bump everything

.github/workflows/README.md

@@ -23,7 +23,7 @@ Details on some of these jobs are given below.
 
 ## conda
 
-When opening or modifying a pull request to `master`, a single variant is built and tested. This variant is for linux with `python=3.9` and `numpy=1.22`.
+When opening or modifying a pull request to `master`, a single variant is built and tested. This variant is for linux with `python=3.11` and `numpy=1.25`.
 
 > [!NOTE]
 > The action does not publish to conda, instead this is done by jenkins. We will eventually move from jenkins to conda-forge instead.

.github/workflows/build.yml

@@ -40,8 +40,8 @@ jobs:
     runs-on: [self-hosted, python, cuda]
     strategy:
       matrix:
-        python-version: [3.9]
-        numpy-version: [1.22]
+        python-version: [3.11]
+        numpy-version: [1.25]
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
@@ -80,15 +80,15 @@ jobs:
     strategy:
       matrix:
         include:
-        - python-version: 3.8
-          numpy-version: 1.21
         - python-version: '3.10'
-          numpy-version: 1.24
+          numpy-version: 1.23
+        - python-version: 3.12
+          numpy-version: 1.26
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
     - name: set requirements
-      run: sed -ri -e '/ python=/d' -e 's/(.* numpy=).*/\1${{ matrix.numpy-version }}/' scripts/requirements-test.yml
+      run: sed -ri -e '/ python=/d' -e 's/(.* numpy=).*/\1${{ matrix.numpy-version }}/' -e 's/=cuda*//' -e '/tigre/d' scripts/requirements-test.yml
     - uses: conda-incubator/setup-miniconda@v3
       with:
         python-version: ${{ matrix.python-version }}
@@ -105,8 +105,8 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.9]
-        numpy-version: [1.22]
+        python-version: [3.11]
+        numpy-version: [1.25]
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
@@ -132,12 +132,12 @@ jobs:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
     - uses: conda-incubator/setup-miniconda@v3
-      with: {python-version: '3.10'}
+      with: {python-version: 3.11}
     - name: install dependencies
       run: |
         cd docs
         conda install -c conda-forge -yq conda-merge
-        conda-merge ../scripts/requirements-test.yml docs_environment.yml > environment.yml
+        conda-merge ../scripts/requirements-test.yml docs_environment.yml | sed '/tigre/d' > environment.yml
         conda env update -n test
         conda list
     - name: build cil

CHANGELOG.md

@@ -1,4 +1,11 @@
-* x.x.x
+* 24.0.0
+  - Update to new CCPi-Regularisation toolkit v24.0.0. This is a backward incompatible release of the toolkit.
+  - CIL plugin support for TIGRE version v2.6
+  - CIL plugin support for ASTRA-TOOLBOX version v2.1
+  - Dropped support for python 3.8 and 3.9
+  - Added support for python 3.11 and 3.12
+  - Dropped support for numpy 1.21 and 1.22
+  - Added support for numpy 1.25 and 1.26
   - Set CMake Policy CMP0148 to OLD to avoid warnings in CMake 3.27
   - AcquisitionGeometry prints the first and last 10 angles, or all if there are 30 or less, rather than the first 20
   - Added a weight argument to the L1Norm function
@@ -48,7 +55,6 @@
     - `TXRMDataReader`
   - Added the ApproximateGradientSumFunction and SGFunction to allow for stochastic gradient algorithms to be created using functions with an approximate gradient and deterministic algorithms
 
-
 * 23.1.0
   - Fix bug in IndicatorBox proximal_conjugate
   - Allow CCPi Regulariser functions for non CIL object

Dockerfile

@@ -11,7 +11,7 @@ LABEL org.opencontainers.image.source=https://github.com/TomographicImaging/CIL
 LABEL org.opencontainers.image.licenses="Apache-2.0 AND BSD-3-Clause AND GPL-3.0"
 
 # CUDA-specific packages
-ARG CIL_EXTRA_PACKAGES="tigre=2.4 astra-toolbox<2.1"
+ARG CIL_EXTRA_PACKAGES="tigre=2.6 astra-toolbox=2.1.0=cuda*"
 # build & runtime dependencies
 # TODO: sync scripts/create_local_env_for_cil_development.sh, scripts/requirements-test.yml, recipe/meta.yaml (e.g. missing libstdcxx-ng _openmp_mutex pip)?
 # vis. https://github.com/TomographicImaging/CIL/pull/1590
@@ -20,7 +20,7 @@ COPY --chown="${NB_USER}" scripts/requirements-test.yml environment.yml
 RUN sed -ri '/tigre|astra-toolbox/d' environment.yml \
   && for pkg in jupyter-server-proxy $CIL_EXTRA_PACKAGES; do echo "  - $pkg" >> environment.yml; done \
   && conda config --env --set channel_priority strict \
-  && for ch in defaults ccpi intel conda-forge; do conda config --env --add channels $ch; done \
+  && for ch in defaults nvidia ccpi intel 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

@@ -29,15 +29,16 @@ conda create --name cil -c conda-forge -c intel -c ccpi cil=23.1.0
 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=23.1.0 astra-toolbox tigre ccpi-regulariser tomophantom ipywidgets
+conda create --name cil -c conda-forge -c intel -c ccpi cil=23.1.0 astra-toolbox=*=cuda* tigre ccpi-regulariser tomophantom ipywidgets
 ```
 
 where:
-
-- `astra-toolbox` (requires an NVIDIA GPU) enables CIL support for [ASTRA toolbox](http://www.astra-toolbox.com) projectors (GPLv3 license)
+- `astra-toolbox` enables CIL support for [ASTRA toolbox](http://www.astra-toolbox.com) CPU projector (2D Parallel beam only) (GPLv3 license)
+- `astra-toolbox=*=cuda*` (requires an NVIDIA GPU) enables CIL support for [ASTRA toolbox](http://www.astra-toolbox.com) GPU projectors (GPLv3 license)
 - `tigre` (requires an NVIDIA GPU) enables support for [TIGRE](https://github.com/CERN/TIGRE) toolbox projectors (BSD license)
-- `ccpi-regulariser` is the [CCPi Regularisation Toolkit](https://github.com/vais-ral/CCPi-Regularisation-Toolkit)
+- `ccpi-regulariser` is the [CCPi Regularisation Toolkit](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit)
 - `tomophantom` can generate phantoms to use as test data [Tomophantom](https://github.com/dkazanc/TomoPhantom)
+- `ipywidgets` enables visulisation tools within jupyter noteboooks
 
 ### Dependencies
 

Wrappers/Python/cil/plugins/ccpi_regularisation/functions/regularisers.py

@@ -18,9 +18,9 @@
 
 try:
     from ccpi.filters import regularisers
-    from ccpi.filters.cpu_regularisers import TV_ENERGY
+    from ccpi.filters.TV import TV_ENERGY
 except ImportError as exc:
-    raise ImportError('Please `conda install "ccpi::ccpi-regulariser>=20.04"`') from exc
+    raise ImportError('Please `conda install "ccpi::ccpi-regulariser>=24"`') from exc
 
 
 from cil.framework import DataOrder
@@ -229,14 +229,17 @@ def _fista_on_dual_rof(self, in_arr, tau):
 
         """
 
-        res , info = regularisers.FGP_TV(\
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.FGP_TV(\
               in_arr,\
               self.alpha * tau,\
               self.max_iteration,\
               self.tolerance,\
               self.methodTV,\
               self.nonnegativity,\
-              self.device)
+              infovector = info,
+              device = self.device)
 
         return res, info
 
@@ -315,14 +318,18 @@ def alpha1(self):
         return 1.
 
     def proximal_numpy(self, in_arr, tau):
-        res , info = regularisers.TGV(in_arr,
+
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.TGV(in_arr,
               self.alpha * tau,
               self.alpha1,
               self.alpha2,
               self.max_iteration,
               self.LipshitzConstant,
               self.tolerance,
-              self.device)
+              infovector = info,
+              device = self.device)
 
         # info: return number of iteration and reached tolerance
         # https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
@@ -400,7 +407,10 @@ def __call__(self,x):
         return np.nan
 
     def proximal_numpy(self, in_arr, tau):
-        res , info = regularisers.FGP_dTV(\
+
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.FGP_dTV(\
                 in_arr,\
                 self.reference,\
                 self.alpha * tau,\
@@ -409,7 +419,8 @@ def proximal_numpy(self, in_arr, tau):
                 self.eta,\
                 self.methodTV,\
                 self.nonnegativity,\
-                self.device)
+                infovector = info,
+                device = self.device)
         return res, info
 
     def convex_conjugate(self, x):
@@ -454,7 +465,7 @@ def __call__(self,x):
     def proximal_numpy(self, in_arr, tau):
         # remove any dimension of size 1
         in_arr = np.squeeze(in_arr)
-
+    
         res = regularisers.TNV(in_arr,
               self.alpha * tau,
               self.max_iteration,

Wrappers/Python/cil/plugins/tigre/FBP.py

@@ -97,7 +97,9 @@ def process(self, out=None):
             arr_out = np.squeeze(arr_out, axis=0)
         else:
             if self.acquisition_geometry.geom_type == 'cone':
-                arr_out = fdk(self.get_input().as_array(), self.tigre_geom, self.tigre_angles)
+                # suppress print statements from TIGRE https://github.com/CERN/TIGRE/issues/532
+                with contextlib.redirect_stdout(io.StringIO()):
+                    arr_out = fdk(self.get_input().as_array(), self.tigre_geom, self.tigre_angles)
             else:
                 arr_out = fbp(self.get_input().as_array(), self.tigre_geom, self.tigre_angles)