October 2025 monthly summary for stfc/st2-cloud-pack: Focused on optimizing FlavorPropertiesSensor behavior and improving overall system efficiency. Implemented a polling interval adjustment and ensured safe defaults for production stability.

September 2025 delivered core sensor capabilities and robust validation for flavor management in st2-cloud-pack, enhancing reliability, test coverage, and business value for flavor handling across environments.

Monthly summary for 2025-08 (stfc/st2-cloud-pack): Delivered two core features to improve multi-cloud image and flavor management, plus substantial maintenance to improve reliability and developer experience. The image metadata sensor was extended with a comprehensive payload schema, config refactors, adjusted polling intervals, tests, and introduced image-to-project sharing; the flavor synchronization feature introduced a new OpenStackFlavor API class, refined sensors, missing flavor detection configuration, and enhanced logging. Maintenance included removing references to the openstack-query-library, eliminating redundant modules, and comprehensive docstring and formatting cleanups, supported by unit tests.

June 2025 — stfc/st2-cloud-pack delivered a unified, refactored project creation workflow with expanded networking options (Internal, External, JASMIN) and added project_domain across the pipeline. The changes consolidate networking setup, include validation for JASMIN configurations, and introduce create_jasmin_project.py. In addition, a floating IP allocation fix was implemented to use the external_network variable, with tests updated. Extensive test coverage, docstring updates, and documentation cleanup accompany the release, driving more reliable provisioning and reduced misconfig risk.

April 2025 monthly summary focused on strengthening the Geometry and ImageStack data model in mantidimaging, with robust test coverage and tighter integration into the imaging workflow (ImageStack and CILRecon). The work reduces downstream risk in geometry/configuration handling and improves the reliability of reconstruction pipelines for larger datasets. Key outcomes include a targeted refactor of the Geometry class, enhanced ImageStack handling, and explicit constructor adjustments to set the projection flag, improving clarity and correctness across the stack.

March 2025 monthly summary for mantidimaging. Delivered a cohesive geometry management layer for ImageStack to stabilize reconstruction workflows and improve data handling. Implemented a dedicated ImageStackGeometry component and a Geometry member on ImageStack, enabling consistent storage and access to projection geometry. Established cross-compatibility by adding center-of-rotation (CoR) conversion to support MantidImaging and CIL conventions, and prepared ImageStack initialization with appropriate projection geometry. No major bug fixes were reported this month; the focus was on architectural foundations to enable faster feature delivery and more reliable runs.

February 2025: Mantid Imaging focused on strengthening user and developer support via targeted documentation improvements and formatting refinements. Implemented CUDA-enabled astra-toolbox guidance for non-GPU systems, added a new error scenario with a recommended fix (install Python-specific astra-toolbox), and corrected sub-subheading formatting to align with the project style guide. These changes improve cross-platform usability, documentation readability, and future maintainability.