Support numpy 2

.github/workflows/build.yml

@@ -27,8 +27,8 @@ jobs:
     runs-on: [self-hosted, python, cuda]
     strategy:
       matrix:
-        python-version: [3.11]
-        numpy-version: [1.25]
+        python-version: [3.12]
+        numpy-version: [2]
     steps:
     - uses: actions/checkout@v6
       with: {fetch-depth: 0, submodules: recursive}
@@ -75,13 +75,13 @@ jobs:
     - id: matrix
       run: |
         if ${{ startsWith(github.ref, 'refs/tags') || github.event_name == 'workflow_dispatch' }}; then
-          echo "python-version=['3.10', 3.11]" >> $GITHUB_OUTPUT
-          echo "numpy-version=[1.23, 1.24, 1.25, 1.26]" >> $GITHUB_OUTPUT
+          echo "python-version=[3.11, 3.12]" >> $GITHUB_OUTPUT
+          echo "numpy-version=[1.26, '2.0', 2.1, 2.2, 2.3, 2.4]" >> $GITHUB_OUTPUT
           echo "os=['ubuntu', 'windows']" >> $GITHUB_OUTPUT
           include_max=true
         else
           echo "python-version=['3.10']" >> $GITHUB_OUTPUT
-          echo "numpy-version=[1.23]" >> $GITHUB_OUTPUT
+          echo "numpy-version=[2]" >> $GITHUB_OUTPUT
           if ${{ github.ref == 'refs/heads/master' }}; then
             echo "os=['ubuntu', 'windows']" >> $GITHUB_OUTPUT
             include_max=true
@@ -91,8 +91,8 @@ jobs:
           fi
         fi
         if $include_max; then
-          echo "include=[{'os': 'ubuntu', 'python-version': 3.12, 'numpy-version': 1.26}, {'os': 'windows', 'python-version': 3.12, 'numpy-version': 1.26}]" >> $GITHUB_OUTPUT
-          echo "conda-include=[{'os': 'ubuntu', 'python-version': 3.12}, {'os': 'windows', 'python-version': 3.12}]" >> $GITHUB_OUTPUT
+          echo "include=[{'os': 'ubuntu', 'python-version': 3.13, 'numpy-version': 2}, {'os': 'windows', 'python-version': 3.13, 'numpy-version': 2}]" >> $GITHUB_OUTPUT
+          echo "conda-include=[{'os': 'ubuntu', 'python-version': 3.13}, {'os': 'windows', 'python-version': 3.13}]" >> $GITHUB_OUTPUT
         else
           echo "include=[]" >> $GITHUB_OUTPUT
           echo "conda-include=[]" >> $GITHUB_OUTPUT
@@ -111,7 +111,7 @@ jobs:
     - uses: actions/checkout@v6
       with: {fetch-depth: 0, submodules: recursive}
     - name: set requirements
-      run: sed -ri -e 's/(.* python) .*/\1=${{ matrix.python-version }}/' -e 's/(.* numpy) .*/\1=${{ matrix.numpy-version }}/' -e 's/cuda*/py*/' -e '/tigre/d' scripts/cil_development.yml
+      run: sed -ri -e 's/(.* python) .*/\1=${{ matrix.python-version }}/' -e 's/(.* numpy) .*/\1=${{ matrix.numpy-version }}/' -e 's/cuda*/cpu*/' -e '/tigre/d' scripts/cil_development.yml
     - uses: conda-incubator/setup-miniconda@v4
       with:
         miniforge-version: latest
@@ -143,7 +143,7 @@ jobs:
       with:
         recipe-path: recipe.yaml
         rattler-build-version: v0.65.0 # https://github.com/prefix-dev/rattler-build/pull/2484
-        build-args: --experimental -c conda-forge -c ccpi -c astra-toolbox --variant python=${{ matrix.python-version }}
+        build-args: --experimental -c astra-toolbox -c conda-forge -c httomo -c ccpi -c ccpi/label/dev --variant python=${{ matrix.python-version }}
         artifact-name: conda-py${{ matrix.python-version }}-${{ matrix.os }}
   conda-upload:
     if: github.ref == 'refs/heads/master' || startsWith(github.ref, 'refs/tags')
@@ -194,7 +194,7 @@ jobs:
     - name: install dependencies
       run: |
         mamba install -c conda-forge -yq conda-merge
-        sed -ri -e 's/(.* python) .*/\1=3.12/' -e 's/cuda*/py*/' -e '/tigre/d' ../scripts/cil_development.yml
+        sed -ri -e 's/(.* python) .*/\1=3.12/' -e 's/cuda*/cpu*/' -e '/tigre/d' ../scripts/cil_development.yml
         conda-merge ../scripts/cil_development.yml docs_environment.yml > environment.yml
         mamba env update -n test --file environment.yml
         conda list

.gitignore

@@ -4,5 +4,6 @@ __pycache__/
 /Wrappers/Python/cil-*.dist-info/
 /Wrappers/Python/cil/include/
 /Wrappers/Python/cil/lib/
+/Wrappers/Python/build/
 *.vscode*
 *.egg*

CHANGELOG.md

@@ -21,6 +21,12 @@
     - Update FindIPP.cmake to find IPP libraries in conda environments (#2286)
     - Update to ASTRA-TOOLBOX version v2.4 from the `astra-toolbox` channel (#2330)
     - Update to TIGRE v3.1.3 (#2317)
+    - Added support for numpy 2 (#2218)
+      - Added utililty method `dtype_like` to convert an input value to the same type as a reference array
+      - Added documentation to developer guide on scalar types with numpy>=2.
+    - Update to CCPi-Regularisation toolkit v25.0.0 (#2218)
+    - Update to Tomophantom v3.1.4 (#2218)
+    - Added support for python 3.13 (#2218)
   - Documentation:
     - Render the user showcase notebooks in the documentation (#2189)
     - Update on build instructions in README and developer guide for all OS (#2286)

README.md

@@ -19,7 +19,7 @@ Binary installation of CIL can be achieved with `conda`.
 We recommend using either [`miniconda`](https://docs.conda.io/projects/miniconda/en/latest) or [`miniforge`](https://github.com/conda-forge/miniforge), which are both minimal installers for `conda`. We also recommend a `conda` version of at least `23.10` for quicker installation.
 
 #### Create a conda environment with CIL
-We maintain an environment file with the required packages to run the [CIL demos](https://github.com/TomographicImaging/CIL-Demos) which you can use to create a new environment. This will have specific and tested versions of all dependencies that are outlined in the table below: 
+We maintain an environment file with the required packages to run the [CIL demos](https://github.com/TomographicImaging/CIL-Demos) which you can use to create a new environment. This will have specific and tested versions of all dependencies that are outlined in the table below:
 
 ```sh
 conda env create -f https://tomographicimaging.github.io/scripts/env/cil_demos.yml
@@ -47,22 +47,21 @@ While building the CIL package we test with specific versions of dependencies. T
 
 | Package | Tested Version |  Conda install command | Description | License |
 |----|----|--------|--------|----|
-| [Python](https://www.python.org/) | 3.10 - 3.12 | `"python>=3.10,<=3.12"` || [PSF-2.0](https://docs.python.org/3/license.html) |
-| [Numpy](https://github.com/numpy/numpy) | 1.23 - 1.26 | `"numpy>=1.23,<2"` || [BSD-3-Clause](https://numpy.org/doc/stable/license.html) |
-| [IPP](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ipp.html#gs.gxwq5p) | 2021.12 | `-c https://software.repos.intel.com/python/conda ipp=2021.12` | The Intel Integrated Performance Primitives Library (required for the CIL recon class). | [ISSL](http://www.intel.com/content/www/us/en/developer/articles/license/end-user-license-agreement.html) |
+| [Python](https://www.python.org/) | 3.10 - 3.13 | `"python>=3.10,<=3.13"` || [PSF-2.0](https://docs.python.org/3/license.html) |
+| [Numpy](https://github.com/numpy/numpy) | 2.0 - 2.4 | `"numpy>=2,<=2.4"` || [BSD-3-Clause](https://numpy.org/doc/stable/license.html) |
+| [IPP](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ipp.html#gs.gxwq5p) | 2021.12 | `-c https://tomography.stfc.ac.uk/conda ipp=2021.12` | The Intel Integrated Performance Primitives Library (required for the CIL recon class). | [ISSL](http://www.intel.com/content/www/us/en/developer/articles/license/end-user-license-agreement.html) |
 |--|--| **Optional dependencies** |--|--|
-| [ASTRA toolbox](http://www.astra-toolbox.com) | 2.1 | CPU: `conda-forge::astra-toolbox=2.1=py*` <br> GPU: `conda-forge::astra-toolbox=2.1=cuda*` | CT projectors, FBP and FDK. | [GPL-3.0](https://github.com/astra-toolbox/astra-toolbox/blob/master/COPYING) |
-| [TIGRE](https://github.com/CERN/TIGRE) | 2.6 | `ccpi::tigre=2.6` | CT projectors, FBP and FDK. | [BSD-3-Clause](https://github.com/CERN/TIGRE/blob/master/LICENSE.txt) |
-| [CCPi Regularisation Toolkit](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit) | 24.0.1 | `ccpi::ccpi-regulariser=24.0.1` | Toolbox of regularisation methods. | [Apache-2.0](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit/blob/master/LICENSE) |
-| [TomoPhantom](https://github.com/dkazanc/TomoPhantom) | [3.0.3](https://github.com/dkazanc/TomoPhantom/releases/tag/v3.0) | `ccpi::tomophantom=3.0.3` | Generates phantoms to use as test data. | [Apache-2.0](https://github.com/dkazanc/TomoPhantom/blob/master/LICENSE) |
+| [ASTRA toolbox](http://www.astra-toolbox.com) | 2.0 - 2.4 | `astra-toolbox::astra-toolbox=2.4` | CT projectors, FBP and FDK. | [GPL-3.0](https://github.com/astra-toolbox/astra-toolbox/blob/master/COPYING) |
+| [TIGRE](https://github.com/CERN/TIGRE) | 3.1.3 | `ccpi::tigre=3.1.3` | CT projectors, FBP and FDK. | [BSD-3-Clause](https://github.com/CERN/TIGRE/blob/master/LICENSE.txt) |
+| [CCPi Regularisation Toolkit](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit) | 25.0.0 | CPU: `ccpi::ccpi-regulariser=25.0.0=cpu*` <br> GPU: `ccpi::ccpi-regulariser=25.0.0=cuda*` | Toolbox of regularisation methods. | [Apache-2.0](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit/blob/master/LICENSE) |
+| [TomoPhantom](https://github.com/dkazanc/TomoPhantom) | 3.0.3 - 3.1.4 | `httomo::tomophantom=3.1.4` | Generates phantoms to use as test data. | [Apache-2.0](https://github.com/dkazanc/TomoPhantom/blob/master/LICENSE) |
 | [ipykernel](https://github.com/ipython/ipykernel) || `ipykernel` | Provides the IPython kernel to run Jupyter notebooks. | [BSD-3-Clause](https://github.com/ipython/ipykernel/blob/main/LICENSE) |
 | [ipywidgets](https://github.com/jupyter-widgets/ipywidgets) || `ipywidgets` | Enables visualisation tools within jupyter noteboooks. | [BSD-3-Clause](https://github.com/jupyter-widgets/ipywidgets/blob/main/LICENSE) |
 |[zenodo_get](https://github.com/dvolgyes/zenodo_get)|>= 1.6|`zenodo_get>=1.6`| Downloads datasets from Zenodo, is used by `dataexample` to get data used in CIL-Demos |[AGPL-3.0](https://github.com/dvolgyes/zenodo_get?tab=AGPL-3.0-1-ov-file)|
 |--|--| **CT data readers** |--|--|
 |[olefile](https://github.com/decalage2/olefile)|>= 0.46|`olefile>=0.46`|Package to process Microsoft OLE2 files, used to read ZEISS data files.|[BSD-style (custom)](https://github.com/decalage2/olefile?tab=License-1-ov-file)|
 |[dxchange](https://github.com/data-exchange/dxchange)|>= 0.2.1|`dxchange>=0.2.1`|Provides an interface with TomoPy for loading tomography data.|[BSD-style (custom)](https://github.com/data-exchange/dxchange?tab=License-1-ov-file)|
 
-
 ### Docker
 
 Finally, CIL can be run via a Jupyter Notebook enabled Docker container:

Wrappers/Python/cil/framework/acquisition_data.py

@@ -27,7 +27,7 @@
 class AcquisitionData(DataContainer, Partitioner):
     """
     DataContainer for holding 2D or 3D sinogram
-    
+
     Parameters
     ----------
     array : numpy.ndarray or DataContainer
@@ -36,10 +36,10 @@ class AcquisitionData(DataContainer, Partitioner):
         If True, the array will be deep copied. If False, the array will be shallow copied.
     geometry : AcquisitionGeometry
         The geometry of the data. If the dtype of the array and geometry are different, the geometry dtype will be overridden.
-    
+
     **kwargs:
         dtype : numpy.dtype
-            Specify the data type of the AcquisitionData array, this is useful if you pass None to array and want to over-ride the dtype of the geometry. 
+            Specify the data type of the AcquisitionData array, this is useful if you pass None to array and want to over-ride the dtype of the geometry.
             If an array is passed, dtype must match the dtype of the array.
 
     """
@@ -84,7 +84,7 @@ def __init__(self,
             if dtype is None:
                 dtype = geometry.dtype
             array = numpy.empty(geometry.shape, dtype)
-    
+
         elif issubclass(type(array) , DataContainer):
             array = array.as_array()
 
@@ -107,12 +107,12 @@ def __eq__(self, other):
         '''
         Check if two AcquisitionData objects are equal. This is done by checking if the geometry, data and dtype are equal.
         Also, if the other object is a numpy.ndarray, it will check if the data and dtype are equal.
-        
+
         Parameters
         ----------
         other: AcquisitionData or numpy.ndarray
             The object to compare with.
-        
+
         Returns
         -------
         bool
@@ -123,12 +123,12 @@ def __eq__(self, other):
             if numpy.array_equal(self.as_array(), other.as_array()) \
                 and self.geometry == other.geometry \
                 and self.dtype == other.dtype:
-                return True 
+                return True
         elif numpy.array_equal(self.as_array(), other) and self.dtype==other.dtype:
             return True
         else:
             return False
-        
+
     def _get_slice(self, **kwargs):
         '''
         Functionality of get_slice
@@ -165,8 +165,8 @@ def _get_slice(self, **kwargs):
             return out
         else:
             return AcquisitionData(out.array, deep_copy=False, geometry=geometry_new)
-        
-    
+
+
     def get_slice(self, *args, **kwargs):
         '''
         Returns a new AcquisitionData of a single slice in the requested direction.
@@ -205,10 +205,10 @@ def get_slice(self, *args, **kwargs):
                 raise TypeError(f"Cannot use 'projection' for geometries that use angles. Use 'angle' instead.")
             elif key not in AcquisitionDimension and key != 'force':
                 raise TypeError(f"'{key}' not in allowed labels {AcquisitionDimension}.")
-            
+
         if args:
             warnings.warn("Positional arguments for get_slice are deprecated. Use keyword arguments instead.", DeprecationWarning, stacklevel=2)
-            
+
             num_args = len(args)
 
             if num_args > 0:

Wrappers/Python/cil/framework/acquisition_geometry.py

@@ -1980,7 +1980,7 @@ def shape(self):
                         AcquisitionDimension.HORIZONTAL: self.config.panel.num_pixels[0]}
 
 
-        return tuple(shape_dict[label] for label in self.dimension_labels)
+        return tuple(int(shape_dict[label]) for label in self.dimension_labels)
 
     @property
     def dimension_labels(self):

Wrappers/Python/cil/framework/block.py

@@ -733,9 +733,7 @@ def __neg__(self):
         return -1 * self
 
     def dot(self, other):
-#
-        tmp = [ self.containers[i].dot(other.containers[i]) for i in range(self.shape[0])]
-        return sum(tmp)
+        return sum(self.containers[i].dot(other.containers[i]) for i in range(self.shape[0]))
 
     def __len__(self):