AllenNeuralDynamics/dynamic-foraging-task
Oct 2024 – Jan 2026
15 Months active
Languages Used
PythonUITOMLXMLC#bonsaiBonsaiPyQt5
Technical Skills
Backend DevelopmentBehavioral AnalysisBug FixingCode FormattingCode RefactoringData Analysis
Micah Woodard
PROFILE
Micah Woodard
Micah Woodard developed and maintained the AllenNeuralDynamics/dynamic-foraging-task repository, delivering robust experimental workflows for behavioral neuroscience research. Over 15 months, he engineered features for data acquisition, real-time analysis, and UI/UX improvements, integrating Python, Qt, and Bonsai scripting to support complex sensor and hardware interactions. His work included workflow automation, calibration systems, and data validation pipelines, ensuring reliable experiment execution and reproducible results. By refactoring code, enhancing logging, and stabilizing dependencies, Micah improved maintainability and deployment consistency. He addressed edge cases in data handling and configuration, demonstrating depth in backend development and a strong focus on experiment reliability and usability.
Overall Statistics
Feature vs Bugs
54%Features
Repository Contributions
414Total
Bugs
108
Commits
414
Features
126
Lines of code
2,021,738
Activity Months15
Your Network
24 people
Work History
January 2026
6 Commits • 1 Features
Jan 1, 2026January 2026: Delivered targeted enhancements to the dynamic-foraging-task visualization and completed configuration/build hygiene to reduce onboarding friction and future maintenance. Key UI improvements include rolling graph visualizers, capacity and range tuning (lickvis), and layout refinements, complemented by cleanup of obsolete config/files (removed sln/snL and updated paths). These changes improve interpretability of foraging data, shorten researcher iteration cycles, and simplify deployment.
6 Commits • 1 Features
Jan 1, 2026January 2026: Delivered targeted enhancements to the dynamic-foraging-task visualization and completed configuration/build hygiene to reduce onboarding friction and future maintenance. Key UI improvements include rolling graph visualizers, capacity and range tuning (lickvis), and layout refinements, complemented by cleanup of obsolete config/files (removed sln/snL and updated paths). These changes improve interpretability of foraging data, shorten researcher iteration cycles, and simplify deployment.
January 2026
December 2025
23 Commits • 7 Features
Dec 1, 2025December 2025 summary for AllenNeuralDynamics/dynamic-foraging-task: Key observability enhancements, configuration reliability improvements, and targeted UI refinements that collectively reduce debugging time, stabilize deployments, and improve data quality. Delivered robust logging, CSV/settings parsing improvements with defaults, YAML-based settings serialization, UI graph relocation, and critical reliability bug fixes.
December 2025
23 Commits • 7 Features
Dec 1, 2025December 2025 summary for AllenNeuralDynamics/dynamic-foraging-task: Key observability enhancements, configuration reliability improvements, and targeted UI refinements that collectively reduce debugging time, stabilize deployments, and improve data quality. Delivered robust logging, CSV/settings parsing improvements with defaults, YAML-based settings serialization, UI graph relocation, and critical reliability bug fixes.
November 2025
15 Commits • 5 Features
Nov 1, 2025November 2025: Delivered a cohesive set of features and reliability fixes for AllenNeuralDynamics/dynamic-foraging-task, prioritizing foraging workflow reliability, observability, UI consistency, and architectural simplification. Deliverables translate into faster task setup, improved data integrity, reduced maintenance overhead, and groundwork for Microsoft service integrations.
15 Commits • 5 Features
Nov 1, 2025November 2025: Delivered a cohesive set of features and reliability fixes for AllenNeuralDynamics/dynamic-foraging-task, prioritizing foraging workflow reliability, observability, UI consistency, and architectural simplification. Deliverables translate into faster task setup, improved data integrity, reduced maintenance overhead, and groundwork for Microsoft service integrations.
November 2025
October 2025
17 Commits • 3 Features
Oct 1, 2025October 2025 performance summary for AllenNeuralDynamics/dynamic-foraging-task. Delivered major feature integration for foraging analysis, data asset updates, and UI refinements, along with targeted bug fixes to improve reliability and maintainability of the foraging analytics pipeline. Re-enabled SLIMS integration in the GUI and hardened the workflow against non-numeric protocol values. Cleaned up data assets to reduce dependencies and optimize asset footprint. Overall, these efforts improved stability, reproducibility, and user experience for researchers and downstream systems.
October 2025
17 Commits • 3 Features
Oct 1, 2025October 2025 performance summary for AllenNeuralDynamics/dynamic-foraging-task. Delivered major feature integration for foraging analysis, data asset updates, and UI refinements, along with targeted bug fixes to improve reliability and maintainability of the foraging analytics pipeline. Re-enabled SLIMS integration in the GUI and hardened the workflow against non-numeric protocol values. Cleaned up data assets to reduce dependencies and optimize asset footprint. Overall, these efforts improved stability, reproducibility, and user experience for researchers and downstream systems.
September 2025
14 Commits • 8 Features
Sep 1, 2025September 2025: Focused on reliability, usability, and end-to-end workflow improvements in dynamic-foraging-task to accelerate experiments and improve data quality.
14 Commits • 8 Features
Sep 1, 2025September 2025: Focused on reliability, usability, and end-to-end workflow improvements in dynamic-foraging-task to accelerate experiments and improve data quality.
September 2025
July 2025
6 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for AllenNeuralDynamics/dynamic-foraging-task: Delivered improvements to the Foraging positioning workflow, stabilized dependencies for cross-environment reliability, and fixed Bonsai/GUI integration issues to ensure robust experimental setups. These changes enhance data accuracy, repeatability, and deployment consistency across labs.
July 2025
6 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for AllenNeuralDynamics/dynamic-foraging-task: Delivered improvements to the Foraging positioning workflow, stabilized dependencies for cross-environment reliability, and fixed Bonsai/GUI integration issues to ensure robust experimental setups. These changes enhance data accuracy, repeatability, and deployment consistency across labs.
June 2025
25 Commits • 6 Features
Jun 1, 2025June 2025 monthly summary for AllenNeuralDynamics/dynamic-foraging-task focused on UI stability, sensor reliability, and data integrity to improve reliability of experiments and accelerate iteration. Key features delivered: 1) Weight and Water widget base size configuration added; obsolete group box base size removed, enabling consistent rendering across layouts. 2) Widget sizing baseline corrected by applying base size instead of maximum size, fixing sizing behavior. 3) Enhanced sensor initialization with key presence checks using a subscribe pattern for robust startup. 4) Temperature and humidity rounding for display/processing and integration of behavior/environment timestamps for accurate time recording. 5) GUI startup improvements including stage initialization on open and workflow enhancements moving environment sensor earlier with added initialization delay and standardized environment keys. 6) Environment sensor workflow refinements and timestamp usage for reliable time-aligned data. 7) Additional GUI stability improvements including layout/dialog fixes to prevent re-adding layouts, trigger errors, and log-name inconsistencies.
25 Commits • 6 Features
Jun 1, 2025June 2025 monthly summary for AllenNeuralDynamics/dynamic-foraging-task focused on UI stability, sensor reliability, and data integrity to improve reliability of experiments and accelerate iteration. Key features delivered: 1) Weight and Water widget base size configuration added; obsolete group box base size removed, enabling consistent rendering across layouts. 2) Widget sizing baseline corrected by applying base size instead of maximum size, fixing sizing behavior. 3) Enhanced sensor initialization with key presence checks using a subscribe pattern for robust startup. 4) Temperature and humidity rounding for display/processing and integration of behavior/environment timestamps for accurate time recording. 5) GUI startup improvements including stage initialization on open and workflow enhancements moving environment sensor earlier with added initialization delay and standardized environment keys. 6) Environment sensor workflow refinements and timestamp usage for reliable time-aligned data. 7) Additional GUI stability improvements including layout/dialog fixes to prevent re-adding layouts, trigger errors, and log-name inconsistencies.
June 2025
May 2025
43 Commits • 8 Features
May 1, 2025May 2025 monthly summary focused on delivering data accessibility, observability, and UI/UX improvements, while strengthening the reliability of the sensor data pipeline and Bonsai integration. Work supported data-driven decision-making, experiment reproducibility, and a more scalable runtime environment across the AllenNeuralDynamics/dynamic-foraging-task repository.
May 2025
43 Commits • 8 Features
May 1, 2025May 2025 monthly summary focused on delivering data accessibility, observability, and UI/UX improvements, while strengthening the reliability of the sensor data pipeline and Bonsai integration. Work supported data-driven decision-making, experiment reproducibility, and a more scalable runtime environment across the AllenNeuralDynamics/dynamic-foraging-task repository.
April 2025
18 Commits • 2 Features
Apr 1, 2025April 2025 performance summary for AllenNeuralDynamics/dynamic-foraging-task focused on delivering reliable data collection pipelines, safer experiment gating, and UI robustness. Implemented end-to-end improvements to the Foraging GUI baseline and photometry timing, strengthened logging, and added input validation to reduce user-error.
18 Commits • 2 Features
Apr 1, 2025April 2025 performance summary for AllenNeuralDynamics/dynamic-foraging-task focused on delivering reliable data collection pipelines, safer experiment gating, and UI robustness. Implemented end-to-end improvements to the Foraging GUI baseline and photometry timing, strengthened logging, and added input validation to reduce user-error.
April 2025
March 2025
43 Commits • 15 Features
Mar 1, 2025March 2025 monthly summary for AllenNeuralDynamics/dynamic-foraging-task focused on delivering value-driven features, stabilizing core workflows, and hardening Bonsai integration. The work emphasizes reliability, maintainability, and performance improvements that enable rapid experimentation and robust audio-visual interactions.
March 2025
43 Commits • 15 Features
Mar 1, 2025March 2025 monthly summary for AllenNeuralDynamics/dynamic-foraging-task focused on delivering value-driven features, stabilizing core workflows, and hardening Bonsai integration. The work emphasizes reliability, maintainability, and performance improvements that enable rapid experimentation and robust audio-visual interactions.
February 2025
30 Commits • 11 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for AllenNeuralDynamics/dynamic-foraging-task. Focused on delivering robust features, stabilizing the codebase, and enhancing experiment reliability while improving developer and user experience.
30 Commits • 11 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for AllenNeuralDynamics/dynamic-foraging-task. Focused on delivering robust features, stabilizing the codebase, and enhancing experiment reliability while improving developer and user experience.
February 2025
January 2025
8 Commits • 1 Features
Jan 1, 2025January 2025 highlights for AllenNeuralDynamics/dynamic-foraging-task: delivered robustness and data-quality improvements across the NWB export and data processing pipeline. Expanded TransferToNWB test data coverage to validate a broader set of scenarios, improving reliability of data exports. Implemented guards to prevent errors when session metadata generation fails by checking session object presence before emitting sessionGenerated. Added protections for empty/missing stage_positions in lickspout data and refreshed test URLs to reflect new attachment locations, reducing data prep edge-case failures. Removed a redundant debugging print in TransferToNWB.bonsai_to_nwb to clean logs. Implemented bias tracking and concatenation fixes across Foraging and GeneratedTrials/NWB data to ensure proper trial-number association and correct use of numpy.concatenate. These changes reduce failure modes, improve data integrity for downstream analyses, and enhance maintainability of the data pipeline.
January 2025
8 Commits • 1 Features
Jan 1, 2025January 2025 highlights for AllenNeuralDynamics/dynamic-foraging-task: delivered robustness and data-quality improvements across the NWB export and data processing pipeline. Expanded TransferToNWB test data coverage to validate a broader set of scenarios, improving reliability of data exports. Implemented guards to prevent errors when session metadata generation fails by checking session object presence before emitting sessionGenerated. Added protections for empty/missing stage_positions in lickspout data and refreshed test URLs to reflect new attachment locations, reducing data prep edge-case failures. Removed a redundant debugging print in TransferToNWB.bonsai_to_nwb to clean logs. Implemented bias tracking and concatenation fixes across Foraging and GeneratedTrials/NWB data to ensure proper trial-number association and correct use of numpy.concatenate. These changes reduce failure modes, improve data integrity for downstream analyses, and enhance maintainability of the data pipeline.
December 2024
47 Commits • 15 Features
Dec 1, 2024December 2024 (AllenNeuralDynamics/dynamic-foraging-task) delivered a focused set of reliability, data-processing, and UI/UX improvements that enable faster experimentation and more trustworthy results. Key features were implemented to streamline workflows and improve data integrity, while a robust set of bug fixes reduced runtime errors and drift across sessions. Key features delivered: - Min time support and ephys reorganization to optimize data processing for time-constrained events. - Scheduling-based current stage check to continuously verify the active stage and reduce state drift. - Populate stage list from aind_auto_train to simplify training workflow setup. - Ephys GUI formatting improvements and UI layout enhancements for clearer visualization and easier interaction. - Add operator and enhanced logging for diagnostics; improved import management and numeric type consistency across the codebase; UI enhancements such as experimenter name in load/verify boxes. Major bugs fixed: - Key error in loading stage positions fixed; restored missing imports to prevent import-time failures. - Guard against arrays of unexpected size to prevent indexing errors; NaN handling improvements in calculations and scheduling logic. - Reverted to autotrain/dialog flow where appropriate and removed stray debug prints; additional miscellaneous bug fixes across batch. Overall impact and accomplishments: - Increased reliability and reproducibility of experiments through robust data loading, improved scheduling reliability, and clearer observability. UI and workflow enhancements reduce operational friction, enabling faster iteration with fewer regressions. These changes collectively raise the product quality and science enablement for the team. Technologies/skills demonstrated: - Python data handling and data structure reorganization; defensive programming (size checks, NaN handling); type normalization (numpy.float64 to Python float). - Scheduling patterns and automation to ensure correct stage progression. - UI/UX design improvements and logging for diagnostics. - Import management and code hygiene to reduce runtime/import failures.
47 Commits • 15 Features
Dec 1, 2024December 2024 (AllenNeuralDynamics/dynamic-foraging-task) delivered a focused set of reliability, data-processing, and UI/UX improvements that enable faster experimentation and more trustworthy results. Key features were implemented to streamline workflows and improve data integrity, while a robust set of bug fixes reduced runtime errors and drift across sessions. Key features delivered: - Min time support and ephys reorganization to optimize data processing for time-constrained events. - Scheduling-based current stage check to continuously verify the active stage and reduce state drift. - Populate stage list from aind_auto_train to simplify training workflow setup. - Ephys GUI formatting improvements and UI layout enhancements for clearer visualization and easier interaction. - Add operator and enhanced logging for diagnostics; improved import management and numeric type consistency across the codebase; UI enhancements such as experimenter name in load/verify boxes. Major bugs fixed: - Key error in loading stage positions fixed; restored missing imports to prevent import-time failures. - Guard against arrays of unexpected size to prevent indexing errors; NaN handling improvements in calculations and scheduling logic. - Reverted to autotrain/dialog flow where appropriate and removed stray debug prints; additional miscellaneous bug fixes across batch. Overall impact and accomplishments: - Increased reliability and reproducibility of experiments through robust data loading, improved scheduling reliability, and clearer observability. UI and workflow enhancements reduce operational friction, enabling faster iteration with fewer regressions. These changes collectively raise the product quality and science enablement for the team. Technologies/skills demonstrated: - Python data handling and data structure reorganization; defensive programming (size checks, NaN handling); type normalization (numpy.float64 to Python float). - Scheduling patterns and automation to ensure correct stage progression. - UI/UX design improvements and logging for diagnostics. - Import management and code hygiene to reduce runtime/import failures.
December 2024
November 2024
95 Commits • 37 Features
Nov 1, 2024November 2024 performance summary for AllenNeuralDynamics/dynamic-foraging-task: stabilized and enhanced core UI workflows, strengthened observability, and advanced model/widget integration with parameter tuning. Key outcomes include Stage UI restoration and integration with last-position handling, warning widget and logging enhancements for better observability, and a model-driven workflow with widget-driven updates and saving. Kernel size updated to 10 with related parameter tuning, and targeted UI improvements (date display) to aid result traceability. These efforts collectively improved reliability, user experience, and data quality while enabling faster diagnosis and iteration across the pipeline.
November 2024
95 Commits • 37 Features
Nov 1, 2024November 2024 performance summary for AllenNeuralDynamics/dynamic-foraging-task: stabilized and enhanced core UI workflows, strengthened observability, and advanced model/widget integration with parameter tuning. Key outcomes include Stage UI restoration and integration with last-position handling, warning widget and logging enhancements for better observability, and a model-driven workflow with widget-driven updates and saving. Kernel size updated to 10 with related parameter tuning, and targeted UI improvements (date display) to aid result traceability. These efforts collectively improved reliability, user experience, and data quality while enabling faster diagnosis and iteration across the pipeline.
October 2024
24 Commits • 6 Features
Oct 1, 2024Monthly work summary for 2024-10 focused on delivering essential features for the AllenNeuralDynamics dynamic-foraging-task, hardening robustness, and enabling smoother experimentation and data quality. Key work includes timing/control enhancements, data-quality alerts, UI integration, and session-driven data artifacts, complemented by code cleanup and testing scaffolding. These efforts improved experiment reliability, data integrity, and maintainability while enabling faster iteration and clearer telemetry for decisions.
24 Commits • 6 Features
Oct 1, 2024Monthly work summary for 2024-10 focused on delivering essential features for the AllenNeuralDynamics dynamic-foraging-task, hardening robustness, and enabling smoother experimentation and data quality. Key work includes timing/control enhancements, data-quality alerts, UI integration, and session-driven data artifacts, complemented by code cleanup and testing scaffolding. These efforts improved experiment reliability, data integrity, and maintainability while enabling faster iteration and clearer telemetry for decisions.
October 2024
Activity
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Quality Metrics
Correctness84.4%
Maintainability86.0%
Architecture79.4%
Performance80.4%
AI Usage20.8%
Skills & Technologies
Programming Languages
BonsaiC#JSONPyQt5PythonTOMLUIVisual Studio Solution FileXMLbonsai
Technical Skills
Algorithm DevelopmentArray OperationsAudio ProgrammingBack-end DevelopmentBackend DevelopmentBehavioral AnalysisBehavioral ExperimentationBehavioral NeuroscienceBonsaiBonsai FrameworkBonsai ScriptingBonsai WorkflowBonsai Workflow DevelopmentBonsai WorkflowsBonsai scripting
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