January 2026: Bug fix delivering clearer normalization parameter guidance across multiple protocols in I2PC/scipion-em-xmipp. Addressed typos in the help text for normalization parameters across protocols (commit 8f54901d20573e8c9826a352ce8d533cc1905c43; message 'JMK typos in normalize helps (#987)'). No new features released this month; focus on documentation/UX improvement and cross-protocol consistency.

November 2025 monthly summary for I2PC/scipion-em-xmipp focusing on documentation quality and user clarity. Delivered targeted documentation improvement for the variance filter parameter in the extract particles protocol, fixing a typographical error ('filtter' -> 'filter') to enhance user understanding and reduce support queries. No additional features or bug fixes were committed this month beyond documentation refinements; the work emphasizes clarity, maintainability, and user onboarding.

April 2025 monthly summary focusing on delivering high-value features, improving reliability, and enabling advanced usage across I2PC/scipion-em-xmipp and I2PC/xmipp. Key features delivered include conditional Log Likelihood (LL) calculation, centralized LL filename generation, and clarified labeling/documentation for the compute likelihood protocol in scipion-em-xmipp, plus advanced gating of the Normalize option behind expertLevel in XmippProtComputeLikelihood. A related code hygiene improvement removed a deprecated call in the xmipp core.

March 2025: Delivered feature improvements and critical fixes across I2PC/xmipp and I2PC/scipion-em-xmipp. Highlights include fix for inverse transformation handling, comprehensive test-suite hardening, and broad usability/stability improvements in the scipion-em-xmipp integration, along with histogram/output enhancements and expanded test coverage.

February 2025 monthly summary focusing on delivering improved reconstruction fidelity, reproducibility, and reporting across two repositories (I2PC/xmipp and I2PC/scipion-em-xmipp). The team fixed a critical GPU cost calculation bug, expanded residuals tooling with a new continuous_create_residuals program and corresponding tests/docs, and extended the experimental residuals workflow with improved likelihood reporting, projections output, and clearer authorship attribution. These efforts strengthened the end-to-end analysis pipeline, data provenance, and developer tooling, while showcasing cross-repo collaboration and robust GPU-accelerated development.

January 2025 monthly summary for I2PC/scipion-em-xmipp: Delivered robustness, performance, and configurability improvements across the image processing pipeline. Key features delivered include robust LL sign keeping, GPU parallelism and resource management improvements, image noise options with safe defaults, SOS option for downstream flexibility, and initialization of pre- and post-CTF noise workflows. Major bug fixes addressed imed/var source, term1 safety, and NumPy-related CTF issues, contributing to safer, more reproducible results. Overall impact: increased reliability and throughput in GPU-accelerated runs, safer output handling, and better configurability for downstream workflows. Technologies demonstrated: CUDA visibility handling, dynamic GPU allocation, Python-based pipeline improvements, noise generation workflows, and robust formatting support for scientific results.

Concise monthly summary for 2024-12 focused on stabilizing the Xmipp workflow and enhancing interpretability of results in I2PC/scipion-em-xmipp. Defensive defaults and usability enhancements reduce operational risk and improve data-driven decision making.

Month 2024-11 highlights for I2PC/scipion-em-xmipp: Delivered substantial enhancements to likelihood computation, introduced a robust log-likelihood viewer, and enabled GPU-accelerated workflows. Focused on configurability, reliability, and performance to accelerate research workloads and improve result quality.

October 2024 monthly summary for I2PC/scipion-em-xmipp: Focused on increasing reconstruction fidelity and pipeline traceability. Delivered two key features in the XMIPP integration: Noise Mask for Likelihood Computation in High-Resolution Reconstruction and Input Volume Filename Tracking. Noise Mask isolates noise regions using particle and noise radii, computes variance over masked regions to improve likelihood estimation, and introduces a new noiseRadius parameter to bound the estimation area for multi-reference inputs. Input Volume Filename Tracking captures and stores the filenames of the first and second input volumes during the processing loop to enable reproducibility and referenceability in the high-resolution protocol. Key commits: 6c3af590dcc2ff9a963aab25479777aff58053a1; 667c804d9a4ae459dd32066f413bebc83ddb4348; 0a5bf8b53142fd9c3c5b5d6154631f8654bb77b7.