May 2026 monthly summary for mxcubecore: Focused on improving data reliability and configuration clarity. Delivered a warning mechanism for absent flux measurement and refactored data collection parameterization from 'overlap' to 'offset'. These changes reduce user confusion, prevent silent data gaps, and improve oscillation sequence configuration accuracy. Result: higher data quality, more robust experimentation workflows, and better maintainability.

March 2026 monthly summary for mxcubecore focused on improving parameter handling and workflow consistency for GPhL, with substantial code quality improvements and documentation that reduce future maintenance risk. Delivered a set of targeted changes to GPhL parameter handling, diffractometer workflows, and logging, alongside linting and refactoring to align with current project conventions.

February 2025 monthly summary for mxcube/mxcubecore. Delivered substantial enhancements to the GPhL workflow strategy and configuration, expanding support for DELPHI block size strategies, two-transmission strategies, and dose correction for non-uniform transmission. Implemented validation for delphi_block and stratcal_step, improved wedge_width enum, and updated configurations to reflect new strategies. Fixed Python 3.8 typing issue in LimsSessionManager by changing the 'users' attribute annotation to typing.Dict for better compatibility. These changes improve workflow flexibility, reliability, and maintainability, enabling more accurate experiments and faster iteration.

December 2024 delivered stability improvements to the GPhL workflow in mxcubecore, with fixes to energy handling, parameter reading, and rotation axis indexing, plus a refactor of dose budget calculation and ensured proper parameter usage in automation mode. Completed maintenance for workflow configuration and code quality, including Black formatting cleanup and yamllint updates to enable energy settings in the workflow. These changes reduced testing defects, improved automation reliability, and enhanced maintainability and onboarding for contributors.

Month: 2024-11 — mxcube/mxcubecore Overview: Focused on stabilizing core I/O, aligning APIs, correcting automation validation, and expanding diffraction plan capabilities to support more flexible experiments. Delivered fixes and enhancements that improve reliability, data integrity, and throughput in automated experiments, with clear business value. Key deliverables: - HDF5 interleaved file naming handling: Removed obsolete temporary fix and clarified that the underlying issue has been resolved; cleanup of interleaved scan naming to reduce complexity and potential confusion. Commits: a5fbd56ec165f802d63581dd29b224cbf492ba88; 58ec5fc02352d4097dbbabdb08f569a0b9ed3999 - CollectMockup snapshot API alignment: Updated to use the correct API for saving snapshots; aligns with expected 'save_snapshot' method to fix broken behavior. Commit: f4e7766c4453451c89ec82145a2b79a4d91fd2ca - Automation mode energy/wavelength validation bug: Refined validation logic to avoid ValueError when energies and wavelengths occupy available slots in automation mode; ensures consistent configuration when energies and wavelengths are set together earlier in the automation process. Commit: 8ed5ae1a4571c93e8196781327e5e6604fa4e08c - Extended diffraction plan support for experimental configuration: Add support for additional diffractionPlan parameters (exposureTime, oscillationRange, energy); refactor parameter extraction; add EMULATION_DATA entry for '4mxt' to enable more flexible diffraction experiments. Commit: eb9fe09c47b32de349d1556e32c51970b37034ea Impact and accomplishments: - Reliability: API compatibility and data handling stability improved; automation flows are more robust, reducing runtime errors. - Productivity: Fewer manual interventions during data collection; greater experimental flexibility with diffraction planning. - Technical capabilities: Strengthened Python codebase with more robust parameter extraction and API alignment; groundwork laid for future diffraction experiments. Technologies/skills demonstrated: - Python, HDF5 data handling, API integration, validation logic, and code refactoring - Parameter extraction and EMULATION_DATA configuration for flexible experiments - Cross-team collaboration between data acquisition and workflow layers Business value: - Reduced downtime due to naming ambiguities, automation-related crashes, and more adaptable diffraction planning, leading to improved data throughput and experiment efficiency.

Concise monthly summary for 2024-10: Implemented configurable suppression of external log output in GPhL workflows across core and web, enabling better verbosity control and cleaner logs. In mxcubecore, added suppress_external_log_output (default false), updated GPhL Workflow logic, and refreshed ESRF ID30a2 and mock environment configs to include the new setting. In mxcubeweb, introduced the same suppress_external_log_output option in GPhL workflow settings (default false) to ensure consistent behavior across platforms. Commits: ae4459aaad0d35de5e48fc06f1bab13afd3530ae; e1a873efed761d12f8a7db823eb25187aa1d3ae9. Overall impact: reduced log noise, improved user experience, and clearer operational signals during workflow executions. Technologies/skills demonstrated: Python, config-driven feature flags, logging control, cross-repo coordination, test and mock config maintenance.

2024-08 monthly summary for mxcube/mxcubeweb focused on stabilizing workflow configuration inputs and improving reliability for beamline operations. Key outcome: a critical bug fix corrected the gphl_beamline_config path so workflow inputs now point to an existing directory, eliminating a class of runtime errors that could disrupt beamline configuration. Impact: reduced downtime during workflows, lower support overhead, and a smoother user experience when configuring beamlines. This aligns with our business goal of reliable, maintainable automation in the MXCube web stack.

In July 2024, mxcube/mxcubecore delivered targeted YAML configuration improvements to strengthen config loading reliability and readability. The team fixed a parsing issue by addressing the YAML version declaration and introduced a standardized indentation scheme for YAML outputs. These changes reduce runtime configuration errors, improve maintainability, and provide a consistent foundation for future config-driven workflows.

Monthly performance summary for May 2024 focusing on delivering YAML-based configuration management improvements across core and web components, strengthening reliability, dev/test workflows, and business value.