GEECS-BELLA/GEECS-Plugins
Apr 2025 – Jun 2025
2 Months active
Languages Used
PythonYAML
Technical Skills
API IntegrationAsynchronous ProgrammingControl SystemsData VisualizationEnvironment ConfigurationPython
Hao Ding
PROFILE
Hao Ding
Developed advanced automation features for the GEECS-BELLA/GEECS-Plugins repository, focusing on real-time camera-based feedback loops and precision drift correction for piezo-driven stages. Leveraged Python and YAML to integrate with the GEECS-PythonAPI, enabling automated piezo actuation based on centroid feedback and reducing manual calibration. Implemented asynchronous programming and control systems to maintain target positioning, while enhancing data visualization for monitoring and validation. Introduced calibration factors and updated target values to improve alignment accuracy, and expanded test harnesses for robust validation. The work emphasized reliability and repeatability in dynamic environments, supporting more stable experimental workflows and streamlined instrumentation control.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
1,463
Activity Months2
Your Network
73 peopleSame Organization
@lbl.gov61
Shared Repositories
12
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Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for GEECS-Plugins focusing on delivering precision and reliability improvements in the piezo-driven stage. Key outcomes include a refined drift correction pipeline, alignment calibration enhancements, and an expanded test harness to visualize corrections. No major bug fixes reported for this period.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for GEECS-Plugins focusing on delivering precision and reliability improvements in the piezo-driven stage. Key outcomes include a refined drift correction pipeline, alignment calibration enhancements, and an expanded test harness to visualize corrections. No major bug fixes reported for this period.
June 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for GEECS-Plugins: Key features delivered include a real-time camera-based feedback loop with drift correction and automated piezo actuation to maintain a target position. This feature encompasses real-time camera monitoring with centroid-based drift correction, data visualization, and an automated piezo motor control that adjusts positioning based on feedback. The work involved integration with the GEECS-PythonAPI, updates to environment/config/test setups to support end-to-end operation, and active piezo actuation to sustain alignment. Major bugs fixed: none reported for this feature; integration adjustments and reliability improvements completed to ensure stable operation of the new control loop. Overall impact and accomplishments: Enables automated, high-precision positioning in dynamic environments, reducing manual calibration and enabling more reliable downstream processing and experiments. Demonstrated business value through improved automation, repeatability, and reduced operator intervention. Technologies/skills demonstrated: Real-time image processing and feedback control, drift correction algorithms, automated actuation control, Python API integration, test-driven environment/config updates, and data visualization.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for GEECS-Plugins: Key features delivered include a real-time camera-based feedback loop with drift correction and automated piezo actuation to maintain a target position. This feature encompasses real-time camera monitoring with centroid-based drift correction, data visualization, and an automated piezo motor control that adjusts positioning based on feedback. The work involved integration with the GEECS-PythonAPI, updates to environment/config/test setups to support end-to-end operation, and active piezo actuation to sustain alignment. Major bugs fixed: none reported for this feature; integration adjustments and reliability improvements completed to ensure stable operation of the new control loop. Overall impact and accomplishments: Enables automated, high-precision positioning in dynamic environments, reducing manual calibration and enabling more reliable downstream processing and experiments. Demonstrated business value through improved automation, repeatability, and reduced operator intervention. Technologies/skills demonstrated: Real-time image processing and feedback control, drift correction algorithms, automated actuation control, Python API integration, test-driven environment/config updates, and data visualization.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture73.4%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
API IntegrationAsynchronous ProgrammingControl SystemsData AcquisitionData VisualizationEnvironment ConfigurationPythonPython DevelopmentPython Scripting
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