liquidinstruments/moku-examples
Nov 2024 – Jan 2025
2 Months active
Languages Used
MATLABPython
Technical Skills
Data AcquisitionEmbedded SystemsMATLAB ScriptingSignal ProcessingData AnalysisData Visualization
LI-LeWang
PROFILE
Li-lewang
Le developed end-to-end deep memory mode data acquisition and analysis workflows for the liquidinstruments/moku-examples repository, focusing on both MATLAB and Python environments. They engineered configurable scripts to capture, process, and visualize high-resolution oscilloscope data, supporting flexible trigger, timebase, and acquisition settings. Their approach emphasized reproducibility and data integrity, introducing robust data saving, NumPy conversion, and post-processing routines. Le refined data loading to extract specific channel data and compute averages across acquisitions, and improved plotting for clearer visualization. Leveraging skills in data acquisition, embedded systems, and signal processing, Le delivered well-structured, maintainable solutions that enhance measurement reliability and user efficiency.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
251
Activity Months2
Your Network
23 people
Work History
January 2025
4 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for liquidinstruments/moku-examples: Delivered end-to-end Oscilloscope Deep Memory Mode workflow improvements, including a new Python acquisition script, refined acquisition parameters, enhanced data saving/NumPy conversion, and plotting refinements. The work tightened data loading to correctly extract channel A data and compute averages across acquisitions, improving data integrity and analysis reliability. These changes enable longer captures, faster insights, and more reproducible measurements with improved visualization and execution consistency.
4 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for liquidinstruments/moku-examples: Delivered end-to-end Oscilloscope Deep Memory Mode workflow improvements, including a new Python acquisition script, refined acquisition parameters, enhanced data saving/NumPy conversion, and plotting refinements. The work tightened data loading to correctly extract channel A data and compute averages across acquisitions, improving data integrity and analysis reliability. These changes enable longer captures, faster insights, and more reproducible measurements with improved visualization and execution consistency.
January 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary focusing on key accomplishments and business value. Delivered a new MATLAB-based data acquisition and analysis workflow for Moku Oscilloscope deep memory mode, enabling high-resolution capture, flexible configuration, and streamlined MATLAB-based post-processing. No major bugs fixed this month in the liquidinstruments/moku-examples repository. Overall impact centers on improved data fidelity, reproducibility, and user efficiency for advanced waveform analysis.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary focusing on key accomplishments and business value. Delivered a new MATLAB-based data acquisition and analysis workflow for Moku Oscilloscope deep memory mode, enabling high-resolution capture, flexible configuration, and streamlined MATLAB-based post-processing. No major bugs fixed this month in the liquidinstruments/moku-examples repository. Overall impact centers on improved data fidelity, reproducibility, and user efficiency for advanced waveform analysis.
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture76.0%
Performance68.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MATLABPython
Technical Skills
Data AcquisitionData AnalysisData VisualizationEmbedded SystemsMATLAB ScriptingPython ScriptingScientific ComputingSignal Processing
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