liquidinstruments/moku-examples
Nov 2024 – Aug 2025
3 Months active
Languages Used
MATLABMarkdownPythonMatlab
Technical Skills
API IntegrationData VisualizationDocumentationEmbedded SystemsInstrument ControlMATLAB Scripting
Indira Shinn Rees
PROFILE
Indira Shinn Rees
Indira contributed to the liquidinstruments/moku-examples repository by developing end-to-end neural network instrument demonstration scripts in Python and MATLAB, enabling ramp generation, neural network processing, and oscilloscope visualization with full instrument setup and data plotting. She migrated device connection examples from serial-number to IP-based methods, streamlining onboarding and aligning with modern network-based workflows. Indira also expanded support for the Moku:Delta device, updating APIs, HDL code generation, and data logging to ensure cross-device compatibility. Her work combined API development, embedded systems integration, and documentation improvements, resulting in deeper workflow coverage and reduced integration friction for users adopting new hardware.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
3
Lines of code
247
Activity Months3
Your Network
23 peopleSame Organization
@liquidinstruments.com10
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary: Expanded device support to Moku:Delta across the moku-examples workflow, enabling API access, HDL code generation, and data logging. Updated documentation and configuration dictionaries to include Delta, and ensured Delta is recognized and parameterized in data logging, oscilloscope deep memory mode, and HDL code generation. This work reduces integration friction for Delta users and broadens cross-device data capture and HDL generation capabilities.
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary: Expanded device support to Moku:Delta across the moku-examples workflow, enabling API access, HDL code generation, and data logging. Updated documentation and configuration dictionaries to include Delta, and ensured Delta is recognized and parameterized in data logging, oscilloscope deep memory mode, and HDL code generation. This work reduces integration friction for Delta users and broadens cross-device data capture and HDL generation capabilities.
August 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 (2025-01) — Summary of work in liquidinstruments/moku-examples. Key feature delivered: Migrated Python API examples from serial-number to IP-based connections to simplify setup and reflect the preferred method for connecting to Moku devices. This reduces onboarding time and aligns with network-based usage patterns. No major bugs fixed this month; focus remained on feature delivery and code quality. Overall impact: improved developer experience, faster integration into automation pipelines, and a cleaner, future-proof example set. Technologies/skills demonstrated: Python API updates, network connection handling, code refactoring, version-controlled changes (commit 0588bce8417a1a99a11b97e8f0173cabc7a19c4b).
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 (2025-01) — Summary of work in liquidinstruments/moku-examples. Key feature delivered: Migrated Python API examples from serial-number to IP-based connections to simplify setup and reflect the preferred method for connecting to Moku devices. This reduces onboarding time and aligns with network-based usage patterns. No major bugs fixed this month; focus remained on feature delivery and code quality. Overall impact: improved developer experience, faster integration into automation pipelines, and a cleaner, future-proof example set. Technologies/skills demonstrated: Python API updates, network connection handling, code refactoring, version-controlled changes (commit 0588bce8417a1a99a11b97e8f0173cabc7a19c4b).
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024 — Focused on delivering an end-to-end Neural Network instrument demonstration and improving docs in the moku-examples repo. Key outcomes include new Python and MATLAB NN example scripts showing ramp generation, NN processing, and oscilloscope visualization with complete instrument setup and data plotting; plus corrected README links and formatting to improve navigability and reduce user confusion. These changes accelerate onboarding, prototyping, and adoption of NN-based workflows.
3 Commits • 1 Features
Nov 1, 2024November 2024 — Focused on delivering an end-to-end Neural Network instrument demonstration and improving docs in the moku-examples repo. Key outcomes include new Python and MATLAB NN example scripts showing ramp generation, NN processing, and oscilloscope visualization with complete instrument setup and data plotting; plus corrected README links and formatting to improve navigability and reduce user confusion. These changes accelerate onboarding, prototyping, and adoption of NN-based workflows.
November 2024
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MATLABMarkdownMatlabPython
Technical Skills
API DevelopmentAPI IntegrationCode CleanupData VisualizationDocumentationEmbedded SystemsHDL DevelopmentInstrument ControlMATLAB ScriptingPython APISignal Processing
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