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
Developed and delivered end-to-end deep memory mode data acquisition and analysis workflows for the liquidinstruments/moku-examples repository, focusing on both MATLAB and Python environments. Built a MATLAB script to enable high-resolution waveform capture and flexible configuration for the Moku Oscilloscope, streamlining post-processing and improving data fidelity. Extended the workflow with a Python script that refined acquisition parameters, enhanced data saving and NumPy conversion, and improved plotting for reliable visualization. Emphasized data integrity by tightening data loading and processing, ensuring accurate extraction and averaging of channel data. Leveraged skills in data acquisition, embedded systems, MATLAB scripting, Python scripting, and signal processing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
251
Activity Months2
Your Network
27 peopleSame Organization
@liquidinstruments.com12
Shared Repositories
15
Ben NizetteMember
Brian NeffMember
Dylan PerezMember
EC2 Default UserMember
EC2 Default UserMember
Heyang LongMember
Indira Shinn ReesMember
jasonliquidinstMember
jobinMember
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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