liquidinstruments/moku-examples
Jan 2025 – Apr 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
API IntegrationData AnalysisEmbedded SystemsPythonScientific ComputingSignal Processing
jasonliquidinst
PROFILE
Jasonliquidinst
Jason developed a suite of Jupyter notebooks for the liquidinstruments/moku-examples repository, focusing on hands-on demonstrations of Moku device capabilities. He implemented features such as Allan deviation measurement, a digital phase-locked loop, and a Hello Moku connection example, using Python and scientific computing libraries to facilitate onboarding and customer engagement. In later work, Jason delivered end-to-end tutorials for PID control and frequency response analysis, as well as a logic analyzer distance measurement tool with UART decoding and a Tkinter GUI. He also reorganized and refactored the notebook structure, improving clarity, reproducibility, and maintainability for both users and developers.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
3
Lines of code
1,462
Activity Months2
Your Network
23 peopleSame Organization
@liquidinstruments.com10
Work History
April 2025
5 Commits • 2 Features
Apr 1, 2025April 2025 — Liquid Instruments (moku-examples): Delivered end-to-end tutorials for Moku devices, improved notebook organization, and reinforced reproducibility. Notable work includes two new notebooks: a Moku:Pro PID Controller and Frequency Response Analyzer Transfer Function Tutorial, and a Moku Logic Analyzer Distance Measurement with UART decoding and a Tkinter GUI. A major housekeeping effort renamed and reorganized notebooks for clarity with no functional changes. Impact: accelerates onboarding, enables faster experiments, and reduces maintenance overhead. Skills demonstrated: Python notebooks, instrument automation, UART decoding, Tkinter GUI, data visualization, and notebook refactoring.
5 Commits • 2 Features
Apr 1, 2025April 2025 — Liquid Instruments (moku-examples): Delivered end-to-end tutorials for Moku devices, improved notebook organization, and reinforced reproducibility. Notable work includes two new notebooks: a Moku:Pro PID Controller and Frequency Response Analyzer Transfer Function Tutorial, and a Moku Logic Analyzer Distance Measurement with UART decoding and a Tkinter GUI. A major housekeeping effort renamed and reorganized notebooks for clarity with no functional changes. Impact: accelerates onboarding, enables faster experiments, and reduces maintenance overhead. Skills demonstrated: Python notebooks, instrument automation, UART decoding, Tkinter GUI, data visualization, and notebook refactoring.
April 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for liquidinstruments/moku-examples: Delivered three Jupyter notebooks that demonstrate core Moku capabilities and provide ready-to-run demos for onboarding and customer engagements. The notebooks cover: (1) measuring Allan deviation using Time & Frequency Analyzer, (2) a digital phase-locked loop implementation with the Phasemeter, and (3) a basic Hello Moku connection/info retrieval example. All assets were added via a single commit, enabling quick replication and deployment in demos and tutorials. This work enhances hands-on experience, accelerates demonstrations, and contributes reusable templates for customer-facing material and internal testing.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for liquidinstruments/moku-examples: Delivered three Jupyter notebooks that demonstrate core Moku capabilities and provide ready-to-run demos for onboarding and customer engagements. The notebooks cover: (1) measuring Allan deviation using Time & Frequency Analyzer, (2) a digital phase-locked loop implementation with the Phasemeter, and (3) a basic Hello Moku connection/info retrieval example. All assets were added via a single commit, enabling quick replication and deployment in demos and tutorials. This work enhances hands-on experience, accelerates demonstrations, and contributes reusable templates for customer-facing material and internal testing.
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
API IntegrationControl SystemsData AcquisitionData AnalysisEmbedded SystemsFile ManagementGUI DevelopmentInstrument ControlJupyter NotebookPythonRefactoringScientific ComputingSignal ProcessingUART Communication
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