Pre-release update for v25.0.0

CHANGELOG.md

@@ -1,9 +1,24 @@
-* XX.X
+* 25.0.0
   - New features:
     - Added `FunctionOfAbs` class (#1976)
     - Added `Cone3D_Flex` geometry as a CIL acquisition geometry type. This allows users to reconstruct non-circular trajectories. (#2039)
     - Added `ASTRA` backend support for `ProjectionOperator` and `FBP` to use `Cone3D_Flex` geometry. (#2039)
     - Added `show_system_positions` for visualisation of `Cone3D_Flex` geometries. (#2039)
+    - Added an Accelerated Proximal Gradient Descent Algorithm (APGD) with options for momentum (#2145)
+  - Enhancements:
+    - Added an accelerated version to the TransmissionAbsorption processor, controlled by the `accelerated` parameter (default is `True`) (#2036)
+    - Made the call to next() in algorithm iteration loop explicit (#2069)
+    - Added option for a random seed in the power method in the linear operator (#1585)
+    - Improved efficiency of `Normaliser` processor. Reduced memory use and increased speed (#2111)
+    - Extra functionality for sampler: `get_previous_samples()` and `get_current_sample()` (#2079)
+    - PDHG 'check_convergence' updated for new literature (#2084)
+    - Updated `create_local_env_for_cil_development.sh` script to work on Windows (#2144)
+    - Improved consistency of `step_size` property across GD, ISTA, FISTA and APGD algorithms (#2157)
+    - In PDHG algorithm, we now have options to initialise the dual variable, as well as the primal variable (#2169)
+    - Allow FluxNormaliser.preview_configuration() even if flux contains zeros (#2177)
+    - Check if kwargs are used in AcquisitionData and ImageData initialisation (#2178)
+    - Added a flag to `show_geometry` that allows for disabling the call to `plt.show()` (#2195)
+    - The random methods to populate a data container can now be accessed via `fill()` and `allocate()`, and will have a lower memory footprint on creation (#2037)
   - Bug fixes:
     - Fix deprecation warning for rtol and atol in GD (#2056)
     - Removed the deprecated usage of run method in test_SIRF.py (#2070)
@@ -14,49 +29,49 @@
     - Copy geometry in the creation of a DataContainer (#2108)
     - Fix order of operations for subtraction and division between a BlockDataContainer and DataContainer (#2133)
     - Update default compression for TIFFWriter setup (#2197)
+    - ProjectionOperator device input is no longer case-sensitive (#2065)
+    - Fix `pkg_resources` deprecation warning on `setuptools>=81 dxchange<=0.2.0` (#2207)
+  - Deprecated
+    - Positional arguments for `get_slice` on `AcquisitionGeometry` and `AcquisitionData` have been deprecated (#2039)
+    - Methods to allocate a `DataContainer` using the old numpy random number generator have been renamed and deprecated, `RANDOM`->`RANDOM_DEPRECATED` and `RANDOM_INT`->`RANDOM_INT_DEPRECATED`. See "Breaking Changes" for details of new behaviour (#2037)
+    - Sampler's `get_samples` has been deprecated, use `view_samples` (#2128)
+  - Removed code
+    - Removed the following code which has been deprecated since v23.0.0 or earlier (#2150):
+      - `dimension_labels` kwarg in `AcquisitionGeometry.allocate()` and in `ImageData`'s `__init__`
+      - `axpby` (alias of `sapyb`)
+      - `shape` setter in `DataContainer` and `ImageGeometry`
+      - use of integer compression in `NEXUSDataWriter`
+    - Removed unused kwargs passed to ImageData and AcquisitionData, including `suppress_warning` (#2206)
   - Documentation:
     - Updated documentation for the ChannelWiseOperator including new example (#2096)
     - Updated documentation for LADMM (#2015)
     - Updated Contributor's Guide to include an example of parametrized tests, using `unittest-parametrize` (#1990)
     - Tidied up documentation of `L1Norm` (#2186)
-  - Enhancements:
-    - Add accelerated version to TransmissionAbsorption processor, controlled by `accelerated` parameter, default is True (#2036)
-    - Made the call to next() in algorithm iteration loop explicit (#2069)
-    - Added option for a random seed in the power method in the linear operator (#1585)
-    - Improved efficiency of `Normaliser` processor. Reduced memory use and increased speed (#2111)
-    - Extra functionality for sampler: `get_previous_samples()` and `get_current_sample()` (#2079)
-    - Renamed Sampler's `get_samples` to `view_samples` (deprecating `get_samples`) (#2128)
-    - PDHG 'check_convergence' updated for new literature (#2084)
-    - Make install local env script work on windows (#2144)
-    - Added an Accelerated Proximal Gradient Descent Algorithm (APGD) with options for momentum (#2145)
-    - Improved consistency of `step_size` property across GD, ISTA, FISTA and APGD algorithms (#2157)
-    - In PDHG algorithm, we now have options to initialise the dual variable, as well as the primal variable (#2169)
-    - Allow FluxNormaliser.preview_configuration() even if flux contains zeros (#2177)
-    - Check if kwargs are used in AcquisitionData and ImageData initialisation (#2178)
-    - Added a flag to `show_geometry` that allows for disabling the call to `plt.show()` (#2195)
+    - Added callbacks to documentation (#2067)
+    - Added new tutorials to the "How-To" page in the docs (#1972)
   - Testing:
     - Developers can now run the full CI matrix [via the web UI](https://github.com/TomographicImaging/CIL/actions/workflows/build.yml) (#2160)
     - Added tests for ProjectionOperator inputs that use `unittest-parametrize` module (#1990)
     - Added tests for Normaliser processor
     - Update minimum cmake version to 3.5
     - Added unit test for the `ZEISSDataReader` (#2098)
+    - Added `requirements-test-windows.yml` to create test environment (#2174)
+    - Automate Windows conda build & release (#1918)
   - Dependencies:
     - Move from CMake to `pip install` (#2145)
+    - Use conda native compilers (#2199)
     - matplotlib-base is an optional dependency, instead of required (#2093)
-    - `unittest-parametrize has been added as a dependency for tests (#1990)
-    - olefile and dxchange are an optional dependency, instead of required (#2149)
+    - `unittest-parametrize` has been added as a dependency for tests (#1990)
+    - Defer import of olefile and dxchange (#2149)
     - zenodo_get is an optional dependency, instead of required (#2146)
     - `FindIPP.cmake` fallback (#2148)
       - enable `find_package` using `*_ROOT` hints
       - simplified `cilacc` build logic for optional dependencies
-  - Removed the following code which has been deprecated since v23.0.0 or earlier (#2150):
-    - `dimension_labels` kwarg in `AcquisitionGeometry.allocate()` and in `ImageData`'s `__init__`
-    - `axpby` (alias of `sapyb`)
-    - `shape` setter in `DataContainer` and `ImageGeometry`
-    - use of integer compression in `NEXUSDataWriter`
-    - Removed unused kwargs passed to ImageData and AcquisitionData, including `suppress_warning` (#2206)
-  - Changes that break backwards compatibility:
-    - Updated `RANDOM` and `RANDOM_INT` `DataContainer.fill()` and `geometry.allocate()` methods to use numpy default random number generator, old methods can be accessed with `RANDOM_DEPRECATED` AND `RANDOM_INT_DEPRECATED`. Random methods can now be accessed from `fill()` and `allocate()` (#2037)
+    - Removed upper version constraints on optional plugins when resolving conda environment  (#2204)
+    - Relaxed numpy runtime pinning of versions (#2156)
+  - Breaking changes:
+    - `RANDOM` and `RANDOM_INT` options in `DataContainer.fill()` and `geometry.allocate()` methods now use numpy default random number generator to populate the array. Values for a set seed will now change compared to previous versions. Previous methods can be accessed with `RANDOM_DEPRECATED` and `RANDOM_INT_DEPRECATED`. (#2037)
+    - `RANDOM` and `RANDOM_INT` options in `DataContainer.fill()` and `geometry.allocate()` methods will no longer use a random seed set externally using `numpy.random.seed()`, the seed should be provided as an argument. `RANDOM_DEPRECATED` and `RANDOM_INT_DEPRECATED` will still use an externally set seed (#2037)
 
 * 24.3.0
   - New features:

CITATION.cff

@@ -31,6 +31,8 @@ authors:
   given-names: Hannah
 - family-names: Sugic
   given-names: Danica
+- family-names: Vidal
+  given-names: Franck
 title: Core Imaging Library (CIL)
 date-released: '2018-01-08'
 identifiers:

README.md

@@ -21,7 +21,7 @@ We recommend using either [`miniconda`](https://docs.conda.io/projects/miniconda
 Install a new minimal environment with CIL using:
 
 ```sh
-conda create --name cil -c https://software.repos.intel.com/python/conda -c conda-forge -c ccpi cil=24.3.0
+conda create --name cil -c https://software.repos.intel.com/python/conda -c conda-forge -c ccpi cil=25.0.0
 ```
 A number of additional dependencies are required for specific functionality in CIL, these should be added to your environment as necessary. See the dependency table below for details.
 
@@ -41,6 +41,7 @@ While building the CIL package we test with specific versions of dependencies. T
 | [TomoPhantom](https://github.com/dkazanc/TomoPhantom) | [2.0.0](https://github.com/dkazanc/TomoPhantom/releases/tag/v2.0.0) | `ccpi::tomophantom=2.0.0` | 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)|
 
 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 above: 
 

Wrappers/Python/cil/framework/acquisition_geometry.py

@@ -2321,7 +2321,7 @@ def create_Cone2D(source_position, detector_position, detector_direction_x=[1,0]
         Creates the AcquisitionGeometry for a cone beam 2D tomographic system.
 
         After creating the AcquisitionGeometry object, the panel must be set using the `set_panel()` method and the angles must be set using the `set_angles()` method.
-        In addition,`set_channels()` can be used to set the number of channels and their labels. `set_labels()` can be used to set the order of the dimensions describing the data.
+        In addition, `set_channels()` can be used to set the number of channels and their labels. `set_labels()` can be used to set the order of the dimensions describing the data.
 
         See notes for default directions.
 
@@ -2364,7 +2364,7 @@ def create_Parallel3D(ray_direction=[0,1,0], detector_position=[0,0,0], detector
         Creates the AcquisitionGeometry for a parallel beam 3D tomographic system.
 
         After creating the AcquisitionGeometry object, the panel must be set using the `set_panel()` method and the angles must be set using the `set_angles()` method.
-        In addition,`set_channels()` can be used to set the number of channels and their labels. `set_labels()` can be used to set the order of the dimensions describing the data.
+        In addition, `set_channels()` can be used to set the number of channels and their labels. `set_labels()` can be used to set the order of the dimensions describing the data.
 
         Parameters
         ----------
@@ -2413,7 +2413,7 @@ def create_Cone3D(source_position, detector_position, detector_direction_x=[1,0,
         Creates the AcquisitionGeometry for a cone beam 3D tomographic system.
 
         After creating the AcquisitionGeometry object, the panel must be set using the `set_panel()` method and the angles must be set using the `set_angles()` method.
-        In addition,`set_channels()` can be used to set the number of channels and their labels. `set_labels()` can be used to set the order of the dimensions describing the data.
+        In addition, `set_channels()` can be used to set the number of channels and their labels. `set_labels()` can be used to set the order of the dimensions describing the data.
 
         Parameters
         ----------
@@ -2460,7 +2460,7 @@ def create_Cone3D_Flex(source_position_set, detector_position_set, detector_dire
         Creates the AcquisitionGeometry for a per-projection cone beam 3D tomographic system.
 
         After creating the AcquisitionGeometry object, the panel must be set using the `set_panel()` method.
-        In addition,`set_channels()` can be used to set the number of channels and their labels. `set_labels()` can be used to set the order of the dimensions describing the data.
+        In addition, `set_channels()` can be used to set the number of channels and their labels. `set_labels()` can be used to set the order of the dimensions describing the data.
 
         Parameters
         ----------

docs/source/utilities.rst

@@ -37,9 +37,6 @@ A set of simulated volumes and CT data
 .. autoclass:: cil.utilities.dataexample.SIMULATED_PARALLEL_BEAM_DATA
    :members:
 
-.. autoclass:: cil.utilities.dataexample.SIMULATED_CONE_BEAM_DATA
-   :members:
-
 
 A CT dataset from the Diamond Light Source
 ------------------------------------------

pyproject.toml

@@ -68,7 +68,6 @@ dependencies = [
     "pywavelets",
     "scipy>=1.4.0",
     "tqdm",
-    "zenodo_get>=1.6",
 ]
 [project.optional-dependencies]
 plugins = [
@@ -83,11 +82,12 @@ gpu = [
 test = [
     #"ccpi-regulariser=24.0.1", # [not osx] # missing from PyPI
     "cvxpy",
-    "matplotlib>=3.3.0",
+    "matplotlib-base>=3.3",
     "packaging",
     "scikit-image",
     "unittest-parametrize",
     "wget",
+    "zenodo_get>=1.6",
 ]
 docs = [
   "jinja2",