Contributed to the liquidinstruments/moku-examples repository by developing and maintaining cross-language instrument control and data acquisition examples, primarily in Python and MATLAB. Focused on API integration, code maintenance, and documentation, this work included implementing new features such as deep memory mode for oscilloscopes, enhancing streaming and plotting capabilities, and aligning hardware configuration templates with evolving device requirements. Addressed API compatibility and usability by updating function signatures, improving error handling, and refining onboarding documentation. Emphasized clarity and consistency across languages, ensuring robust example scripts and reducing support overhead. The approach balanced feature delivery with ongoing maintenance and cross-platform reliability.
March 2026 monthly summary for liquidinstruments/moku-examples: Prioritized MATLAB frontend API compatibility, implementing a parameter cleanup to align with the updated MATLAB API. Removed the gain parameter from set_frontend calls to reflect the current function signature, reducing potential runtime errors and improving cross-language interoperability. The change was implemented in commit 4de547f424cb5f1561e774f8f2aa26c51dc94db9. Result: smoother MATLAB onboarding, fewer API mismatches, and a solid foundation for future API evolution.
March 2026 monthly summary for liquidinstruments/moku-examples: Prioritized MATLAB frontend API compatibility, implementing a parameter cleanup to align with the updated MATLAB API. Removed the gain parameter from set_frontend calls to reflect the current function signature, reducing potential runtime errors and improving cross-language interoperability. The change was implemented in commit 4de547f424cb5f1561e774f8f2aa26c51dc94db9. Result: smoother MATLAB onboarding, fewer API mismatches, and a solid foundation for future API evolution.
September 2025 summary for liquidinstruments/moku-examples: Maintained API stability for streaming/logging, improved data visualization readability, and streamlined MATLAB integration through targeted fixes and documentation cleanup. These efforts reduced user friction, enhanced analysis workflows, and aligned MATLAB documentation with current conventions, delivering clear business value and improved developer experience.
September 2025 summary for liquidinstruments/moku-examples: Maintained API stability for streaming/logging, improved data visualization readability, and streamlined MATLAB integration through targeted fixes and documentation cleanup. These efforts reduced user friction, enhanced analysis workflows, and aligned MATLAB documentation with current conventions, delivering clear business value and improved developer experience.
In 2025-08, delivered targeted enhancements to the liquidinstruments/moku-examples repository focusing on documentation clarity, repository hygiene, and MATLAB API reliability to accelerate customer onboarding and reduce support overhead.
In 2025-08, delivered targeted enhancements to the liquidinstruments/moku-examples repository focusing on documentation clarity, repository hygiene, and MATLAB API reliability to accelerate customer onboarding and reduce support overhead.
March 2025 performance highlights for liquidinstruments/moku-examples: Focused on MCC-based onboarding and multi-instrument workflows, delivering key features, reliability improvements, and clear business value across MATLAB and Python examples.
March 2025 performance highlights for liquidinstruments/moku-examples: Focused on MCC-based onboarding and multi-instrument workflows, delivering key features, reliability improvements, and clear business value across MATLAB and Python examples.
February 2025 performance summary for liquidinstruments/moku-examples: Delivered a new Oscilloscope Deep Memory Mode Example Script and integrated it into the README to illustrate deep memory mode acquisition for the Moku Oscilloscope. Updated copyright year to 2025 across Python and MATLAB example files; licensing/doc documentation updated. No major functional bugs fixed this month; focus on feature completion and documentation quality. This work improves user onboarding for deep memory mode and ensures consistency across languages and licensing.
February 2025 performance summary for liquidinstruments/moku-examples: Delivered a new Oscilloscope Deep Memory Mode Example Script and integrated it into the README to illustrate deep memory mode acquisition for the Moku Oscilloscope. Updated copyright year to 2025 across Python and MATLAB example files; licensing/doc documentation updated. No major functional bugs fixed this month; focus on feature completion and documentation quality. This work improves user onboarding for deep memory mode and ensures consistency across languages and licensing.
January 2025 monthly summary for liquidinstruments/moku-examples. Delivered key API and hardware configuration enhancements, plus documentation improvements. Phasemeter and Waveform Generator Python API enhancements enable plotting, streaming, and triggered waveform generation, improving automation. Moku Cloud Compile template now supports all 16 controls and an external trigger input, expanding hardware configuration scenarios. Documentation fixes correct broken links across DSP, HDLCoder, and Python API docs, reducing onboarding friction. Overall, these changes improve developer productivity, reliability, and business readiness by enabling richer experiments and easier maintenance.
January 2025 monthly summary for liquidinstruments/moku-examples. Delivered key API and hardware configuration enhancements, plus documentation improvements. Phasemeter and Waveform Generator Python API enhancements enable plotting, streaming, and triggered waveform generation, improving automation. Moku Cloud Compile template now supports all 16 controls and an external trigger input, expanding hardware configuration scenarios. Documentation fixes correct broken links across DSP, HDLCoder, and Python API docs, reducing onboarding friction. Overall, these changes improve developer productivity, reliability, and business readiness by enabling richer experiments and easier maintenance.
Monthly summary for 2024-11 concentrated on delivering cross-language Time Frequency Analyzer (TFA) examples and documentation in the moku-examples repository. Implemented Python and MATLAB usage examples for TFA, with updated READMEs that cover basic usage, configuration, statistics retrieval, and plotting guidance. This work enables users to configure TFA, retrieve statistics, plot histograms, and understand TFA usage across languages, improving onboarding and reducing support needs.
Monthly summary for 2024-11 concentrated on delivering cross-language Time Frequency Analyzer (TFA) examples and documentation in the moku-examples repository. Implemented Python and MATLAB usage examples for TFA, with updated READMEs that cover basic usage, configuration, statistics retrieval, and plotting guidance. This work enables users to configure TFA, retrieve statistics, plot histograms, and understand TFA usage across languages, improving onboarding and reducing support needs.
Overview of all repositories you've contributed to across your timeline