aolabNeuro/analyze
Nov 2024 – Feb 2026
8 Months active
Languages Used
NumpyPythonJupyter NotebookRSTMATLABXMLMarkdownNumPy
Technical Skills
Backend DevelopmentBug FixingData LoadingData PreprocessingData ProcessingData Synchronization
TOMOHIRO OUCHI
PROFILE
Tomohiro Ouchi
Over eight months, Touchi developed and maintained core data analysis and preprocessing pipelines for the aolabNeuro/analyze repository, focusing on neuroscience and behavioral datasets. He implemented scalable APIs for time-series synchronization, multi-drive data loading, and memory-efficient batch processing, using Python and NumPy to handle large neural datasets. Touchi enhanced data reliability by introducing robust error handling, metadata-driven preprocessing, and comprehensive unit testing. He also expanded analytical capabilities with cross-validation frameworks and sliding window statistics for reaction time analysis. His work emphasized maintainable code through refactoring, documentation, and test coverage, resulting in reproducible, high-performance workflows for complex neuroinformatics research.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
65Total
Bugs
2
Commits
65
Features
17
Lines of code
35,432
Activity Months8
Your Network
125 peopleSame Organization
@uw.edu116
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for aolabNeuro/analyze. Focused on delivering enhanced data analysis capability by introducing a Sliding Window Statistics function to study the relationship between reaction times (RTs) and delay periods. Implemented in the aolabNeuro/analyze repository with commit d0aa37fe15fd79a98b89937b9fc67f9f8f4d3973 (message: 'Code to see a relationship of delay and RTs'). This feature enables more robust interpretation of RT dynamics, supporting improved experimental design and data-driven decisions.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for aolabNeuro/analyze. Focused on delivering enhanced data analysis capability by introducing a Sliding Window Statistics function to study the relationship between reaction times (RTs) and delay periods. Implemented in the aolabNeuro/analyze repository with commit d0aa37fe15fd79a98b89937b9fc67f9f8f4d3973 (message: 'Code to see a relationship of delay and RTs'). This feature enables more robust interpretation of RT dynamics, supporting improved experimental design and data-driven decisions.
February 2026
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for aolabNeuro/analyze focusing on delivering enhanced movement onset analytics and configurable data outputs. Key feature delivered: Movement Onset Detection Enhancements, including refining the filtering process, adding parameters for initial position and filter settings, and introducing optional speed calculation with the ability to return speed data alongside movement onset. This work lays groundwork for richer downstream analytics and potential real-time processing. Commits reflect focused code changes and documentation: f2eebedea085606e1f7c40864b5f67f8a0bb7e64 (Modified movement onset function) and 3856518193069de51580fa91ac3f70d42753b15d (marios comment).
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for aolabNeuro/analyze focusing on delivering enhanced movement onset analytics and configurable data outputs. Key feature delivered: Movement Onset Detection Enhancements, including refining the filtering process, adding parameters for initial position and filter settings, and introducing optional speed calculation with the ability to return speed data alongside movement onset. This work lays groundwork for richer downstream analytics and potential real-time processing. Commits reflect focused code changes and documentation: f2eebedea085606e1f7c40864b5f67f8a0bb7e64 (Modified movement onset function) and 3856518193069de51580fa91ac3f70d42753b15d (marios comment).
October 2025
2 Commits
Oct 1, 2025Month: 2025-10 | Repository: aolabNeuro/analyze | Focus: bug fixes improving data integrity and metadata accessibility; no new features delivered this period. Primary value came from correcting critical issues and stabilizing the data pipeline for downstream analyses.
2 Commits
Oct 1, 2025Month: 2025-10 | Repository: aolabNeuro/analyze | Focus: bug fixes improving data integrity and metadata accessibility; no new features delivered this period. Primary value came from correcting critical issues and stabilizing the data pipeline for downstream analyses.
October 2025
August 2025
7 Commits • 3 Features
Aug 1, 2025August 2025 highlights for the aolabNeuro/analyze repository: delivered data processing and API enhancements that improve reliability, performance, and clarity for researchers and engineers. Kilosort data parsing now supports kilosort4 and kilosort2.5, with version-specific data loading and organized storage that reduces downstream errors. The LDA cross-validation wrapper was renamed and refactored to xval_lda_subsample_wrapper, clarifying parameters and improving multi-dimensional data handling and return values. Documentation and learning resources were expanded with an ECoG notebook and improved docstrings and doc structure. These changes reduce maintenance burden, accelerate analysis workflows, and improve onboarding for new users across neurodata analysis tasks.
August 2025
7 Commits • 3 Features
Aug 1, 2025August 2025 highlights for the aolabNeuro/analyze repository: delivered data processing and API enhancements that improve reliability, performance, and clarity for researchers and engineers. Kilosort data parsing now supports kilosort4 and kilosort2.5, with version-specific data loading and organized storage that reduces downstream errors. The LDA cross-validation wrapper was renamed and refactored to xval_lda_subsample_wrapper, clarifying parameters and improving multi-dimensional data handling and return values. Documentation and learning resources were expanded with an ECoG notebook and improved docstrings and doc structure. These changes reduce maintenance burden, accelerate analysis workflows, and improve onboarding for new users across neurodata analysis tasks.
July 2025
20 Commits • 3 Features
Jul 1, 20252025-07 monthly summary focusing on key accomplishments in the aolabNeuro/analyze repository. Delivered scalable, cross-drive LFP data access, advanced preprocessing with metadata-driven rules, and a cross-validation framework for LDA, complemented by strengthened test coverage and data structure alignment. Business value includes improved data reliability, faster onboarding for new experiments, and more robust model evaluation across drives and metadata schemas.
20 Commits • 3 Features
Jul 1, 20252025-07 monthly summary focusing on key accomplishments in the aolabNeuro/analyze repository. Delivered scalable, cross-drive LFP data access, advanced preprocessing with metadata-driven rules, and a cross-validation framework for LDA, complemented by strengthened test coverage and data structure alignment. Business value includes improved data reliability, faster onboarding for new experiments, and more robust model evaluation across drives and metadata schemas.
July 2025
June 2025
13 Commits • 2 Features
Jun 1, 2025June 2025 performance: In aolabNeuro/analyze, delivered foundational Neuropixel data processing enhancements and improved data organization. Implemented Neuropixel Data Preprocessing Pipeline with spike (proc_neuropixel_spikes) and time-series (proc_neuropixel_ts) processing, including modular LFP/AP data handling, expanded test coverage, docstring improvements, and test cleanup to manage large preprocessed files. Consolidated preprocessing tests and refactored code for readability and maintainability. Extended Neuropixel data concatenation and organization by renaming and expanding functions (concat_neuropixel_within_day -> concat_neuropixel; concat_neuropixel -> concat_neuropixels with explicit concat_number), saving concatenated outputs to port folders within the kilosort preproc directory, and updating tests accordingly. These changes collectively improve data processing speed, reproducibility, and maintainability while reducing regression risk across neuropixel workflows.
June 2025
13 Commits • 2 Features
Jun 1, 2025June 2025 performance: In aolabNeuro/analyze, delivered foundational Neuropixel data processing enhancements and improved data organization. Implemented Neuropixel Data Preprocessing Pipeline with spike (proc_neuropixel_spikes) and time-series (proc_neuropixel_ts) processing, including modular LFP/AP data handling, expanded test coverage, docstring improvements, and test cleanup to manage large preprocessed files. Consolidated preprocessing tests and refactored code for readability and maintainability. Extended Neuropixel data concatenation and organization by renaming and expanding functions (concat_neuropixel_within_day -> concat_neuropixel; concat_neuropixel -> concat_neuropixels with explicit concat_number), saving concatenated outputs to port folders within the kilosort preproc directory, and updating tests accordingly. These changes collectively improve data processing speed, reproducibility, and maintainability while reducing regression risk across neuropixel workflows.
December 2024
8 Commits • 3 Features
Dec 1, 2024December 2024 performance summary for aolabNeuro/analyze: delivered core data exploration and processing enhancements, expanded data loading capabilities for multi-drive recordings, and introduced an instructional notebook for movement onset analysis. The work strengthens data discoverability, reliability, and reproducibility, enabling faster insight generation for behavioral datasets.
8 Commits • 3 Features
Dec 1, 2024December 2024 performance summary for aolabNeuro/analyze: delivered core data exploration and processing enhancements, expanded data loading capabilities for multi-drive recordings, and introduced an instructional notebook for movement onset analysis. The work strengthens data discoverability, reliability, and reproducibility, enabling faster insight generation for behavioral datasets.
December 2024
November 2024
12 Commits • 4 Features
Nov 1, 2024November 2024: Delivered a set of features to improve data processing reliability and scalability in aolabNeuro/analyze. Key outcomes include robust time-series synchronization API, memory-efficient batched operations for large neural datasets, batch destriping of LFP data, and multi-drive data loading support. Implemented API refinements, added tests, and enhanced data pipeline resilience with disk-persisted results to support large-scale experiments. Business impact includes faster, more reliable preprocessing and enabling analysis of multi-drive, long-recordings with reduced memory footprint.
November 2024
12 Commits • 4 Features
Nov 1, 2024November 2024: Delivered a set of features to improve data processing reliability and scalability in aolabNeuro/analyze. Key outcomes include robust time-series synchronization API, memory-efficient batched operations for large neural datasets, batch destriping of LFP data, and multi-drive data loading support. Implemented API refinements, added tests, and enhanced data pipeline resilience with disk-persisted results to support large-scale experiments. Business impact includes faster, more reliable preprocessing and enabling analysis of multi-drive, long-recordings with reduced memory footprint.
Activity
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Quality Metrics
Correctness86.4%
Maintainability85.8%
Architecture80.8%
Performance77.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMATLABMarkdownNumPyNumpyPythonRSTXML
Technical Skills
API DesignBackend DevelopmentBehavioral AnalysisBug FixingCode RefactoringConditional LogicCross-validationData AnalysisData HandlingData LoadingData PreprocessingData ProcessingData SynchronizationData ValidationData Visualization
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