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Initial commit: GLM project for linking click data to decision variables
🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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‎.github/workflows/tests.yml‎

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name: Tests
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on:
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push:
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branches: [ main, master ]
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pull_request:
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branches: [ main, master ]
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [3.8, 3.9, '3.10']
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install uv
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uses: astral-sh/setup-uv@v4
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with:
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version: "latest"
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- name: Install dependencies
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run: uv sync
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- name: Run tests
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run: uv run pytest tests/ --cov=src --cov-report=xml
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- name: Upload coverage to Codecov
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uses: codecov/codecov-action@v3
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with:
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file: ./coverage.xml
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flags: unittests

‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook checkpoints only (not the notebooks themselves)
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# Data files
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data/raw/
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*.mat
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*.h5
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*.hdf5
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*.pkl
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*.pickle
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# Results and outputs
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results/figures/*.png
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results/figures/*.jpg
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results/figures/*.pdf
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results/models/*.pkl
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results/models/*.joblib
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results/reports/*.html
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# IDE files
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# macOS
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.DS_Store
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# Temporary files
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*.tmp
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*.temp
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# Large model files
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*.model
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*.weights

‎CLAUDE.md‎

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# ClickDV Project Context
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## Project Overview
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GLM project linking Poisson click input data from rats to decision variable outputs. This is a computational neuroscience project for the Brody-Daw lab rotation.
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## Research Goals
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- Extract decision variables from spike data (starting with session A324)
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- Access and analyze click times from behavioral data
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- Develop GLM to link click inputs to decision variables DV(t)
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- Investigate how GLM weights differ between sessions
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- Explore direct link between click inputs and decision making
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## Global Claude Preferences
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### Communication Style
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- Keep responses concise and technical
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- No unnecessary praise - I want thought from a good assistant, not a sycophant
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- Give explanations of technical concepts
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- Use bullet points for complex information
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- Avoid unnecessary preamble or postamble
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### Default Behaviors
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- Always use TodoWrite for multi-step tasks
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- For anything beyond a very simple task, always make a plan before coding
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- Proactively search and understand codebases before making changes
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- Follow existing code conventions and patterns
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- Run lint/typecheck commands after code changes
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- Always commit changes after major modifications with clear, descriptive commit messages
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### Technical Preferences
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- Prefer performance-optimized implementations
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- Use type hints in Python code
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- Include proper error handling
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- Follow academic/research coding standards
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### Project Context
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- Focus on computational neuroscience and GLM modeling
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- Prioritize accuracy and reproducibility
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- Use proper statistical analysis methods
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- Generate publication-quality outputs
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### Programming Education Goals
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- Introduce advanced statistical modeling concepts
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- Emphasize clean, scalable, and extensible code architecture
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- Explain GLM theory and implementation
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- Connect theory to real-world neuroscience applications
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- Build on existing computational background
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- Focus on scientific computing best practices
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### Tool Usage
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- Use parallel tool calls for efficiency
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- Prefer existing file editing over creating new files
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- Use comprehensive search before implementation
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- Maintain organized project structure
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## Important Instructions
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- Do what has been asked; nothing more, nothing less
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- NEVER create files unless absolutely necessary for achieving the goal
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- ALWAYS prefer editing an existing file to creating a new one
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- NEVER proactively create documentation files unless explicitly requested

‎README.md‎

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# ClickDV
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GLM project linking Poisson click input data from rats to decision variable outputs.
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## Project Overview
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This project develops a Generalized Linear Model (GLM) to link click input data to decision variables in rat behavioral experiments. The goal is to understand how sensory inputs (clicks) relate to decision-making processes through neural activity patterns.
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## Getting Started
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### Installation
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This project uses [uv](https://docs.astral.sh/uv/) for fast, modern Python package management.
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```bash
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# Install uv (if not already installed)
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curl -LsSf https://astral.sh/uv/install.sh | sh
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# Clone the repository
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git clone <repository-url>
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cd ClickDV
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# Install dependencies and create virtual environment
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uv sync
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# Activate the environment (optional - uv run handles this automatically)
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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```
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**Why uv?**
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- 10-100x faster than pip
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- Built-in virtual environment management
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- Reproducible builds with `uv.lock`
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- Modern Python packaging standards
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### Project Structure
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```
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ClickDV/
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├── data/ # Data storage
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│ ├── raw/ # Original data files
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│ └── processed/ # Processed data
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├── src/ # Source code
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│ ├── preprocessing/ # Data extraction and alignment
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│ ├── models/ # GLM implementations
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│ ├── analysis/ # Analysis and comparison tools
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│ └── utils/ # Utility functions
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├── notebooks/ # Jupyter notebooks for exploration
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├── tests/ # Test suite
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└── results/ # Output figures and reports
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```
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## Research Goals
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1. Extract decision variables from spike data (starting with session A324)
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2. Access and analyze click times from behavioral data
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3. Develop GLM to link click inputs DV(t)
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4. Investigate how GLM weights differ between sessions
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5. Explore direct link between click inputs and decision making
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## Usage
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Start with the notebooks in the `notebooks/` directory for data exploration and model development.
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## Development
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The package uses modern Python packaging with `pyproject.toml` and uv for dependency management. Development dependencies are installed automatically.
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Run commands in the virtual environment:
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```bash
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# Run tests
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uv run pytest tests/
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# Run tests with coverage
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uv run pytest tests/ --cov=src --cov-report=html
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# Run Python scripts
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uv run python src/main.py
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# Start Jupyter notebook
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uv run jupyter notebook
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```
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Update dependencies:
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```bash
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# Add new dependency
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uv add numpy
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# Add development dependency
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uv add --dev pytest
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# Update all dependencies
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uv sync
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```

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