liquidinstruments/moku-examples
Nov 2024 – Mar 2026
6 Months active
Languages Used
MATLABMarkdownPythonMatlabVHDLVerilog
Technical Skills
API IntegrationData VisualizationDocumentationEmbedded SystemsInstrument ControlMATLAB Scripting
indira
PROFILE
Indira
Over six months, contributed to the liquidinstruments/moku-examples repository by developing and refining features that streamline instrument control, data acquisition, and signal processing workflows. Delivered end-to-end neural network instrument demonstrations and reusable full-rate wrapper templates, enabling faster onboarding and consistent development patterns. Expanded device support, including Moku:Delta and Gigabit Streamer, through updates to Python and MATLAB APIs, HDL code generation, and documentation. Improved reliability and usability by correcting control signal references, enhancing VHDL and Verilog modules, and clarifying data representations. Demonstrated strong technical writing and collaboration skills, with a focus on Python, MATLAB, and hardware description languages throughout the project.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
20Total
Bugs
3
Commits
20
Features
8
Lines of code
4,199,215
Activity Months6
Your Network
27 peopleSame Organization
@liquidinstruments.com12
Shared Repositories
15
Ben NizetteMember
Brian NeffMember
Dylan PerezMember
EC2 Default UserMember
EC2 Default UserMember
Heyang LongMember
jasonliquidinstMember
jobinMember
lauralbecerraMember
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03. Delivered reusable templates to accelerate full-rate feature development in the Moku Examples repo. Implemented Moku Full Rate Wrapper Templates, providing structured, ready-to-use examples for users to implement full-rate operations. No major bugs recorded in this period. This work improves onboarding, reduces integration time for customers, and promotes consistency across examples. Demonstrated strong collaboration through PR-based workflows aligned with APPS-720.
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03. Delivered reusable templates to accelerate full-rate feature development in the Moku Examples repo. Implemented Moku Full Rate Wrapper Templates, providing structured, ready-to-use examples for users to implement full-rate operations. No major bugs recorded in this period. This work improves onboarding, reduces integration time for customers, and promotes consistency across examples. Demonstrated strong collaboration through PR-based workflows aligned with APPS-720.
March 2026
February 2026
4 Commits • 2 Features
Feb 1, 2026February 2026 performance summary for liquidinstruments/moku-examples. Focused on reliability, usability, and language support enhancements across EventCounter and FRA components. Key outcomes include code-quality improvements, clearer data representations, and better developer/doc readership through targeted documentation updates.
February 2026
4 Commits • 2 Features
Feb 1, 2026February 2026 performance summary for liquidinstruments/moku-examples. Focused on reliability, usability, and language support enhancements across EventCounter and FRA components. Key outcomes include code-quality improvements, clearer data representations, and better developer/doc readership through targeted documentation updates.
December 2025
10 Commits • 2 Features
Dec 1, 2025December 2025 performance summary for liquidinstruments/moku-examples: Key feature deliveries around Gigabit Streamer support, foundational platform tooling, and a critical reliability fix. The work enables larger-scale streaming use-cases, expands device support (Go/Lab 3) via MCC refactor, and strengthens signal handling across modules through corrected control signal references, Verilog wrappers, and Time Frequency Analyzer enhancements. This delivers faster prototyping, broader hardware compatibility, and a more robust developer experience.
10 Commits • 2 Features
Dec 1, 2025December 2025 performance summary for liquidinstruments/moku-examples: Key feature deliveries around Gigabit Streamer support, foundational platform tooling, and a critical reliability fix. The work enables larger-scale streaming use-cases, expands device support (Go/Lab 3) via MCC refactor, and strengthens signal handling across modules through corrected control signal references, Verilog wrappers, and Time Frequency Analyzer enhancements. This delivers faster prototyping, broader hardware compatibility, and a more robust developer experience.
December 2025
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.
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.
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).
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
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.
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.
Activity
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Quality Metrics
Correctness96.0%
Maintainability95.0%
Architecture95.0%
Performance94.0%
AI Usage23.0%
Skills & Technologies
Programming Languages
MATLABMarkdownMatlabPythonVHDLVerilog
Technical Skills
API DevelopmentAPI IntegrationAPI developmentAPI integrationAPI usageCode CleanupData VisualizationData acquisitionDigital Signal ProcessingDocumentationEmbedded SystemsEmbedded systemsFPGA DevelopmentFPGA designFPGA development
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