_passes(self):
         """ The property :code:`data_passes` is a list of floats that holds the amount of data that has been processed up until each call of `gradient`. This list is updated each time `gradient` is called by appending the proportion of the data used when calculating the approximate gradient since the class was initialised (a full gradient calculation would be 1 full data pass). Warning: if your functions do not contain an equal `amount` of data, for example your data was not partitioned into equal batches, then you must first use the `set_data_partition_weights" function for this to be accurate.   """
         data_passes = []
-        for el in self._data_passes_indices:
+        for el in self.data_passes_indices:
             try:
                 data_passes.append(data_passes[-1])
             except IndexError: