liquidinstruments/moku-examples
Apr 2025 – Feb 2026
4 Months active
Languages Used
PythonJupyter NotebookVHDL
Technical Skills
Data AnalysisEmbedded SystemsPython ProgrammingSignal ProcessingFilter DesignFrequency Response Analysis
dylan-perez
PROFILE
Dylan-perez
Contributed a series of end-to-end measurement and signal processing features to the liquidinstruments/moku-examples repository, focusing on Python-based automation and hardware integration. Developed workflows for FIR filter center frequency tracking, multi-instrument FIR and frequency response analysis, and oscilloscope waveform averaging, each providing practical, user-facing examples for data acquisition, analysis, and visualization. Enhanced firmware-level timing control in the Boxcar module by expanding trigger delay parameters using VHDL, improving measurement flexibility for time-critical experiments. Leveraged Python, Jupyter Notebook, and hardware description languages to streamline prototyping, accelerate onboarding, and enable reproducible, real-time data processing for embedded systems and digital signal processing applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
4
Lines of code
1,247
Activity Months4
Your Network
27 peopleSame Organization
@liquidinstruments.com12
Shared Repositories
15
Ben NizetteMember
Brian NeffMember
EC2 Default UserMember
EC2 Default UserMember
Heyang LongMember
Indira Shinn ReesMember
jasonliquidinstMember
jobinMember
lauralbecerraMember
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for liquidinstruments/moku-examples focused on enhancing timing control in the Boxcar module. Implemented Timing Gate Width Expansion by increasing the trigger delay bit-width from 16 to 32 bits to allow wider gate separation and more precise timing control. Commit 39b1e7131451b4b7f2e5fd6da1fbf3fe7c8b0655 updated the boxcar for wider gate separation. No major bugs fixed this month. Business impact: improved measurement accuracy and flexibility for time-critical experiments, reducing workaround efforts and enabling broader usage scenarios. Skills demonstrated include firmware-level timing control, 32-bit parameter handling, and Git-based change management in a collaborative repo.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for liquidinstruments/moku-examples focused on enhancing timing control in the Boxcar module. Implemented Timing Gate Width Expansion by increasing the trigger delay bit-width from 16 to 32 bits to allow wider gate separation and more precise timing control. Commit 39b1e7131451b4b7f2e5fd6da1fbf3fe7c8b0655 updated the boxcar for wider gate separation. No major bugs fixed this month. Business impact: improved measurement accuracy and flexibility for time-critical experiments, reducing workaround efforts and enabling broader usage scenarios. Skills demonstrated include firmware-level timing control, 32-bit parameter handling, and Git-based change management in a collaborative repo.
February 2026
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered an end-to-end Oscilloscope Waveform Averaging Demo in liquidinstruments/moku-examples, showcasing an automated Python workflow for configuring the oscilloscope, acquiring data, and computing/displaying a rolling average in real-time. The feature provides a practical blueprint for users to implement signal averaging and enhances the Moku platform’s data-analysis capabilities.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered an end-to-end Oscilloscope Waveform Averaging Demo in liquidinstruments/moku-examples, showcasing an automated Python workflow for configuring the oscilloscope, acquiring data, and computing/displaying a rolling average in real-time. The feature provides a practical blueprint for users to implement signal averaging and enhances the Moku platform’s data-analysis capabilities.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for liquidinstruments/moku-examples: Delivered FIR Filter Box Instrument Usage and FRA Analysis Examples with multi-instrument support. Added end-to-end examples for configuring the FIR Filter Box instrument on Moku devices, including setup of filter shapes (lowpass, highpass, bandpass, bandstop) and configuring the Frequency Response Analyzer to analyze performance. Implemented a multi-instrument workflow to run FIR filtering and FRA concurrently, enabling streamlined experiments and performance evaluation. This work is anchored by the commit: 2ec2a3d086b18cd8dfd384e8507396bc396a616c.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for liquidinstruments/moku-examples: Delivered FIR Filter Box Instrument Usage and FRA Analysis Examples with multi-instrument support. Added end-to-end examples for configuring the FIR Filter Box instrument on Moku devices, including setup of filter shapes (lowpass, highpass, bandpass, bandstop) and configuring the Frequency Response Analyzer to analyze performance. Implemented a multi-instrument workflow to run FIR filtering and FRA concurrently, enabling streamlined experiments and performance evaluation. This work is anchored by the commit: 2ec2a3d086b18cd8dfd384e8507396bc396a616c.
July 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focused on delivering a Python-based DFRT (Dual Frequency Resonance Tracker) example for automating FIR center frequency tracking in the liquidinstruments/moku-examples repository. The work enabled end-to-end measurement workflows, including setup, data logging, plotting, and error calculation to validate DFRT accuracy. This contributes to faster prototyping, improved validation, and a reusable reference for customers and contributors.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focused on delivering a Python-based DFRT (Dual Frequency Resonance Tracker) example for automating FIR center frequency tracking in the liquidinstruments/moku-examples repository. The work enabled end-to-end measurement workflows, including setup, data logging, plotting, and error calculation to validate DFRT accuracy. This contributes to faster prototyping, improved validation, and a reusable reference for customers and contributors.
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonVHDL
Technical Skills
Data AcquisitionData AnalysisData VisualizationEmbedded SystemsFPGA designFilter DesignFrequency Response AnalysisPython APIPython API IntegrationPython ProgrammingSignal Processingdigital signal processinghardware description language
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