[ { "path": ".claude/commands/coverage.md", "content": "Run tests with coverage.\n\nCommand to run:\n```\nuv run pytest -q --maxfail=1 --cov --cov-branch && rm -f .coverage.* && uv run coverage annotate\n```\n\nThe result will be the terminal output and the line-by-line coverage will be in files sitting next to each module with the file naming `{module_name.py},cover`. \n\nIf you are working on a PR, figure out which files were changed and look for coverage specifically in those. If you don't know which files to look for coverage in, use this:\n\n```\ngit diff --name-only $(git merge-base origin/develop HEAD) | jq -R . | jq -s .\n```" }, { "path": ".claude/commands/lint.md", "content": "Run linting with `ruff`.\n\nCommand:\n\n```\nuv run ruff format sleap tests && uv run ruff check --fix sleap tests\n```\n\nThen manually fix any remaining errors which cannot be automatically fixed by ruff." }, { "path": ".claude/commands/pr-description.md", "content": "Update PR description.\n\nUse the `gh` CLI to fetch the current PR description, then update it with a comprehensive description of the changes made in this PR.\n\nIf there is an associated issue (linked in the PR metadata or mentioned in the PR description), then use the `gh` CLI to fetch that too to contextualize the work done in the PR.\n\nInclude a summary, example usage (for enhancements), API changes, and other notes for future consideration (including reasoning behind design decisions)." }, { "path": ".claude/skills/investigation/SKILL.md", "content": "---\r\nname: investigation\r\ndescription: >\r\n Scaffolds a structured investigation in scratch/ for empirical research and documentation.\r\n Use when the user says \"start an investigation\" or wants to: trace code paths or data flow\r\n (\"trace from X to Y\", \"what touches X\", \"follow the wiring\"), document system architecture\r\n comprehensively (\"document how the system works\", \"archeology\"), investigate bugs\r\n (\"figure out why X happens\"), explore technical feasibility (\"can we do X?\"), or explore\r\n design options (\"explore the API\", \"gather context\", \"design alternatives\").\r\n Creates dated folder with README. NOT for simple code questions or single-file searches.\r\n---\r\n\r\n# Set up an investigation\r\n\r\n## Instructions\r\n\r\n1. Create a folder in `{REPO_ROOT}/scratch/` with the format `{YYYY-MM-DD}-{descriptive-name}`:\r\n ```bash\r\n mkdir scratch/$(uv run python -c \"import datetime; print(datetime.date.today().isoformat())\")-{descriptive-name}\r\n ```\r\n2. Create a `README.md` in this folder with: task description, background context, task checklist. Update with findings as you progress.\r\n3. Create scripts and data files as needed for empirical work.\r\n4. For complex investigations, split into sub-documents as patterns emerge.\r\n\r\n## Investigation Patterns\r\n\r\nThese are common patterns, not rigid categories. Most investigations blend multiple patterns.\r\n\r\n**Tracing** - \"trace from X to Y\", \"what touches X\", \"follow the wiring\"\r\n- Follow call stack or data flow from a focal component to its connections\r\n- Can trace forward (X ā†’ where does it go?) or backward (what leads to X?)\r\n- Useful for: assessing impact of changes, understanding coupling\r\n\r\n**System Architecture Archeology** - \"document how the system works\", \"archeology\"\r\n- Comprehensive documentation of an entire system or flow for reusable reference\r\n- Start from entry points, trace through all layers, document relationships exhaustively\r\n- For complex systems, consider numbered sub-documents (01-cli.md, 02-data.md, etc.)\r\n\r\n**Bug Investigation** - \"figure out why X happens\", \"this is broken\"\r\n- Reproduce ā†’ trace root cause ā†’ propose fix\r\n- For cross-repo bugs, consider per-repo task breakdowns\r\n\r\n**Technical Exploration** - \"can we do X?\", \"is this possible?\", \"figure out how to\"\r\n- Feasibility testing with proof-of-concept scripts\r\n- Document what works AND what doesn't\r\n\r\n**Design Research** - \"explore the API\", \"gather context\", \"design alternatives\"\r\n- Understand systems and constraints before building\r\n- Compare alternatives, document trade-offs\r\n- Include visual artifacts (mockups, screenshots) when relevant\r\n- For iterative decisions, use numbered \"Design Questions\" (DQ1, DQ2...) to structure review\r\n\r\n## Best Practices\r\n\r\n- Use `uv` with inline dependencies for standalone scripts; for scripts importing local project code, use `python` directly (or `uv run python` if env not activated)\r\n- Use subagents for parallel exploration to save context\r\n- Write small scripts to explore APIs interactively\r\n- Generate figures/diagrams and reference inline in markdown\r\n- For web servers: `npx serve -p 8080 --cors --no-clipboard &`\r\n- For screenshots: use Playwright MCP for web, Qt's grab() for GUI\r\n- For external package API review: clone to `scratch/repos/` for direct source access\r\n\r\n## Important: Scratch is Gitignored\r\n\r\nThe `scratch/` directory is in `.gitignore` and will NOT be committed.\r\n\r\n- NEVER delete anything from scratch - it doesn't need cleanup\r\n- When distilling findings into PRs, include all relevant info inline\r\n- Copy key findings, code, and data directly into PR descriptions\r\n- PRs must be self-contained; don't reference scratch files\r\n" }, { "path": ".claude/skills/pr/skill.md", "content": "---\nname: pr\ndescription: >\n Create a well-structured GitHub PR with proper branching, testing, formatting, and documentation.\n Use when the user says \"create a PR\", \"make a PR\", \"open a pull request\", or wants to submit\n changes for review. Handles the full workflow: branch creation, implementation, testing,\n formatting, committing, and PR creation with comprehensive descriptions.\n---\n\n# Create a GitHub Pull Request\n\n## Overview\n\nThis skill guides the complete PR workflow from branch creation to PR submission. Follow all steps\nin order to ensure high-quality, well-documented contributions.\n\n## Step 1: Branch Setup\n\n### Pull latest develop\n```bash\ngit checkout develop\ngit pull origin develop\n```\n\n### Create feature branch\nUse a descriptive branch name following the pattern: `{type}/{description}`\n\nTypes:\n- `feature/` - New functionality\n- `fix/` - Bug fixes\n- `refactor/` - Code restructuring\n- `docs/` - Documentation updates\n- `test/` - Test additions/improvements\n\n```bash\ngit checkout -b feature/descriptive-name\n```\n\n## Step 2: Understand the Problem\n\nBefore coding, clearly identify:\n1. **Core problem**: What issue are we solving?\n2. **Scope**: What files/modules will be affected?\n3. **Approach**: What's the implementation strategy?\n4. **Edge cases**: What scenarios need special handling?\n\nIf there's an associated GitHub issue, fetch it for context:\n```bash\ngh issue view \n```\n\n## Step 3: Implement Changes\n\n- Make focused, incremental changes\n- Follow existing code patterns and style\n- Add docstrings and comments for complex logic\n- Consider backwards compatibility\n\n## Step 4: Write Tests\n\n### Location\nTests go in the `tests/` directory, mirroring the source structure.\n\n### Requirements\n- Cover all new functionality\n- Test edge cases and error conditions\n- Test both success and failure paths\n- Aim for high coverage of changed code\n\n### Test file naming\n- `test_{module_name}.py` for module tests\n- Place in corresponding `tests/` subdirectory\n\n## Step 5: Format Code\n\nRun formatting and linting:\n```bash\n# Format code\nuv run ruff format sleap tests\n\n# Fix auto-fixable lint issues\nuv run ruff check --fix sleap tests\n```\n\nThen manually fix any remaining errors which cannot be automatically fixed by ruff.\n\n## Step 6: Run Tests with Coverage\n\nRun the full test suite with coverage:\n```bash\nuv run pytest -q --maxfail=1 --cov --cov-branch && rm -f .coverage.* && uv run coverage annotate\n```\n\n### Check coverage for changed files\nThe coverage annotate command creates `{module_name.py},cover` files next to each module.\n\nTo find which files changed:\n```bash\ngit diff --name-only $(git merge-base origin/develop HEAD)\n```\n\nReview the `,cover` files for your changed modules to ensure adequate coverage.\n\n### Coverage markers in annotated files\n- **>** means line was executed\n- **!** means line was NOT executed (needs test coverage)\n- **-** means line is not executable (comments, blank lines)\n\n## Step 7: Commit Changes\n\n### Commit structure\nMake well-structured, atomic commits:\n- Each commit should be a logical unit of work\n- Write clear, descriptive commit messages\n- Use conventional commit format when appropriate\n\n### Commit message format\n```\n: \n\n\n\nCo-Authored-By: Claude Opus 4.5 \n```\n\nTypes: `feat`, `fix`, `refactor`, `test`, `docs`, `chore`\n\n## Step 8: Push to GitHub\n\n```bash\ngit push -u origin \n```\n\n## Step 9: Create Pull Request\n\n### Create the PR\n```bash\ngh pr create --base develop --title \"\" --body \"$(cat <<'EOF'\n## Summary\n<1-3 bullet points describing the changes>\n\n## Changes Made\n- \n\n## Example Usage\n```python\n# For enhancements, show how to use the new functionality\n```\n\n## API Changes\n- \n\n## Testing\n- \n- \n\n## Design Decisions\n- \n- \n\n## Future Considerations\n- \n- \n\n## Related Issues\nCloses # (if applicable)\n\n---\nšŸ¤– Generated with [Claude Code](https://claude.com/claude-code)\nEOF\n)\"\n```\n\n### If updating an existing PR\n\nFetch current PR description:\n```bash\ngh pr view --json body -q '.body'\n```\n\nUpdate PR description:\n```bash\ngh pr edit --body \"\"\n```\n\n### Fetch associated issue for context\nIf an issue is linked:\n```bash\ngh issue view \n```\n\nUse issue context to ensure PR description addresses all requirements.\n\n## PR Description Checklist\n\n- [ ] Summary clearly explains the \"what\" and \"why\"\n- [ ] All significant changes are documented\n- [ ] Example usage provided for new features\n- [ ] API changes explicitly listed\n- [ ] Breaking changes highlighted\n- [ ] Test coverage described\n- [ ] Design decisions explained with reasoning\n- [ ] Related issues linked\n\n## Docs Preview\n\nPRs that modify `docs/`, `sleap/`, or `mkdocs.yml` will automatically get a docs preview deployed at:\n```\nhttps://docs.sleap.ai/pr//\n```\n\nA comment will be posted on the PR with the preview link.\n\n## Quick Reference Commands\n\n```bash\n# Branch setup\ngit checkout develop && git pull origin develop\ngit checkout -b feature/my-feature\n\n# Format and lint\nuv run ruff format sleap tests\nuv run ruff check --fix sleap tests\n\n# Test with coverage\nuv run pytest -q --maxfail=1 --cov --cov-branch && rm -f .coverage.* && uv run coverage annotate\n\n# Find changed files\ngit diff --name-only $(git merge-base origin/develop HEAD)\n\n# Commit\ngit add \ngit commit -m \"feat: description\"\n\n# Push and create PR\ngit push -u origin \ngh pr create --base develop --title \"Title\" --body \"Description\"\n\n# View/edit existing PR\ngh pr view \ngh pr edit --body \"New description\"\n\n# View linked issue\ngh issue view \n```\n\n## CI Checks\n\nPRs trigger the following CI checks:\n- **Lint**: `uv run ruff check sleap tests` and `uv run ruff format --check sleap tests`\n- **Tests**: `uv run pytest --cov=sleap` on Ubuntu, Windows, and macOS\n- **Docs Preview**: Auto-deployed for changes to `docs/`, `sleap/`, or `mkdocs.yml`\n\nEnsure all checks pass before requesting review.\n" }, { "path": ".claude/skills/qt-testing/SKILL.md", "content": "---\r\nname: qt-testing\r\ndescription: Capture and visually inspect Qt GUI widgets using screenshots. Use when asked to verify GUI rendering, test widget appearance, check layouts, or visually inspect any PySide6/Qt component. Enables Claude to \"see\" Qt interfaces by capturing offscreen screenshots and analyzing them with vision.\r\n---\r\n\r\n# Qt GUI Testing\r\n\r\nCapture screenshots of Qt widgets for visual inspection without displaying windows on screen.\r\n\r\n## Quick Start\r\n\r\n```python\r\n# Capture any widget\r\nfrom scripts.qt_capture import capture_widget\r\npath = capture_widget(my_widget, \"description_here\")\r\n# Then read the screenshot with the Read tool\r\n```\r\n\r\n## Core Script\r\n\r\nRun `scripts/qt_capture.py` or import `capture_widget` from it:\r\n\r\n```bash\r\n# Standalone test\r\nuv run --with PySide6 python .claude/skills/qt-testing/scripts/qt_capture.py\r\n```\r\n\r\n## Output Location\r\n\r\nAll screenshots save to: `scratch/.qt-screenshots/`\r\n\r\nNaming: `{YYYY-MM-DD.HH-MM-SS}_{description}.png`\r\n\r\n## Workflow\r\n\r\n1. Create/obtain the widget to test\r\n2. Call `capture_widget(widget, \"description\")`\r\n3. Read the saved screenshot with the Read tool\r\n4. Analyze with vision to verify correctness\r\n\r\n## Interaction Pattern\r\n\r\nTo interact with widgets (click buttons, etc.):\r\n\r\n```python\r\n# Find widget at coordinates (from vision analysis)\r\ntarget = widget.childAt(x, y)\r\n\r\n# Trigger it directly (not mouse events)\r\nif hasattr(target, 'click'):\r\n target.click()\r\n QApplication.processEvents()\r\n\r\n# Capture result\r\ncapture_widget(widget, \"after_click\")\r\n```\r\n\r\n## Example: Test a Dialog\r\n\r\n```python\r\nimport sys\r\nfrom PySide6.QtWidgets import QApplication\r\nfrom sleap.gui.learning.dialog import TrainingEditorDialog\r\n\r\n# Add skill scripts to path\r\nsys.path.insert(0, \".claude/skills/qt-testing\")\r\nfrom scripts.qt_capture import capture_widget, init_qt\r\n\r\napp = init_qt()\r\ndialog = TrainingEditorDialog()\r\npath = capture_widget(dialog, \"training_dialog\")\r\ndialog.close()\r\nprint(f\"Inspect: {path}\")\r\n```\r\n\r\n## Key Points\r\n\r\n- Uses `Qt.WA_DontShowOnScreen` - no window popup\r\n- Renders identically to on-screen display (verified)\r\n- Call `processEvents()` after interactions before capture\r\n- Use `childAt(x, y)` to map vision coordinates to widgets\r\n- Direct method calls (`.click()`) work; simulated mouse events don't\r\n" }, { "path": ".claude/skills/qt-testing/scripts/qt_capture.py", "content": "#!/usr/bin/env python\r\n\"\"\"Qt widget screenshot capture utility.\r\n\r\nCaptures screenshots of Qt widgets without displaying them on screen.\r\n\"\"\"\r\n\r\nimport sys\r\nfrom datetime import datetime\r\nfrom pathlib import Path\r\nfrom typing import Optional\r\n\r\nfrom PySide6.QtWidgets import QApplication, QWidget\r\nfrom PySide6.QtCore import Qt\r\n\r\n# Output directory\r\nOUTPUT_DIR = Path(\"scratch/.qt-screenshots\")\r\n\r\n\r\ndef init_qt() -> QApplication:\r\n \"\"\"Initialize Qt application (required once per process).\"\"\"\r\n app = QApplication.instance()\r\n if app is None:\r\n app = QApplication(sys.argv)\r\n return app\r\n\r\n\r\ndef timestamp() -> str:\r\n \"\"\"Generate timestamp for filename.\"\"\"\r\n return datetime.now().strftime(\"%Y-%m-%d.%H-%M-%S\")\r\n\r\n\r\ndef capture_widget(\r\n widget: QWidget,\r\n description: str,\r\n output_dir: Optional[Path] = None,\r\n) -> Path:\r\n \"\"\"\r\n Capture a screenshot of a widget without displaying it on screen.\r\n\r\n Args:\r\n widget: The QWidget to capture\r\n description: Short description for filename (use underscores, no spaces)\r\n output_dir: Override output directory (default: scratch/.qt-screenshots)\r\n\r\n Returns:\r\n Path to saved screenshot\r\n \"\"\"\r\n out = output_dir or OUTPUT_DIR\r\n out.mkdir(parents=True, exist_ok=True)\r\n\r\n # Configure for invisible rendering\r\n widget.setAttribute(Qt.WA_DontShowOnScreen, True)\r\n widget.show()\r\n QApplication.processEvents()\r\n\r\n # Capture\r\n pixmap = widget.grab()\r\n if pixmap.isNull():\r\n raise RuntimeError(\"Failed to capture widget - pixmap is null\")\r\n\r\n # Save with timestamp\r\n desc_clean = description.replace(\" \", \"_\").replace(\"/\", \"-\")\r\n filename = f\"{timestamp()}_{desc_clean}.png\"\r\n filepath = out / filename\r\n\r\n if not pixmap.save(str(filepath)):\r\n raise RuntimeError(f\"Failed to save screenshot to {filepath}\")\r\n\r\n # Don't close - caller may want to interact further\r\n widget.hide()\r\n\r\n return filepath\r\n\r\n\r\ndef capture_and_click(\r\n widget: QWidget,\r\n x: int,\r\n y: int,\r\n description: str,\r\n) -> tuple[Path, Optional[QWidget]]:\r\n \"\"\"\r\n Click at coordinates and capture the result.\r\n\r\n Args:\r\n widget: Parent widget\r\n x, y: Coordinates to click (in widget coordinate space)\r\n description: Description for filename\r\n\r\n Returns:\r\n Tuple of (screenshot path, clicked widget or None)\r\n \"\"\"\r\n widget.setAttribute(Qt.WA_DontShowOnScreen, True)\r\n widget.show()\r\n QApplication.processEvents()\r\n\r\n # Find and click widget at coordinates\r\n target = widget.childAt(x, y)\r\n if target is not None:\r\n if hasattr(target, \"click\"):\r\n target.click()\r\n elif hasattr(target, \"toggle\"):\r\n target.toggle()\r\n QApplication.processEvents()\r\n\r\n # Capture result\r\n path = capture_widget(widget, description)\r\n return path, target\r\n\r\n\r\n# Self-test when run directly\r\nif __name__ == \"__main__\":\r\n from PySide6.QtWidgets import QPushButton, QVBoxLayout, QLabel\r\n\r\n app = init_qt()\r\n\r\n # Create test widget\r\n widget = QWidget()\r\n widget.setWindowTitle(\"Qt Capture Test\")\r\n widget.setFixedSize(300, 150)\r\n\r\n layout = QVBoxLayout(widget)\r\n layout.addWidget(QLabel(\"Qt Capture Test Widget\"))\r\n btn = QPushButton(\"Test Button\")\r\n layout.addWidget(btn)\r\n\r\n # Capture it\r\n path = capture_widget(widget, \"self_test\")\r\n print(f\"Captured: {path}\")\r\n\r\n widget.close()\r\n print(\"Self-test complete!\")\r\n" }, { "path": ".claude/skills/sleap-support/SKILL.md", "content": "---\nname: sleap-support\ndescription: >\n Handle SLEAP GitHub support workflow for issues and discussions. Use when\n the user says \"support\", provides a GitHub issue/discussion number like \"#2512\",\n or asks to investigate a user report from talmolab/sleap. Scaffolds investigation\n folders, downloads posts with images, analyzes problems, and drafts friendly responses.\n---\n\n# SLEAP Support Workflow\n\nHandle GitHub issues and discussions from `talmolab/sleap` with a systematic investigation process.\n\n## Quick Start\n\nWhen given an issue/discussion number:\n\n```bash\n# Check both issues AND discussions (users often post in wrong category)\ngh issue view 2512 --repo talmolab/sleap --json number,title,body,author,createdAt,comments 2>/dev/null || \\\ngh api repos/talmolab/sleap/discussions/2512 2>/dev/null\n```\n\n## Workflow Steps\n\n### 1. Create Investigation Folder\n\nFirst, check if an investigation already exists:\n\n```bash\nls -d scratch/*-*2512* 2>/dev/null || echo \"No existing investigation\"\n```\n\nIf none exists, create one:\n\n```bash\nmkdir -p scratch/$(date +%Y-%m-%d)-{issue|discussion}-2512-{short-description}\n```\n\n### 2. Fetch Post Content\n\n**For Issues:**\n```bash\ngh issue view NUMBER --repo talmolab/sleap --json number,title,body,author,createdAt,comments,labels > scratch/.../issue.json\n```\n\n**For Discussions:**\n```bash\ngh api repos/talmolab/sleap/discussions/NUMBER > scratch/.../discussion.json\n```\n\n### 3. Download Images\n\nExtract image URLs from the post body and download:\n\n```bash\n# Parse markdown image links: ![alt](url)\ngrep -oP '!\\[.*?\\]\\(\\K[^)]+' scratch/.../issue.json | while read url; do\n wget -P scratch/.../images/ \"$url\"\ndone\n```\n\n### 4. Create USER_POST.md\n\nConvert the JSON to readable markdown with inline images:\n\n```markdown\n# Issue/Discussion #NUMBER: Title\n\n**Author**: @username\n**Created**: YYYY-MM-DD\n**Platform**: (extract from post if mentioned)\n**SLEAP Version**: (extract from post if mentioned)\n\n## Original Post\n\n[post body with images referenced inline]\n\n## Comments\n\n[any replies]\n```\n\n### 5. Write Investigation README\n\nCreate `scratch/.../README.md`:\n\n```markdown\n# Investigation: Issue/Discussion #NUMBER\n\n**Date**: YYYY-MM-DD\n**Post**: https://github.com/talmolab/sleap/{issues|discussions}/NUMBER\n**Author**: @username\n**Type**: Bug Report | Usage Question | Feature Request\n\n## Summary\n\n[1-2 sentence summary of the issue]\n\n## Key Information\n\n- **Platform**: Windows/macOS/Linux\n- **SLEAP Version**: X.Y.Z\n- **GPU**: (if relevant)\n- **Dataset**: (if described)\n\n## Preliminary Analysis\n\n[Initial thoughts on what might be happening]\n\n## Areas to Investigate\n\n- [ ] Check area 1\n- [ ] Check area 2\n\n## Files\n\n- `USER_POST.md` - Original post content\n- `images/` - Downloaded screenshots\n- `RESPONSE_DRAFT.md` - Draft response (when ready)\n```\n\n### 6. Check Release History First\n\n**Before deep investigation, check if the issue was already fixed:**\n\n```bash\n# Get user's SLEAP version from their post (look for sleap doctor output or sleap.__version__)\nUSER_VERSION=\"1.4.0\" # example\n\n# Check sleap releases for fixes\ngh release list --repo talmolab/sleap --limit 20\ngh release view v1.5.0 --repo talmolab/sleap --json body -q '.body' | grep -i \"fix\"\n\n# For sleap-io issues\ngh release list --repo talmolab/sleap-io --limit 10\ngh release view v0.6.0 --repo talmolab/sleap-io --json body -q '.body'\n\n# For sleap-nn/training issues\ngh release list --repo talmolab/sleap-nn --limit 10\ngh release view v0.2.0 --repo talmolab/sleap-nn --json body -q '.body'\n```\n\n**If a fix exists in a newer version:**\n- Response should guide user to upgrade\n- Include the specific version with the fix\n- Mention what was fixed (link to PR/issue if available)\n\n**If investigating an unfixed bug:**\n- Checkout the user's version to see their actual code:\n\n```bash\n# Clone repos if not present\n[ -d scratch/repos/sleap-io ] || gh repo clone talmolab/sleap-io scratch/repos/sleap-io\n[ -d scratch/repos/sleap-nn ] || gh repo clone talmolab/sleap-nn scratch/repos/sleap-nn\n\n# Checkout user's version\ncd scratch/repos/sleap-io && git fetch --tags && git checkout v0.5.0\ncd scratch/repos/sleap-nn && git fetch --tags && git checkout v0.1.5\n\n# Now you're looking at the code they're actually running\n```\n\n**Use `git blame` to find potential culprits:**\n\n```bash\n# Find when a suspicious function was last changed\ngit blame -L 50,100 sleap/io/main.py\n\n# Check if a line was changed recently\ngit log --oneline -5 -- path/to/file.py\n\n# Find the commit that introduced a specific change\ngit log -S \"function_name\" --oneline\n```\n\n### 7. Analyze and Reproduce\n\nDetermine the issue type:\n\n**Usage Question**: Check if documentation covers this. Common topics:\n- Model configuration (skeleton, training params)\n- Multi-animal vs single-animal tracking\n- Inference and tracking settings\n- Data format questions\n\n**Bug Report**: Try to reproduce. Check:\n- Version-specific issues\n- Platform-specific behavior\n- GPU/CUDA compatibility\n- Data corruption signs\n\n**Feature Request**: Note for tracking, no immediate action needed.\n\n### 8. Determine Data Needs\n\n**When to request SLP file:**\n- Inference/tracking issues that can't be diagnosed from logs\n- \"Labels not showing\" or display issues\n- Merging or import problems\n- Corruption or data loss reports\n\n**Suggest upload to `https://slp.sh`** - our SLP file sharing service.\n\n**If SLP provided:** Download and analyze:\n```bash\nsio show path/to/file.slp --summary\nsio show path/to/file.slp --videos\nsio show path/to/file.slp --skeleton\n```\n\n### 9. Draft Response\n\nCreate `RESPONSE_DRAFT.md` following this structure:\n\n```markdown\nHi @{username},\n\nThanks for the post!\n\n[Restate understanding: \"If I understand correctly, you're seeing X when you try to Y...\"]\n\n[Provide solution OR request more info]\n\n[If requesting info, give EXPLICIT instructions:]\n- Use `sleap doctor` CLI for diagnostics\n- Provide copy-paste terminal commands\n- Assume non-technical user\n\nLet us know if that works for you!\n\nCheers,\n\n:heart: Talmo & Claude :robot:\n\n
\nExtended technical analysis\n\n[Detailed investigation notes, code traces, version checks]\n\n
\n```\n\n## Response Tone Guidelines\n\n- **Opening**: Bright and positive (\"Thanks for the post!\", \"Great question!\")\n- **Body**: Clear, concise, non-technical language\n- **Instructions**: Step-by-step, assume terminal newbie\n- **Closing**: Encouraging (\"Let us know if that works for you!\")\n- **Signature**: `:heart: Talmo & Claude :robot:`\n\n## When Requesting User Actions\n\n**Terminal commands must be copy-paste ready:**\n\n```bash\n# Good - one-liner, no environment activation needed\nsleap doctor\n\n# Good - uses uvx for isolated execution\nuvx sio show your_file.slp --summary\n\n# Bad - assumes environment knowledge\nsource activate sleap && python -c \"import sleap; print(sleap.__version__)\"\n```\n\n## Related Repositories\n\n**For data/I/O issues** - Check `talmolab/sleap-io`:\n- Local: `../sleap-io` (preferred)\n- Clone if needed: `gh repo clone talmolab/sleap-io scratch/repos/sleap-io`\n- Key docs: `sleap-io/docs/examples.md`, `sleap-io/docs/formats/SLP.md`\n- CLI: `sio show --help` for inspection commands\n\n**For training/inference issues** - Check `talmolab/sleap-nn`:\n- Local: `../sleap-nn` (preferred)\n- Clone if needed: `gh repo clone talmolab/sleap-nn scratch/repos/sleap-nn`\n- Topics: Model configs, training params, evaluation metrics, tracking\n\n## Common Patterns\n\n### Instance Duplication\n- Check track assignment logic\n- Look for ID switching during tracking\n- May need `sleap-track` with different settings\n\n### Training Issues\n- GPU memory: suggest reducing batch size\n- Loss not decreasing: check learning rate, augmentation\n- NaN losses: data normalization issues\n\n### Import/Export Issues\n- Format compatibility (H5 vs SLP versions)\n- Missing video paths\n- Skeleton definition mismatches\n\n### GUI Issues\n- Qt/PySide6 version conflicts\n- Display scaling on high-DPI\n- Video codec issues\n\n## Confirmation Before Posting\n\n**ALWAYS** confirm with the developer before posting:\n1. Show the full draft response\n2. Ask: \"Does this look good to post?\"\n3. Wait for explicit approval\n\n**Never post automatically** - support responses represent the project.\n" }, { "path": ".claude/skills/sleap-support/gh-commands.md", "content": "# GitHub CLI Commands Reference\n\nQuick reference for `gh` commands used in support workflow.\n\n## Fetching Issues\n\n```bash\n# Get issue with all details\ngh issue view NUMBER --repo talmolab/sleap --json number,title,body,author,createdAt,comments,labels,state\n\n# Get issue body only (for quick reading)\ngh issue view NUMBER --repo talmolab/sleap --json body -q '.body'\n\n# Get issue comments\ngh issue view NUMBER --repo talmolab/sleap --json comments -q '.comments[].body'\n\n# List recent issues\ngh issue list --repo talmolab/sleap --limit 10 --json number,title,author,createdAt\n```\n\n## Fetching Discussions\n\n```bash\n# Get discussion details\ngh api repos/talmolab/sleap/discussions/NUMBER\n\n# Get discussion body\ngh api repos/talmolab/sleap/discussions/NUMBER -q '.body'\n\n# Get discussion comments\ngh api repos/talmolab/sleap/discussions/NUMBER -q '.comments.nodes[].body'\n\n# List recent discussions\ngh api repos/talmolab/sleap/discussions --jq '.[] | {number, title, author: .user.login}'\n```\n\n## Posting Replies\n\n**Issues:**\n```bash\ngh issue comment NUMBER --repo talmolab/sleap --body \"Your response here\"\n\n# Or from file\ngh issue comment NUMBER --repo talmolab/sleap --body-file RESPONSE_DRAFT.md\n```\n\n**Discussions:**\n```bash\n# Need to use API for discussions\ngh api repos/talmolab/sleap/discussions/NUMBER/comments -X POST -f body=\"Your response\"\n```\n\n## Searching\n\n```bash\n# Search issues by keyword\ngh issue list --repo talmolab/sleap --search \"keyword\" --json number,title\n\n# Search with labels\ngh issue list --repo talmolab/sleap --label \"bug\" --json number,title\n\n# Search discussions\ngh api repos/talmolab/sleap/discussions -q '.[] | select(.title | test(\"keyword\"; \"i\")) | {number, title}'\n```\n\n## Checking Both Issues AND Discussions\n\nUsers often post in the wrong category. Always check both:\n\n```bash\n# Try issue first, fall back to discussion\ngh issue view NUMBER --repo talmolab/sleap 2>/dev/null || \\\ngh api repos/talmolab/sleap/discussions/NUMBER 2>/dev/null || \\\necho \"Not found as issue or discussion\"\n```\n\n## Downloading Images\n\nImages in posts use GitHub's CDN format:\n```\nhttps://user-images.githubusercontent.com/...\nhttps://github.com/user-attachments/assets/...\n```\n\nDownload all images from a post:\n```bash\n# Extract and download image URLs\ngrep -oE 'https://[^)]+\\.(png|jpg|jpeg|gif)' post.md | while read url; do\n wget -q -P images/ \"$url\"\ndone\n```\n\n## Labels\n\nCommon labels in talmolab/sleap:\n- `bug` - Bug reports\n- `enhancement` - Feature requests\n- `question` - Usage questions\n- `documentation` - Doc improvements needed\n- `good first issue` - Beginner-friendly\n\nAdd a label:\n```bash\ngh issue edit NUMBER --repo talmolab/sleap --add-label \"label-name\"\n```\n\n## Closing Issues\n\n```bash\n# Close with comment\ngh issue close NUMBER --repo talmolab/sleap --comment \"Closing as resolved. Feel free to reopen if needed!\"\n\n# Close as not planned\ngh issue close NUMBER --repo talmolab/sleap --reason \"not planned\" --comment \"Thanks for the suggestion!\"\n```\n" }, { "path": ".claude/skills/sleap-support/response-templates.md", "content": "# Response Templates\n\n## Opening Lines\n\nUse variety - pick one that fits the context:\n\n**For questions:**\n- \"Thanks for the post!\"\n- \"Great question!\"\n- \"Thanks for reaching out!\"\n\n**For bug reports:**\n- \"Thanks for the detailed report!\"\n- \"Thanks for flagging this!\"\n- \"Appreciate you reporting this!\"\n\n**For feature requests:**\n- \"Thanks for the suggestion!\"\n- \"Interesting idea!\"\n\n## Requesting More Information\n\n### Need SLP File\n\n```markdown\nTo help diagnose this, it would be helpful to see your project file. Could you upload your `.slp` file to https://slp.sh and share the link here?\n\nThis is our secure file sharing service - files are automatically deleted after 7 days.\n```\n\n### Need System Info\n\n```markdown\nCould you share the output of `sleap doctor`? You can run this in a terminal:\n\n1. Open a terminal (Command Prompt on Windows, Terminal on Mac/Linux)\n2. Type `sleap doctor` and press Enter\n3. Copy-paste the output here\n\nThis will help us understand your setup!\n```\n\n### Need Error Logs\n\n```markdown\nCould you share the full error message? If you're running from the GUI:\n\n1. Open SLEAP from a terminal instead of clicking the icon\n2. On Windows: Open Command Prompt, type `sleap-label`, press Enter\n3. On Mac/Linux: Open Terminal, type `sleap-label`, press Enter\n4. Reproduce the error\n5. Copy-paste any red text from the terminal here\n```\n\n### Need Video Info\n\n```markdown\nCould you tell us more about your video?\n\n1. What format is it? (mp4, avi, etc.)\n2. How long is it? (minutes/hours)\n3. What resolution? (you can check this by right-clicking the file ā†’ Properties ā†’ Details on Windows)\n```\n\n## Common Solutions\n\n### GPU Memory Issues\n\n```markdown\nThis looks like a GPU memory issue! Try reducing the batch size:\n\n1. In the training dialog, look for \"Batch Size\"\n2. Try halving it (e.g., if it's 4, try 2)\n3. If that still fails, try 1\n\nSmaller batch sizes use less GPU memory but may train a bit slower.\n```\n\n### Multi-Animal Tracking\n\n```markdown\nFor multi-animal projects, make sure you're using the correct tracking method:\n\n1. After inference, go to `Predict` ā†’ `Run Tracking...`\n2. Try the \"Simple\" tracker first\n3. If that doesn't work well, try \"Flow\" tracker for animals that move smoothly\n\nThe tracker connects detections across frames - without it, you'll see different track IDs each frame!\n```\n\n### Missing Labels After Import\n\n```markdown\nIf labels aren't showing after import, the skeleton might not match:\n\n1. Go to `Skeleton` menu ā†’ check that nodes and edges match your data\n2. If they don't match, you may need to re-import with the correct skeleton\n\nYou can check your file's skeleton with:\n```bash\nuvx sio show your_file.slp --skeleton\n```\n```\n\n## Closing Lines\n\n**Standard:**\n```markdown\nLet us know if that works for you!\n\nCheers,\n\n:heart: Talmo & Claude :robot:\n```\n\n**If uncertain about solution:**\n```markdown\nLet us know if this helps or if you're still running into issues!\n\nCheers,\n\n:heart: Talmo & Claude :robot:\n```\n\n**If they need to try something:**\n```markdown\nGive that a try and let us know how it goes!\n\nCheers,\n\n:heart: Talmo & Claude :robot:\n```\n\n## Technical Details Section\n\nAlways include this at the end:\n\n```markdown\n
\nExtended technical analysis\n\n## Investigation Notes\n\n- [What you checked]\n- [What you found]\n- [Relevant code paths]\n\n## Relevant Files\n\n- `sleap/path/to/file.py:123` - [what it does]\n\n## Version Info\n\n- Reported version: X.Y.Z\n- Current version: A.B.C\n- Relevant changes: [if any]\n\n
\n```\n" }, { "path": ".claude/skills/sleap-support/troubleshooting-patterns.md", "content": "# Troubleshooting Patterns\n\nQuick reference for common issues and diagnostic steps.\n\n## Training Issues\n\n### Loss Not Decreasing\n\n**Symptoms**: Training runs but loss stays flat or increases\n\n**Diagnostics**:\n1. Check learning rate (too high?)\n2. Check data augmentation (too aggressive?)\n3. Check labels (are they correct?)\n\n**Common fixes**:\n- Reduce learning rate by 10x\n- Disable rotation augmentation initially\n- Verify skeleton is correct\n\n### NaN Loss\n\n**Symptoms**: Loss becomes NaN early in training\n\n**Diagnostics**:\n1. Check for empty frames (no labels)\n2. Check image normalization\n3. Check for corrupted images\n\n**Common fixes**:\n- Remove frames with no instances\n- Check video codec compatibility\n\n### Out of Memory (OOM)\n\n**Symptoms**: CUDA out of memory error\n\n**Diagnostics**:\n```bash\n# Check GPU memory\nnvidia-smi\n```\n\n**Common fixes**:\n- Reduce batch size\n- Reduce input scale\n- Use smaller backbone\n\n## Inference Issues\n\n### No Predictions\n\n**Symptoms**: Model runs but no instances detected\n\n**Diagnostics**:\n1. Check confidence threshold\n2. Check model was trained on similar data\n3. Check input resolution matches training\n\n**Common fixes**:\n- Lower confidence threshold in inference settings\n- Retrain with more diverse data\n\n### Instance Duplication\n\n**Symptoms**: Same animal gets multiple instances\n\n**Diagnostics**:\n1. Check NMS threshold\n2. Check centroid model predictions\n3. Review tracking settings\n\n**Common fixes**:\n- Increase NMS threshold\n- Use tracking to merge duplicates\n- Check multi-animal vs single-animal settings\n\n### Track ID Switching\n\n**Symptoms**: Animal identities swap between frames\n\n**Diagnostics**:\n1. Check tracker settings\n2. Check for occlusions in video\n3. Review prediction confidence\n\n**Common fixes**:\n- Try different tracker (Simple ā†’ Flow)\n- Increase tracking window\n- Manual correction in GUI\n\n## GUI Issues\n\n### SLEAP Won't Start\n\n**Symptoms**: Nothing happens or immediate crash\n\n**Diagnostics**:\n```bash\n# Run from terminal to see errors\nsleap-label\n```\n\n**Common causes**:\n- Qt/PySide6 conflicts\n- Missing dependencies\n- Corrupted installation\n\n**Fixes**:\n- Reinstall following the [installation guide](https://sleap.ai/installation.html)\n- Check Python version compatibility\n- Run `sleap doctor` to verify environment\n\n### Video Won't Load\n\n**Symptoms**: Error when opening video\n\n**Diagnostics**:\n```bash\n# Check video info\nffprobe video.mp4\n```\n\n**Common causes**:\n- Unsupported codec\n- Corrupted file\n- Path with special characters\n\n**Fixes**:\n- Re-encode video: `ffmpeg -i input.mp4 -c:v libx264 output.mp4`\n- Move to path without spaces/special chars\n\n### Display Issues\n\n**Symptoms**: UI elements too small/large, rendering problems\n\n**Common causes**:\n- High-DPI scaling issues\n- OpenGL driver problems\n\n**Fixes**:\n- Set environment variable: `QT_SCALE_FACTOR=1.5`\n- Update graphics drivers\n\n## Data Issues\n\n### Can't Open SLP File\n\n**Symptoms**: Error loading project\n\n**Diagnostics**:\n```bash\nuvx sio show file.slp --summary\n```\n\n**Common causes**:\n- File corruption\n- Version incompatibility\n- Missing video files\n\n**Fixes**:\n- Try loading with `fix_videos=True`\n- Check video paths match\n\n### Missing Labels\n\n**Symptoms**: Labels exist but don't display\n\n**Diagnostics**:\n1. Check skeleton matches\n2. Check frame indices\n3. Check instance visibility\n\n**Fixes**:\n- Verify skeleton: `uvx sio show file.slp --skeleton`\n- Check track visibility in GUI\n\n### Merge Conflicts\n\n**Symptoms**: Issues when merging SLP files\n\n**Diagnostics**:\n```bash\n# Compare skeletons\nuvx sio show file1.slp --skeleton\nuvx sio show file2.slp --skeleton\n```\n\n**Common causes**:\n- Different skeleton definitions\n- Overlapping frame ranges\n- Different video sources\n\n**Fixes**:\n- Ensure identical skeletons before merge\n- Use sleap-io merge with conflict resolution\n\n## Version-Specific Issues\n\n### Upgrading from v1.2 to v1.3+\n\n- HDF5 format changed\n- Need to re-export old projects\n\n### Python 3.11+ Compatibility\n\n- Some dependencies may have issues\n- Check dependency versions\n\n### GPU/CUDA Issues\n\n**Diagnostics**:\n```bash\n# Check GPU availability (sleap doctor reports this)\nsleap doctor\n\n# Check NVIDIA driver\nnvidia-smi\n```\n\n**Common fixes**:\n- Ensure CUDA toolkit matches TensorFlow version\n- Check cuDNN installation\n- See [GPU setup guide](https://sleap.ai/installation.html#gpu-support)\n\n## Version-Aware Investigation\n\n**IMPORTANT**: Before diving deep into investigation, always check if the issue was already fixed.\n\n### Step 1: Identify User's Versions\n\nLook in the user's post for version information:\n- `sleap doctor` output (preferred - contains all relevant info)\n- `sleap.__version__` output\n- Error tracebacks mentioning package paths\n\nIf version info is missing, request `sleap doctor` output from the user.\n\n### Step 2: Check Release Notes for Fixes\n\n```bash\n# SLEAP main package releases\ngh release list --repo talmolab/sleap --limit 20\n\n# View specific release notes\ngh release view v1.5.0 --repo talmolab/sleap\n\n# Search release notes for keywords\ngh release view v1.5.0 --repo talmolab/sleap --json body -q '.body' | grep -i \"fix\\|bug\\|issue\"\n\n# sleap-io releases (data I/O issues)\ngh release list --repo talmolab/sleap-io --limit 10\ngh release view v0.6.0 --repo talmolab/sleap-io\n\n# sleap-nn releases (training/inference issues)\ngh release list --repo talmolab/sleap-nn --limit 10\ngh release view v0.2.0 --repo talmolab/sleap-nn\n```\n\n### Step 3: Match User's Version to Code\n\nIf the user's version is older, checkout that version to see their actual code:\n\n```bash\n# Ensure repos are cloned\n[ -d scratch/repos/sleap-io ] || gh repo clone talmolab/sleap-io scratch/repos/sleap-io\n[ -d scratch/repos/sleap-nn ] || gh repo clone talmolab/sleap-nn scratch/repos/sleap-nn\n\n# Checkout user's sleap-io version\ncd scratch/repos/sleap-io\ngit fetch --tags\ngit checkout v0.5.0 # user's version\n\n# Checkout user's sleap-nn version\ncd scratch/repos/sleap-nn\ngit fetch --tags\ngit checkout v0.1.5 # user's version\n\n# Return to sleap main repo\ncd /home/talmo/code/sleap\n```\n\n### Step 4: Use Git Blame to Find Culprits\n\nWhen you've identified a suspicious code region:\n\n```bash\n# Blame specific lines\ngit blame -L 50,100 path/to/file.py\n\n# Show who changed a function recently\ngit log --oneline -10 -- path/to/file.py\n\n# Find commits that added/removed a string\ngit log -S \"suspicious_function\" --oneline\n\n# Find commits touching a pattern\ngit log -G \"regex_pattern\" --oneline -- path/to/file.py\n\n# Show the diff for a specific commit\ngit show COMMIT_HASH --stat\ngit show COMMIT_HASH -- path/to/file.py\n```\n\n### Step 5: Compare Versions\n\n```bash\n# See what changed between user's version and current\ngit diff v1.4.0..HEAD -- path/to/file.py\n\n# List commits between versions\ngit log --oneline v1.4.0..HEAD -- path/to/file.py\n\n# Find when a bug was introduced (bisect conceptually)\ngit log --oneline --all -- path/to/file.py | head -20\n```\n\n### Response Scenarios\n\n**Best case - Already fixed:**\n```markdown\nGood news! This issue was fixed in version X.Y.Z (PR #123).\n\nTo fix this, upgrade to the latest version of SLEAP. You can verify your current version with:\n\\`\\`\\`bash\nsleap doctor\n\\`\\`\\`\n\nSee the [installation guide](https://sleap.ai/installation.html) for upgrade instructions for your setup.\n\nLet us know if that resolves it!\n```\n\n**Issue exists in their version:**\n```markdown\nI checked the code for version X.Y.Z and I can see the issue.\nThis appears to be caused by [explanation].\n\n[Solution or workaround]\n```\n\n**Need to backport a fix:**\n- Note the specific commit that fixed it\n- Check if it can be cleanly cherry-picked\n- Consider releasing a patch version\n" }, { "path": ".github/ISSUE_TEMPLATE/bug_report.md", "content": "---\nname: \"\\U0001F41B Bug report\"\nabout: Create a report to help us repair something that is currently broken\nlabels: bug\n---\n\n\n\n## Bug description\n\n\n\n## Expected behaviour\n\n\n\n## Actual behaviour\n\n\n\n## How to reproduce\n\n\n\n1. Go to '...'\n2. Click on '....'\n3. Scroll down to '....'\n4. See error\n\n## Screenshots\n\n\n\n## System diagnostics\n\n\n\n
Output of sleap doctor\n\n```\nSLEAP System Diagnostics\n========================\nGenerated: 2026-01-30 12:00:00\n\n[Platform]\n OS: Darwin 24.6.0\n Platform: macOS-15.7.3-arm64-arm-64bit-Mach-O\n ...\n\n[Python]\n Version: 3.13.7\n ...\n\n[GPU / CUDA]\n PyTorch: v2.10.0 (MPS)\n ...\n\n[Packages]\n sleap: v1.6.0\n sleap-io: v0.6.3\n sleap-nn: v0.1.0a4\n ...\n```\n\n
\n\n
Logs\n\n\n\n```\n# paste relevant logs here, if any\n```\n\n
\n" }, { "path": ".github/ISSUE_TEMPLATE/config.yml", "content": "contact_links:\n - name: \"\\U0001F914 Ask any general questions relating to SLEAP\"\n url: https://github.com/talmolab/sleap/discussions/categories/help\n about: Ask general questions or get help.\n - name: \"\\U0001F4A1 Propose new features or enhancements for SLEAP\"\n url: https://github.com/talmolab/sleap/discussions/categories/ideas\n about: Propose new features or enhancements.\n - name: \"\\U0001F4DD See the SLEAP documentation\"\n url: https://docs.sleap.ai\n about: See the documentation\n" }, { "path": ".github/workflows/build.yml", "content": "# Package builds\nname: Build\n\non:\n release:\n types:\n - published\n workflow_dispatch: # Manual trigger for re-runs\n\njobs:\n pypi:\n name: PyPI Wheel\n runs-on: ubuntu-22.04\n permissions:\n id-token: write # Required for PyPI trusted publishing\n steps:\n\n - name: Checkout repo\n uses: actions/checkout@v4\n\n - name: Setup UV\n uses: astral-sh/setup-uv@v6\n with:\n python-version: \"3.13\"\n\n - name: Build distributions\n run: |\n uv build\n\n - name: Publish to PyPI\n run: |\n uv publish" }, { "path": ".github/workflows/ci.yml", "content": "# Continuous integration\nname: CI\n\non:\n pull_request:\n types: [opened, reopened, synchronize]\n push:\n branches:\n - main\n - develop\n workflow_dispatch: # Allow manual triggers\n\njobs:\n # Check which files changed to determine if we need to run tests\n changes:\n name: Check for changes\n runs-on: ubuntu-latest\n outputs:\n code: ${{ steps.filter.outputs.code }}\n steps:\n - uses: actions/checkout@v4\n - uses: dorny/paths-filter@v3\n id: filter\n with:\n filters: |\n code:\n - 'sleap/**'\n - 'tests/**'\n - '.github/workflows/ci.yml'\n - 'pyproject.toml'\n\n # Lint with ruff\n lint:\n needs: changes\n if: needs.changes.outputs.code == 'true' || github.event_name == 'workflow_dispatch'\n # This job runs:\n #\n # 1. Linting and formatting checks with ruff\n #\n # Note: This uses Google-style docstring convention\n # Ref: https://google.github.io/styleguide/pyguide.html\n name: Lint\n runs-on: \"ubuntu-latest\"\n steps:\n\n - name: Checkout repo\n uses: actions/checkout@v4\n\n - name: Setup UV\n uses: astral-sh/setup-uv@v6\n with:\n python-version: \"3.13\"\n\n - name: Install dependencies\n run: |\n uv sync --extra nn-cpu\n\n - name: Run ruff checks\n run: |\n uv run ruff check sleap tests\n \n - name: Run ruff format check\n run: |\n uv run ruff format --check sleap tests\n\n # Tests with pytest\n tests:\n needs: changes\n if: needs.changes.outputs.code == 'true' || github.event_name == 'workflow_dispatch'\n timeout-minutes: 30\n strategy:\n fail-fast: false\n matrix:\n os: [\"ubuntu\", \"windows\", \"mac\"]\n # os: [\"ubuntu\", \"windows\", \"mac\", \"self-hosted-gpu\"]\n include:\n - os: ubuntu\n runs-on: ubuntu-latest\n - os: windows\n runs-on: windows-latest\n - os: mac\n runs-on: macos-latest\n # - os: self-hosted-gpu\n # runs-on: [self-hosted, puma, gpu, 2xgpu]\n\n name: Tests (${{ matrix.os }})\n runs-on: ${{ matrix.runs-on }}\n steps:\n - name: Checkout repo\n uses: actions/checkout@v4\n\n - name: Setup UV\n uses: astral-sh/setup-uv@v6\n with:\n python-version: \"3.13\"\n\n - name: Install dependencies\n run: |\n uv sync --extra nn-cpu\n # uv pip install PySide6==6.7.3 # exit code 139\n # uv pip install PySide6==6.8.1.1 # exit code 139\n # uv pip install PySide6==6.8.3 # exit code 139\n # uv pip install PySide6==6.9.1 # exit code 139\n\n - name: Print environment info\n run: |\n uv run python --version\n uv pip freeze\n\n - name: Install graphics dependencies on Ubuntu\n if: ${{ startsWith(matrix.os, 'ubuntu') }}\n run: |\n sudo apt-get update\n sudo apt-get install -y \\\n xvfb \\\n libxkbcommon-x11-0 \\\n libxcb1 \\\n libxcb-util1 \\\n libxcb-cursor0 \\\n libxcb-icccm4 \\\n libxcb-image0 \\\n libxcb-keysyms1 \\\n libxcb-render0 \\\n libxcb-render-util0 \\\n libxcb-shm0 \\\n libxcb-xfixes0 \\\n libxcb-randr0 \\\n libxcb-shape0 \\\n libxcb-xinerama0 \\\n libx11-xcb1 \\\n libxext6 \\\n libxi6 \\\n libxrender1 \\\n libxrandr2 \\\n libfontconfig1 \\\n libdbus-1-3 \\\n libfreetype6 \\\n libegl1 \\\n libgl1\n\n - name: Test with pytest (with coverage)\n env:\n PYTHONFAULTHANDLER: \"1\"\n OMP_NUM_THREADS: \"1\"\n MKL_NUM_THREADS: \"1\"\n OPENBLAS_NUM_THREADS: \"1\"\n NUMEXPR_NUM_THREADS: \"1\"\n QT_OPENGL: \"software\"\n # MPLBACKEND: \"Agg\"\n shell: bash\n run: |\n # Run pytest and capture the exit code\n set +e # Don't exit immediately on non-zero return\n uv run pytest --cov=sleap --cov-report=xml --durations=-1 tests/ -v\n EXIT_CODE=$?\n set -e # Re-enable exit on error\n \n # Check the exit code\n if [ \"$EXIT_CODE\" = \"0\" ]; then\n # Tests passed normally\n echo \"Tests passed successfully\"\n exit 0\n elif [ \"$EXIT_CODE\" = \"139\" ]; then\n # Exit code 139 is a segfault - we're temporarily allowing this\n # TODO: Remove this workaround once the segfault issues are resolved\n echo \"Warning: Tests completed with exit code 139 (segfault) - treating as pass for now\"\n exit 0\n else\n # Any other non-zero exit code is a failure\n echo \"Tests failed with exit code $EXIT_CODE\"\n exit $EXIT_CODE\n fi\n\n - name: Upload coverage\n uses: codecov/codecov-action@v4\n with:\n fail_ci_if_error: true\n verbose: false\n token: ${{ secrets.CODECOV_TOKEN }}\n\n # This job aggregates all CI results into a single check for branch protection.\n # It succeeds if all jobs passed OR were correctly skipped (e.g., docs-only changes).\n ci:\n name: CI\n if: always()\n needs: [changes, lint, tests]\n runs-on: ubuntu-latest\n steps:\n - name: Check CI status\n run: |\n echo \"changes: ${{ needs.changes.result }}\"\n echo \"lint: ${{ needs.lint.result }}\"\n echo \"tests: ${{ needs.tests.result }}\"\n\n # Fail if any job failed or was cancelled\n if [[ \"${{ needs.changes.result }}\" == \"failure\" || \"${{ needs.changes.result }}\" == \"cancelled\" ]]; then\n echo \"::error::changes job failed or was cancelled\"\n exit 1\n fi\n if [[ \"${{ needs.lint.result }}\" == \"failure\" || \"${{ needs.lint.result }}\" == \"cancelled\" ]]; then\n echo \"::error::lint job failed or was cancelled\"\n exit 1\n fi\n if [[ \"${{ needs.tests.result }}\" == \"failure\" || \"${{ needs.tests.result }}\" == \"cancelled\" ]]; then\n echo \"::error::tests job failed or was cancelled\"\n exit 1\n fi\n\n echo \"All CI checks passed or were correctly skipped!\"\n" }, { "path": ".github/workflows/docs.yml", "content": "name: Deploy MkDocs to GitHub Pages\n\non:\n release:\n types:\n - published\n push:\n branches:\n - develop\n\n# Ensure only one docs deployment runs at a time to prevent gh-pages push conflicts\nconcurrency:\n group: docs-deployment\n cancel-in-progress: false\n\njobs:\n docs:\n name: Docs\n runs-on: \"ubuntu-latest\"\n permissions:\n contents: write\n steps:\n - name: Checkout repo\n uses: actions/checkout@v4\n with:\n fetch-depth: 0\n\n - name: Setup UV\n uses: astral-sh/setup-uv@v6\n with:\n python-version: \"3.13\"\n\n - name: Install dependencies\n run: |\n uv sync --extra nn-cpu\n\n - name: Print environment info\n run: |\n which python\n uv pip list\n uv pip freeze\n\n - name: Setup Git user\n run: |\n git config --global user.name \"github-actions[bot]\"\n git config --global user.email \"github-actions[bot]@users.noreply.github.com\"\n\n - name: Build and upload docs (stable release)\n if: ${{ github.event_name == 'release' && github.event.action == 'published' && !github.event.release.prerelease }}\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n run: |\n uv run mike deploy --update-aliases --allow-empty --push \"${{ github.event.release.tag_name }}\" latest\n\n - name: Build and upload docs (pre-release)\n if: ${{ github.event_name == 'release' && github.event.action == 'published' && github.event.release.prerelease }}\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n run: |\n uv run mike deploy --update-aliases --allow-empty --push \"${{ github.event.release.tag_name }}\" prerelease\n\n - name: Build and upload docs (dev)\n if: ${{ github.event_name == 'push' }}\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n run: |\n uv run mike deploy --update-aliases --allow-empty --push dev" }, { "path": ".github/workflows/pr-preview.yml", "content": "# PR-local documentation preview deployment\n# Each PR gets its own isolated preview at https://docs.sleap.ai/pr/{number}/\n# This prevents parallel PRs from clobbering each other's docs\nname: Docs - PR Preview\n\non:\n pull_request:\n types: [opened, reopened, synchronize, closed]\n paths:\n - \"sleap/**\"\n - \"docs/**\"\n - \"mkdocs.yml\"\n - \".github/workflows/pr-preview.yml\"\n\n# Per-PR concurrency: each PR gets its own group, allowing parallel PR previews\n# cancel-in-progress: true means new pushes to the same PR cancel previous builds\nconcurrency:\n group: pr-preview-${{ github.event.number }}\n cancel-in-progress: true\n\npermissions:\n contents: write # To push to gh-pages branch\n pull-requests: write # To post comments on PRs\n\njobs:\n deploy:\n name: Deploy Preview\n if: github.event.action != 'closed'\n runs-on: ubuntu-latest\n steps:\n - name: Checkout repo\n uses: actions/checkout@v4\n with:\n fetch-depth: 0 # Fetch all history for diff computation\n\n - name: Setup UV\n uses: astral-sh/setup-uv@v6\n with:\n python-version: \"3.13\"\n\n - name: Install dependencies\n run: uv sync --extra nn-cpu\n\n - name: Build docs\n env:\n GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n run: uv run mkdocs build --site-dir _site\n\n - name: Get changed files and generate links\n id: changed-pages\n env:\n BASE_URL: https://docs.sleap.ai/pr/${{ github.event.number }}/\n run: |\n # Get list of changed files in this PR (docs-relevant paths only)\n CHANGED_FILES=$(git diff --name-only origin/${{ github.event.pull_request.base.ref }}...HEAD -- \\\n 'docs/**' 'sleap/**' 'mkdocs.yml' '.github/workflows/pr-preview.yml' \\\n 2>/dev/null || echo \"\")\n\n # Convert to space-separated args\n FILES_ARGS=$(echo \"$CHANGED_FILES\" | tr '\\n' ' ')\n\n # Run the script to generate markdown links\n RESULT=$(python scripts/get_changed_docs_urls.py \"$BASE_URL\" $FILES_ARGS)\n\n # Extract markdown from JSON result (handle multiline)\n MARKDOWN=$(echo \"$RESULT\" | python -c \"import sys, json; print(json.load(sys.stdin)['markdown'])\")\n\n # Use heredoc for multiline output\n echo \"links<> $GITHUB_OUTPUT\n echo \"$MARKDOWN\" >> $GITHUB_OUTPUT\n echo \"EOF\" >> $GITHUB_OUTPUT\n\n - name: Deploy PR Preview\n uses: JamesIves/github-pages-deploy-action@v4\n with:\n branch: gh-pages\n folder: _site\n target-folder: pr/${{ github.event.number }}\n commit-message: \"Deploy PR #${{ github.event.number }} preview\"\n\n - name: Comment on PR\n uses: marocchino/sticky-pull-request-comment@v2\n with:\n header: docs-preview\n message: |\n ## Docs Preview\n\n | | |\n |---|---|\n | **Preview URL** | https://docs.sleap.ai/pr/${{ github.event.number }}/ |\n | **Commit** | `${{ github.event.pull_request.head.sha }}` |\n\n ${{ steps.changed-pages.outputs.links }}\n\n _This preview will be removed when the PR is closed._\n\n cleanup:\n name: Cleanup Preview\n if: github.event.action == 'closed'\n runs-on: ubuntu-latest\n steps:\n - name: Checkout gh-pages\n uses: actions/checkout@v4\n with:\n ref: gh-pages\n\n - name: Remove PR preview\n run: |\n if [ -d \"pr/${{ github.event.number }}\" ]; then\n rm -rf \"pr/${{ github.event.number }}\"\n git config user.name \"github-actions[bot]\"\n git config user.email \"github-actions[bot]@users.noreply.github.com\"\n git add -A\n git commit -m \"Remove PR #${{ github.event.number }} preview\" || echo \"Nothing to commit\"\n git push\n else\n echo \"Preview directory pr/${{ github.event.number }} does not exist\"\n fi\n\n - name: Comment on PR (cleanup)\n uses: marocchino/sticky-pull-request-comment@v2\n with:\n header: docs-preview\n message: |\n ## Docs Preview\n\n Preview has been removed.\n" }, { "path": ".gitignore", "content": "# Claude\n.claude/settings.local.json\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\nuv.lock\n.ruff_cache/\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\ndocs/api/\ndocs/_autosummary/\ndocs/api.rst\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n# Pycharm Files\n.idea/\n\n# Sublime files\n*.sublime-*\n\n# VSCode files\n.vscode/\n\n# OS generated files\n.DS_Store\n*.analysis.h5\n*.analysis.nix\ntests/data/slp_hdf5/minimal_instance.000_centered_pair_low_quality.analysis.*\n\n# Test artifacts in root\ntest*.avi\n\n# Output files\noutputs/\n\n# Temp/scratch files\nplan/\nscratch/\ntmp/\nopenspec/\n# Playwright MCP artifacts\n.playwright-mcp/\n" }, { "path": "CLAUDE.md", "content": "# SLEAP Development Guidelines for Claude\n\n## Package Management\n\n- **Always use `uv` for python commands and environment management.**\n- Never use `conda` or `pip`.\n- Do not call `python` directly, use `uv run python`.\n\n### Basic usage\nWe manage our dependencies in `pyproject.toml` and will only edit the versions or add dependencies very sparingly and with a lot of careful planning.\n\n```bash\n# Sync environment with dependencies (this removes packages installed with `uv pip install`)\nuv sync --extra nn\n\n# Run Python in the uv environment\nuv run python script.py\n\n# Install local packages (e.g., for sleap-io or sleap-nn development)\nuv pip install -e \"../sleap-io[all]\"\nuv pip install -e \"../sleap-nn[torch]\" --torch-backend=auto\n\n# Only when trying out new packages or pulling something in for an experiment\nuv pip install XYZ\n```\n\n### Reference for `uv`\n- When looking up `uv` command syntax, use `uv --help`, `uv sync --help`, etc.\n- For advanced usage, like resolution mechanics, refer to the docs in `scratch/repos/uv/docs`. If it doesn't exist, use `gh` to clone `astral-sh/uv` into that folder.\n\n## Testing\n\n```bash\n# Run all tests\nuv run pytest\n\n# Run specific test file\nuv run pytest tests/test_specific.py\n\n# Run with coverage\nuv run pytest --cov=sleap\n```\n\n## Linting\n\n```bash\n# Run ruff for linting\nuv run ruff check .\n\n# Run ruff with auto-fix\nuv run ruff check --fix .\n```\n\n## Project Structure\n\n- `sleap/` - Main SLEAP package\n- `tests/` - Test suite\n- `docs/` - Documentation\n- `scratch/` - Investigation and prototype files (not part of main package)\n\n## Related Projects\n\n- `../sleap-io/` - sleap-io library\n - data backend, data model, I/O backends, merging, labels project (.slp) manipulation, advanced workflows like filtering, rendering or embedding\n - If not available locally in `../sleap-io` (typical local dev setup), use `gh` to clone `talmolab/sleap-io` in `scratch/repos/sleap-io` to have a local copy to read for reference.\n- `../sleap-nn/` - sleap-nn neural network package\n - neural network backend, training, inference, evaluation metrics, tracking, model configuration, hyperparameters\n - If not available locally in `../sleap-nn` (typical local dev setup), use `gh` to clone `talmolab/sleap-nn` to `scratch/repos/sleap-nn` to have a local copy to read for reference.\n" }, { "path": "LICENSE", "content": "The Clear BSD License\n\nCopyright (c) [2019-2025] [Talmo Pereira and The Trustees of Princeton University]\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted (subject to the limitations in the disclaimer\nbelow) provided that the following conditions are met:\n\n * Redistributions of source code must retain the above copyright notice,\n this list of conditions and the following disclaimer.\n\n * Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in the\n documentation and/or other materials provided with the distribution.\n\n * Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from this\n software without specific prior written permission.\n\nNO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY\nTHIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND\nCONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\nLIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\nPARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\nCONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\nEXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\nPROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR\nBUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER\nIN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\nARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\nPOSSIBILITY OF SUCH DAMAGE." }, { "path": "MANIFEST.in", "content": "include README.md\ninclude LICENSE\ninclude pyproject.toml\n\nrecursive-exclude docs *\nrecursive-exclude tests *\nprune docs\nprune tests\n" }, { "path": "README.md", "content": "[![CI](https://github.com/talmolab/sleap/actions/workflows/ci.yml/badge.svg)](https://github.com/talmolab/sleap/actions/workflows/ci.yml)\n[![Coverage](https://codecov.io/gh/talmolab/sleap/branch/develop/graph/badge.svg?token=oBmTlGIQRn)](https://codecov.io/gh/talmolab/sleap)\n[![Documentation](https://img.shields.io/badge/Documentation-sleap.ai-lightgrey)](https://docs.sleap.ai)\n[![Downloads](https://static.pepy.tech/personalized-badge/sleap?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=PyPI%20Downloads)](https://pepy.tech/project/sleap)\n[![Stable version](https://img.shields.io/github/v/release/talmolab/sleap?label=stable)](https://github.com/talmolab/sleap/releases/)\n[![Latest version](https://img.shields.io/github/v/release/talmolab/sleap?include_prereleases&label=latest)](https://github.com/talmolab/sleap/releases/)\n\n# Social LEAP Estimates Animal Poses (SLEAP)\n\n![SLEAP Demo](https://raw.githubusercontent.com/talmolab/sleap/develop/docs/assets/images/sleap_movie.gif)\n\n**SLEAP** is an open-source deep-learning based framework for multi-animal pose tracking [(Pereira et al., Nature Methods, 2022)](https://www.nature.com/articles/s41592-022-01426-1). It can be used to track any type or number of animals and includes an advanced labeling/training GUI for active learning and proofreading.\n\n## Features\n\n* Easy, one-line installation with support for all OSes\n* Purpose-built GUI and human-in-the-loop workflow for rapidly labeling large datasets\n* Single- and multi-animal pose estimation with *top-down* and *bottom-up* training strategies\n* Customizable neural network architectures that deliver *accurate predictions* with *very few* labels\n* Fast training: 15 to 60 mins on a single GPU for a typical dataset\n* Fast inference: up to 600+ FPS for batch, <10ms latency for realtime\n* Support for remote training/inference workflow (for using SLEAP without GPUs)\n* Flexible developer API for building integrated apps and customization\n* Two independent backendsā€” [`sleap-nn`](https://nn.sleap.ai) and [`sleap-io`](https://io.sleap.ai) for training/inference pipelines & handling SLEAP files respectively\n\n## Get some SLEAP\n\nSLEAP is installed as a Python package. We strongly recommend using [uv](https://docs.astral.sh/uv/) to install SLEAP in its own environment.\n\nYou can find the latest version of SLEAP in the [Releases](https://github.com/talmolab/sleap/releases) page.\n\n### Quick install\n\n> **Python 3.14 is not yet supported**\n>\n> SLEAP currently supports **Python 3.11, 3.12, and 3.13**. \n> **Python 3.14 is not yet tested or supported.** \n> By default, `uv` will use your system-installed Python. \n> If you have Python 3.14 installed, you must specify the Python version (ā‰¤3.13) in the install command. \n>\n> For example:\n>\n> ```bash\n> uv tool install --python 3.13 \"sleap[nn]\" ...\n> ```\n> Replace `...` with the rest of your install command as needed.\n\n**`uv tool install` (any OS):**\n\nFirst, install [`uv`](https://github.com/astral-sh/uv) if you haven't already:\n\n```bash\n# macOS/Linux\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n\n# Windows\npowershell -c \"irm https://astral.sh/uv/install.ps1 | iex\"\n```\n\nThen install SLEAP:\n\n```bash\n# Windows/Linux CUDA 12.8\nuv tool install \"sleap[nn]\" --index https://download.pytorch.org/whl/cu128 --index https://pypi.org/simple\n\n# macOS / CPU-only\nuv tool install \"sleap[nn]\" --index https://download.pytorch.org/whl/cpu --index https://pypi.org/simple\n```\n\nRun the SLEAP GUI after installation:\n\n```bash\nsleap\n```\n\nSee the docs for [full installation instructions](https://docs.sleap.ai/latest/installation).\n\n## Learn to SLEAP\n\n- **Learn step-by-step:** [Tutorial](https://docs.sleap.ai/latest/tutorial/overview)\n- **Learn more advanced usage:** [Guides](https://docs.sleap.ai/latest/guides/guides-overview/) and [Notebooks](https://docs.sleap.ai/latest/notebooks/notebooks-overview/)\n- **Learn by watching:** [COSYNE 2024 Tutorial (Part 1)](https://youtu.be/R5PRhkhAve0), [COSYNE 2024 Tutorial (Part 2)](https://youtu.be/Z64v-vp-Jvo), [ABL:AOC 2023 Workshop](https://www.youtube.com/watch?v=BfW-HgeDfMI), and [MIT CBMM Tutorial](https://cbmm.mit.edu/video/decoding-animal-behavior-through-pose-tracking)\n- **Learn by reading:** [Paper (Pereira et al., Nature Methods, 2022)](https://www.nature.com/articles/s41592-022-01426-1) and [Review on behavioral quantification (Pereira et al., Nature Neuroscience, 2020)](https://rdcu.be/caH3H)\n- **Learn from others:** [Discussions on Github](https://github.com/talmolab/sleap/discussions)\n\n## References\n\nSLEAP is the successor to the single-animal pose estimation software [LEAP](https://github.com/talmo/leap) ([Pereira et al., Nature Methods, 2019](https://www.nature.com/articles/s41592-018-0234-5)).\n\nIf you use SLEAP in your research, please cite:\n\n> T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D'Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy. [Sleap: A deep learning system for multi-animal pose tracking](https://www.nature.com/articles/s41592-022-01426-1). *Nature Methods*, 19(4), 2022\n\n**BibTeX:**\n\n```bibtex\n@ARTICLE{Pereira2022sleap,\n title={SLEAP: A deep learning system for multi-animal pose tracking},\n author={Pereira, Talmo D and \n Tabris, Nathaniel and\n Matsliah, Arie and\n Turner, David M and\n Li, Junyu and\n Ravindranath, Shruthi and\n Papadoyannis, Eleni S and\n Normand, Edna and\n Deutsch, David S and\n Wang, Z. Yan and\n McKenzie-Smith, Grace C and\n Mitelut, Catalin C and\n Castro, Marielisa Diez and\n D'Uva, John and\n Kislin, Mikhail and\n Sanes, Dan H and\n Kocher, Sarah D and\n Samuel S-H and\n Falkner, Annegret L and\n Shaevitz, Joshua W and\n Murthy, Mala},\n journal={Nature Methods},\n volume={19},\n number={4},\n year={2022},\n publisher={Nature Publishing Group}\n }\n}\n```\n\n\n**Technical issue with the software?**\n\n1. Check the [Help page](https://docs.sleap.ai/latest/help).\n2. Ask the community via [discussions on Github](https://github.com/talmolab/sleap/discussions).\n3. Search the [issues on GitHub](https://github.com/talmolab/sleap/issues) or open a new one.\n\n**General inquiries?**\nReach out to [talmo@salk.edu](mailto:talmo@salk.edu).\n\n## Acknowledgments\n\nSLEAP was created in the [Murthy](https://murthylab.princeton.edu) and [Shaevitz](https://shaevitzlab.princeton.edu) labs at the [Princeton Neuroscience Institute](https://pni.princeton.edu) at Princeton University.\n\nSLEAP is currently being developed and maintained in the [Talmo Lab](https://talmolab.org) at the [Salk Institute for Biological Studies](https://salk.edu).\n\n### Maintainers\n\n* **Divya Murali** ([@gitttt-1234](https://github.com/gitttt-1234)), Salk Institute for Biological Studies\n* **Amick Licup** ([@alicup29](https://github.com/alicup29)), Salk Institute for Biological Studies\n* **Elizabeth Berrigan** ([@eberrigan](https://github.com/eberrigan)), Salk Institute for Biological Studies\n* **Andrew Park** ([@7174Andy](https://github.com/7174Andy)), Salk Institute for Biological Studies\n* **Tom Han** ([@tom21100227](https://github.com/tom21100227)), Salk Institute for Biological Studies\n* **Talmo Pereira** ([@talmo](https://github.com/talmo)), Salk Institute for Biological Studies\n\n### Contributors\nSLEAP would not be possible without major contributions from many folks, including:\n\n* **Liezl Maree**, Salk Institute for Biological Studies\n* **Arlo Sheridan**, Salk Institute for Biological Studies\n* **Arie Matsliah**, Princeton Neuroscience Institute, Princeton University\n* **Nat Tabris**, Princeton Neuroscience Institute, Princeton University\n* **David Turner**, Research Computing and Princeton Neuroscience Institute, Princeton University\n* **Joshua Shaevitz**, Physics and Lewis-Sigler Institute, Princeton University\n* **Mala Murthy**, Princeton Neuroscience Institute, Princeton University\n\n### Funding\n\nThis work was made possible through our funding sources, including:\n\n* NIH BRAIN Initiative R01 NS104899\n* Princeton Innovation Accelerator Fund\n* NIH BRAIN Initiative RF1 MH132653\n\n## License\n\n\n\nSLEAP is released under a [BSD 3-Clause Clear License](LICENSE).\n\n## Links\n\n* [Documentation Homepage](https://docs.sleap.ai)\n* [Overview](https://docs.sleap.ai/latest/overview)\n* [Installation](https://docs.sleap.ai/latest/installation)\n* [Tutorial](https://docs.sleap.ai/latest/tutorial/overview/)\n* [Guides](https://docs.sleap.ai/latest/guides/guides-overview/)\n* [Notebooks](https://docs.sleap.ai/latest/notebooks/notebooks-overview/)\n* [Developer API](https://docs.sleap.ai/latest/api)\n* [Help](https://docs.sleap.ai/latest/help)\n" }, { "path": "codecov.yml", "content": "coverage:\n ignore: \n - \"tests\"\n status:\n project: # Measures overall project coverage.\n gui:\n target: auto\n threshold: 0.5% # Some leeway with GUI code.\n paths:\n - \"sleap/gui/\"\n informational: true # \"For now\"\n non-gui:\n target: auto\n threshold: 0.05% # Less leeway with backend code.\n paths:\n - \"sleap/\"\n - \"!sleap/gui/\"\n informational: true # \"For now\"\n patch: # Only measures lines adjusted in the pull request.\n gui:\n target: auto\n paths:\n - \"sleap/gui/\"\n informational: true # GUI patch coverage for stats only.\n non-gui:\n target: 100% # All backend code should be tested...\n threshold: 20% # ... but some tests are infeasable.\n paths:\n - \"sleap/\"\n - \"!sleap/gui/\"\n informational: true # \"For now\"\n" }, { "path": "docs/assets/stylesheets/extra.css", "content": "/* Custom fonts */\n:root {\n --md-text-font: \"PT Sans\", -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, sans-serif;\n --md-code-font: \"Fira Mono\", \"Fira Code\", monospace;\n}\n\n.md-typeset .admonition,\n.md-typeset details {\n font-size: inherit; /* Ensures it matches the surrounding text */\n}\n\n/* Markdown source link button - positioned in top-right like Material action buttons */\n.md-source-file {\n float: right;\n margin: 0 0 0.5em 0.5em;\n}\n\n.md-source-file a {\n display: inline-flex;\n align-items: center;\n justify-content: center;\n width: 2em;\n height: 2em;\n border-radius: 0.2em;\n color: var(--md-default-fg-color--light);\n transition: color 0.25s, background-color 0.25s;\n}\n\n.md-source-file a:hover {\n color: var(--md-accent-fg-color);\n background-color: var(--md-accent-fg-color--transparent);\n}\n\n.md-source-file svg {\n width: 1.2em;\n height: 1.2em;\n fill: currentColor;\n}\n\n/* Hero section */\n.hero {\n text-align: center;\n padding: 0.5rem 0;\n margin-bottom: 0.5rem;\n}\n\n.hero img {\n max-width: 600px;\n width: 100%;\n border-radius: 12px;\n box-shadow: 0 20px 40px rgba(0, 0, 0, 0.15), 0 8px 16px rgba(0, 0, 0, 0.1);\n}\n\n/* Grid cards - modern shadow style */\n.grid.cards {\n display: grid;\n grid-template-columns: repeat(auto-fit, minmax(15rem, 1fr));\n gap: 1rem;\n margin: 1.5rem 0;\n}\n\n.grid.cards > ul {\n display: contents;\n list-style: none;\n margin: 0;\n padding: 0;\n}\n\n.grid.cards > ul > li {\n background: var(--md-default-bg-color);\n border: none;\n border-radius: 12px;\n padding: 1.25rem;\n box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08), 0 2px 4px rgba(0, 0, 0, 0.04);\n transition: transform 0.2s ease, box-shadow 0.2s ease;\n}\n\n.grid.cards > ul > li:hover {\n transform: translateY(-4px);\n box-shadow: 0 12px 24px rgba(0, 0, 0, 0.12), 0 4px 8px rgba(0, 0, 0, 0.08);\n}\n\n.grid.cards > ul > li > hr {\n margin: 0.75rem 0;\n border: none;\n border-top: 1px solid var(--md-default-fg-color--lightest);\n}\n\n/* Card titles */\n.grid.cards > ul > li > p:first-child {\n font-size: 1.1rem;\n font-weight: 600;\n margin-bottom: 0.5rem;\n}\n\n/* Better code blocks */\n.md-typeset pre {\n border-radius: 8px;\n box-shadow: 0 2px 8px rgba(0, 0, 0, 0.06);\n}\n\n/* Buttons/links styling */\n.md-typeset a.md-button {\n border-radius: 8px;\n font-weight: 500;\n box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);\n transition: transform 0.15s ease, box-shadow 0.15s ease;\n}\n\n.md-typeset a.md-button:hover {\n transform: translateY(-2px);\n box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15);\n}\n\n/* Section spacing */\n.md-typeset h2 {\n margin-top: 2rem;\n}\n\n/* Feature list styling */\n.md-typeset ul li {\n margin-bottom: 0.4rem;\n}\n\n/* Dark mode adjustments */\n[data-md-color-scheme=\"slate\"] .grid.cards > ul > li {\n box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3), 0 2px 4px rgba(0, 0, 0, 0.2);\n}\n\n[data-md-color-scheme=\"slate\"] .grid.cards > ul > li:hover {\n box-shadow: 0 12px 24px rgba(0, 0, 0, 0.4), 0 4px 8px rgba(0, 0, 0, 0.3);\n}\n\n[data-md-color-scheme=\"slate\"] .hero img {\n box-shadow: 0 20px 40px rgba(0, 0, 0, 0.4), 0 8px 16px rgba(0, 0, 0, 0.3);\n}\n\n/* Plain toggles - no colored box */\n.md-typeset details.plain {\n border: none !important;\n box-shadow: none !important;\n background: transparent !important;\n margin: 0.75em 0;\n padding: 0;\n}\n\n.md-typeset details.plain > summary {\n background: transparent !important;\n border: none !important;\n border-bottom: 1px solid var(--md-default-fg-color--lightest) !important;\n padding: 0.6em 0 0.6em 1.2em !important;\n margin: 0;\n font-weight: 500;\n cursor: pointer;\n min-height: 0;\n position: relative;\n display: block;\n list-style: none;\n}\n\n.md-typeset details.plain > summary::-webkit-details-marker {\n display: none;\n}\n\n/* Custom arrow */\n.md-typeset details.plain > summary::before {\n content: \"ā–ø\" !important;\n position: absolute !important;\n left: 0 !important;\n top: 0.6em !important;\n width: auto !important;\n height: auto !important;\n background: none !important;\n transform: none !important;\n mask: none !important;\n -webkit-mask: none !important;\n font-size: 0.9em;\n color: var(--md-default-fg-color--light);\n transition: transform 0.15s ease;\n}\n\n.md-typeset details.plain[open] > summary::before {\n content: \"ā–¾\" !important;\n}\n\n.md-typeset details.plain > summary::after {\n display: none !important;\n}\n\n.md-typeset details.plain > *:not(summary) {\n margin-left: 0;\n padding: 0.5em 0;\n}\n\n/* Changelog - prevent long lines from overflowing */\n.md-content pre {\n overflow-x: auto;\n}\n\n.md-content code {\n word-break: break-word;\n}\n\n/* Colored phase icons */\n.icon-blue { color: #2196F3; }\n.icon-green { color: #4CAF50; }\n.icon-orange { color: #FF9800; }\n.icon-purple { color: #9C27B0; }\n.icon-red { color: #F44336; }\n.icon-teal { color: #009688; }\n" }, { "path": "docs/code-of-conduct.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment include:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at [talmo@salk.edu](mailto:talmo@salk.edu). All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [https://contributor-covenant.org/version/1/4][version]\n\n[homepage]: https://contributor-covenant.org\n[version]: https://contributor-covenant.org/version/1/4/" }, { "path": "docs/contribute.md", "content": "# Contributing to SLEAP\n\nAs our community grows it is important to adhere to a set of contribution guidelines. These guidelines may change as needed. Please feel free to propose changes to source code in a pull request! \n\n\n## Code of Conduct\n\nEveryone contributing to SLEAP is governed by the [SLEAP Code of Conduct](code-of-conduct.md). As such, all are expected to abide by this code. Please report any unacceptable behavior to `talmo@salk.edu`.\n\n## Contributing\n\nGithub has made it easy to separate issues from discussions. Generally speaking, if something warrants a pull request (e.g. bug fixes, TODOs) it should be submitted as an issue. Conversely, general questions about usage and improvement ideas should go into discussions. These both act as staging areas before adding the items to the SLEAP Roadmap. Once added to the Roadmap, items can be given priority, status, assignee(s), etc. \n\n### Issues\n\n* Check [open/closed issues](https://github.com/talmolab/sleap/issues), [ideas](https://github.com/talmolab/sleap/discussions/categories/ideas), and [the help page](https://github.com/talmolab/sleap/discussions/categories/help) to make sure issue doesn't already exist / has been solved.\n* Create new issue using the [issue template](https://github.com/talmolab/sleap/blob/arlo/contributing_guide/.github/ISSUE_TEMPLATE/bug_report.md).\n\n### Discussions\n\n* This is a place to go to ask questions and propose new ideas.\n* 3 categories: Help, Ideas, General\n * **Help** - Having trouble with software, user experience issue, etc.\n * **Ideas** - Enhancements, things that would make the user experience nicer but not necessarily a problem with the code.\n * **General** - If it doesn't fall into help/ideas it goes here as long as it isn't bug fix (issue).\n\n### Pull Requests\n\n1. Install from source using the [Development Setup](installation.md#development-setup) instructions.\n2. Create a fork from the `develop` branch.\n * Either work on the `develop` branch or create a new branch (recommended if tackling multiple issues at a time).\n * If creating a branch, use your name followed by a relevant keyword for your changes, eg: `git checkout -b john/some_issue`\n3. Make some changes/additions to the source code that tackle the issue(s).\n4. Write [tests](https://github.com/talmolab/sleap/tree/develop/tests).\n * Can either write tests before creating a draft PR, or submit draft PR (to get code coverage statistics via codecov) and then write tests to narrow down error prone lines.\n * The test(s) should go into relevant subtest folders to the proposed change(s).\n * Test(s) should aim to hit every point in the proposed change(s) - cover edge cases to best of your ability.\n * Try to hit code coverage points.\n5. Add files, commit, and push to origin.\n6. Create a draft PR on [Github](https://github.com/talmolab/sleap/pulls) (follow instructions in template).\n * Make sure the tests pass and code coverage is good.\n * If either the tests or code coverage fail, repeat steps 3-5.\n7. Once the draft PR looks good, convert to a finalized PR (hit the `ready for review` button).\n * IMPORTANT: Only convert to a finalized PR when you believe your changes are ready to be merged.\n * Optionally assign a reviewer on the right of the screen - otherwise a member of the SLEAP developer team will self-assign themselves.\n8. If the reviewer requests changes, repeat steps 3-5 and `Re-request review`.\n9. Once the reviewer signs off they will squash + merge the PR into the `develop` branch.\n * New feautures will be available on the `main` branch when a new release of SLEAP is released.\n\n## Style Guides\n\n* **Lint** - [Black](https://black.readthedocs.io/en/stable/) version 21.6b0 (see [dev_requirements](https://github.com/talmolab/sleap/blob/develop/dev_requirements.txt) for any changes).\n* **Code** - Generally follow [PEP8](https://peps.python.org/pep-0008/). Type hinting is encouraged.\n* **Documentation** - Use [Google-style comments and docstrings](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) to document code.\n\n#### Thank you for contributing to SLEAP!" }, { "path": "docs/guides/creating-a-custom-training-profile.md", "content": "*Case: You've created a project with training data and you want to train models with custom hyperparameters.*\n\nHyperparameters include the model architecture, learning rate, and data augmentation settings. While model **parameters** are learned from your data during training, **hyperparameters** are not learned from your dataā€”they have to be set before training since they control the training process.\n\nThis guide will explain how to create a custom training profile but doesn't cover how to decide what the hyperparameters should be. For more information about the hyperparameters, see our guide to [Configuring models](https://nn.sleap.ai/latest/reference/models/).\n\nTraining profiles are YAML files. The YAML format is fairly easy to read (and edit) with a text-editor and you can use the default \"baseline\" profiles as a starting point for creating your own training profiles. For example, take a look at the [baseline bottom-up profile](https://github.com/talmolab/sleap/blob/develop/sleap/training_profiles/baseline_medium_rf.bottomup.yaml) or our [other baseline profiles](https://github.com/talmolab/sleap/blob/develop/sleap/training_profiles).\n\nBut if this sounds intimidating, you don't have to edit the YAML file by hand! You can use the same GUI that's used for training on a local machine to export custom training profiles.\n\nIf it isn't open already, run SLEAP and open the SLEAP project with your training dataset. Then select the \"**Run Training...**\" command in the \"**Predict**\" menu. You'll see the training GUI which lets you configure the pipeline type and the hyperparameters for each model:\n\n![Training Dialog](../assets/images/training_dialog.png)\n\nFirst pick the desired pipeline (i.e., top-down, bottom-up, or single animal). For each model in the pipeline, you'll then see a \"**Model Configuration**\" tabā€”in the image above with the top-down pipeline, there's one tab for configuring the centroid model and one for the centered instance model. Other pipelines will only have one model to configure.\n\nYou can click on each model configuration tab to configure the hyperparameters for training that model:\n\n![Training Model Dialog](../assets/images/training-model-dialog.png)\n\nFor advice about what you might want to customize with this dialog, see our guide to [Configuring models](https://nn.sleap.ai/latest/reference/models/).\n\nOnce you've configured each of your models, click the \"**Save configuration files...**\" button at the bottom of the dialog. You'll be prompted for where to save the files. It's a good idea to create a new folder which will contain the files since there will be multiple files exported.\n\nWherever you selected to save your files, you'll now have a custom training profile(s) with the settings you selected in the dialog. The filename of the training profile(s) will be:\n\n- `bottomup.yaml` for a bottom-up pipeline,\n- `centroid.yaml` and `centered_instance.yaml` for a top-down pipeline, and\n- `single_instance.yaml` for a single animal pipeline.\n\n(There will also be a `train-script.sh` file with the command-line command you could use to train your dataset using these training profiles, and possibly an `inference-script.sh` file if you selected frames for inference after training.)\n\nIf you're running training on a remote machine (including Colab), export your training job package into the remote machine. Then call:\n\n\n```bash\nsleap-nn train --config /path/to/profile.yaml \"data_config.train_labels_path=[path/to/dataset.pkg.slp]\"\n```\n\nfor each model you want to train (where `path/to/custom/profile.yaml` should be replaced with the path to your custom training profile and `path/to/dataset.pkg.slp` replaced with the path to your training job package). See our guide to [remote training](running-sleap-remotely.md) for more details.\n\n!!! note\n If you exported the training package as a ZIP file, it contains both the `.pkg.slp` and `.yaml` files necessary to train with the configuration you selected in the GUI. Before running the [`sleap-nn train`](https://nn.sleap.ai/latest/training/#using-cli) command, make sure to unzip this file:\n\n === \"macOS/Linux\"\n\n ```bash\n unzip training_job.zip -d training_job\n ```\n\n === \"Windows (PowerShell)\"\n\n ```powershell\n Expand-Archive -Path training_job.zip -DestinationPath training_job\n ```\n\n === \"Windows (Command Prompt)\"\n\n ```cmd\n tar -xf training_job.zip -C training_job\n ```\n\n### Training Hardware support\n\nSLEAP supports training on:\n\n- **NVIDIA GPUs**: Fully supported and tested.\n- **Apple Silicon Macs**: Supported and tested.\n\n**Unsupported configurations:**\n\n- AMD GPUs and older Macs (pre-M1) may fail during training.\n- Other GPU architectures or unsupported hardware configurations may lead to memory or allocation errors.\n\nGPU detection is automatic when you install SLEAP. Run `sleap doctor` to verify your GPU is detected. If you need to manually specify a backend, see the [Pre-release Versions](../installation.md#pre-release-versions) section." }, { "path": "docs/guides/guides-overview.md", "content": "# Overview\n**Here's an overview of the guides:**\n\n!!! warning \"Documentation for New SLEAP Versions\"\n This documentation is for the **latest version of SLEAP**. \n If you are using **SLEAP version 1.4.1 or earlier**, please visit the [legacy documentation](https://legacy.sleap.ai).\n\n!!! info \"Major Changes in SLEAP 1.5+\"\n Want to learn about the major changes and updates in the latest release?\n See [Migrating to SLEAP 1.5+](migrating-to-sleap-1-5.md) for a summary of what's new and how to update your workflows.\n\n **New in v1.6:** [Label Quality Control](label-quality-control.md) for automated detection of labeling errors.\n\n[Importing predictions for labeling](importing-predictions-for-labeling.md) when you have predictions that arenā€™t in the same project as your original training data and you want to correct some of the predictions and use these corrections to train a better model.\n\n[Tracking and proofreading](tracking-and-proofreading.md) provides tips and tools you can use to speed up proofreading when you're happy enough with the frame-by-frame predictions but you need to correct the identities tracked across frames.\n\n[Label Quality Control](label-quality-control.md) for detecting and fixing labeling errors using automated quality checks.\n\n[Instance Size Distribution](instance-size-distribution.md) for determining the optimal crop size for top-down models.\n\n[Run training and inference on Colab](run-training-and-inference-on-colab.md) when you have a project with labeled training data and youā€™d like to run training or inference in a Colab notebook.\n\n[Creating a custom training profile](creating-a-custom-training-profile.md) for creating custom training profiles (i.e., non-default model hyperparameters) from the GUI.\n\n[Running SLEAP remotely](running-sleap-remotely.md) when you have a project with training data and you want to train on a different machine using a command-line interface.\n\n!!! note \"Bonsai Integration\"\n **Bonsai is not natively supported with the new Torch backend in SLEAP.** \n If you want to use Bonsai with legacy SLEAP models, please refer to the [legacy Bonsai guide](https://legacy.sleap.ai/guides/bonsai.html).\n\n" }, { "path": "docs/guides/importing-predictions-for-labeling.md", "content": "*Case: You have predictions that aren't in the same project as your original training data and you want to correct some of the predictions and use these corrections to train a better model.*\n\nAll of your training data must be in a single SLEAP project file (or labels package), so if you have data in multiple files, you'll need to merge them before you can train on the entire set of data.\n\nWhen you run inference from the GUI, the predictions will be added to the same project as your training data (they'll also be saved in a separate file). When you run inference from the command-line, they'll only be in a separate file.\n\nIf you open a separate predictions file, make corrections there and train a new model from that file, then new models will be trained from scratch using only those corrections. The new models will not be trained on any of the original data that was used to train the previous modelsā€”i.e., the models used to generate these predictions. Usually you'll want to include both the original data and the new corrections.\n\n**Note** that uncorrected predictions will never be used for training. Only predictions which you've \"converted\" into an editable instance will be used for training. To convert a predicted instance into an editable instance, you can **double-click** on the predicted instance or use the \"**Add Instance**\" command in the \"Labels\" menu (there's also a keyboard shortcut). As you might guess, once you have an editable instance you can move nodes and toggle their \"visibility\" (see the [tutorial](../tutorial/overview.md) if you're not familiar with how to do this). When you've created an editable instance from a predicted instance, the predicted instance will no longer be shown, although it will re-appear if you delete the editable instance.\n\nLet's suppose we have a project file and a predictions file with corrections, and we'd like to merge the corrections into the original project file.\n\nIf you want to merge *only* the corrections, then you should first make a copy of the predictions file. You can either just copy the file itself, or make a copy from the GUI using \"**Save As..**\" in the \"File\" menu. Open the copy of the file in the GUI and use the \"**Delete All Predictions...**\" command in the \"Predictions\" menu to remove all of the predicted instances from the file. Save and you'll be left with a file which just contains your corrections.\n\nOpen the original project file (or whatever file you want to merge **into**). Then, use the \"**Merge Data From...**\" command in the \"File\" menu. You'll need to select the file **from which** you are getting the data to mergeā€”this would be the file with your corrected predictions.\n\nYou'll then see a window with information about the merge:\n\n![clean-merge](../assets/images/clean-merge.jpg)\n\nIf there are no merge conflicts, then you can click \"**Finish Merge**. If the two files contain conflictsā€”frames from the same video which both have editable instances or both have predicted instancesā€”then you'll need to decide how to resolve the conflicts. You can choose to use the \"base\" version (i.e., the original project file **into which** you are merging), the \"new\" version (i.e., from the predictions file with the data which you're adding to the original project), or neither. Whichever you choose, you'll also get all of the frames which can be merged without conflicts.\n\nAfter merging you should save (or save a copy of the project with the \"**Save As...**\" command). Once you have a single project file which contains both your old and new training data, you can train new models." }, { "path": "docs/guides/instance-size-distribution.md", "content": "# Instance Size Distribution\n\n*Case: You want to determine the optimal crop size for top-down models.*\n\nWhen using top-down pose estimation pipelines, SLEAP crops around each detected instance before estimating pose. Choosing the right crop size is important:\n\n- **Too small**: Parts of the animal may be cut off\n- **Too large**: Wastes computation and may include other animals\n\nThe Instance Size Distribution widget helps you analyze your labeled data to choose an appropriate crop size.\n\n## Accessing the Widget\n\nFrom the GUI: **Analyze** ā†’ **Instance Size Distribution...**\n\nThis opens a dialog window showing the size distribution of all user-labeled instances in your project.\n\n![Instance Size Distribution dialog before loading data](../assets/images/instancesd_preview.png)\n\n![Instance Size Distribution dialog with data loaded](../assets/images/instancesd_example.png)\n\n## Recompute\n\nClick the **Recompute** button to recalculate sizes from your current labels. This is useful if you've added or modified labels since opening the dialog.\n\nThe widget only analyzes user-labeled instances (not predicted instances) to give you accurate statistics based on your ground truth annotations.\n\n## Rotation Augmentation\n\nThe **Rotation Augmentation** controls let you preview how rotation augmentation during training will affect the required crop size.\n\nWhen you rotate an instance, its bounding box grows to accommodate the rotated skeleton. This setting helps you understand what crop size you'll need if using rotation augmentation.\n\n**Preset options:**\n\n- **Off**: No rotation applied. Shows the raw bounding box sizes.\n- **+/-15**: Simulates +/-15 degree rotation augmentation (common for videos with limited rotation).\n- **+/-180**: Simulates full rotation augmentation (common for overhead/top-down views).\n- **Custom**: Enables the custom angle spinner to set any angle from 0-180 degrees.\n\nWhen using rotation augmentation in training, set this to match your training configuration to see the effective crop sizes you'll need.\n\n## Visualization\n\nThe center plot shows the distribution of instance sizes. You can switch between two views:\n\n### Scatter (clickable)\n\nThe default view shows each instance as a point:\n\n- **X-axis**: Size in pixels (the larger dimension of the bounding box)\n- **Y-axis**: Instance index (ordered by position in the labels file)\n- **Point colors**: Colored by relative size (blue = near median, red = larger than median)\n- **Vertical lines**: Show statistics:\n - **Green dashed**: Median size\n - **Blue dotted**: Mean size\n - **Red solid**: Maximum size\n\n**Clicking a point** selects that instance and navigates to it in the main video player, making it easy to inspect outliers.\n\n### Histogram\n\nAn alternative view showing the count distribution:\n\n- **X-axis**: Size in pixels\n- **Y-axis**: Count (number of instances in each bin)\n- **Vertical lines**: Same statistics as scatter view (median, mean, max)\n\n**Histogram options** (enabled when Histogram is selected):\n\n- **Bins**: Number of histogram bins (5-100, default 30)\n- **X-min**: Minimum x-axis value, or \"Auto\" for automatic range\n- **X-max**: Maximum x-axis value, or \"Auto\" for automatic range\n\nUse the range controls to zoom in on a specific size range if needed.\n\n## Selected Instance\n\nWhen you click a point in the scatter plot, the **Selected Instance** panel shows:\n\n- **Frame**: The frame index where the instance appears\n- **Instance**: The instance index within that frame\n- **Video**: The video index (for multi-video projects)\n- **Raw Size**: The bounding box size without rotation (with width Ɨ height dimensions)\n- **Rotated Size**: The effective size with the current rotation setting applied\n\nThis helps you understand both the original annotation size and how it will grow with rotation augmentation.\n\n## Statistics\n\nThe **Statistics** panel shows summary statistics for all instances:\n\n- **Count**: Total number of user-labeled instances\n- **Range**: Minimum and maximum sizes in pixels\n- **Mean +/- Std**: Average size with standard deviation\n- **Median**: The middle value (50th percentile)\n- **90th/95th/99th**: Percentile values for choosing crop sizes\n- **Outliers (>2 sigma)**: Count of instances more than 2 standard deviations above the mean\n\nThese statistics update when you change the rotation setting.\n\n## Choosing a Crop Size\n\nUse the statistics to choose an appropriate crop size:\n\n1. **95th percentile** is a common choiceā€”it covers 95% of instances while excluding extreme outliers\n2. **Add 10-20% padding** for animals near frame edges or to account for prediction uncertainty\n3. **Consider rotation**: If using rotation augmentation, set the rotation preview to match and use those statistics\n\nFor example, if your 95th percentile with +/-180 rotation is 200px, a crop size of 220-240px would be reasonable.\n\n## Tips\n\n- **Consistent animal sizes**: If your animals are similar sizes, the distribution will be tight and crop size selection is straightforward\n- **Variable sizes**: If sizes vary significantly (e.g., adults and juveniles), consider using a larger crop size or filtering your training data\n- **Multiple videos**: The widget analyzes all videos together. If recording conditions vary, click through outliers to see if they come from specific videos\n- **Inspect outliers**: Click on the largest points in the scatter plot to check if they represent real variation or labeling errors\n\n## Using with Crop Size Visualization\n\nAfter choosing a crop size, you can visualize it in the main view:\n\n1. Open the training dialog (**Predict** ā†’ **Run Training...**)\n2. The crop size overlay will show the crop region on your video\n3. Scrub through frames to verify the crop captures the full animal\n\n## Programmatic Access\n\nFor scripting or automation, you can compute instance sizes programmatically:\n\n```python\nimport sleap_io as sio\nfrom sleap.gui.learning.size import compute_instance_sizes\nimport numpy as np\n\n# Load labels\nlabels = sio.load_file(\"labels.slp\")\n\n# Compute sizes for user-labeled instances\nsizes = compute_instance_sizes(labels, user_instances_only=True)\n\n# Get raw sizes\nraw_sizes = np.array([s.raw_size for s in sizes])\nprint(f\"95th percentile: {np.percentile(raw_sizes, 95):.0f}px\")\n\n# With rotation augmentation (+/-180 degrees)\nrotated_sizes = np.array([s.get_rotated_size(180) for s in sizes])\nprint(f\"95th percentile with rotation: {np.percentile(rotated_sizes, 95):.0f}px\")\n```\n\nEach `InstanceSizeInfo` object contains:\n- `video_idx`, `frame_idx`, `instance_idx`: Location identifiers\n- `raw_width`, `raw_height`: Bounding box dimensions\n- `raw_size`: Maximum of width and height\n- `get_rotated_size(angle)`: Size needed with rotation augmentation\n\n## Related\n\n- [Configuring Models](https://nn.sleap.ai/latest/reference/models/) - Full details on crop size and other model parameters\n- [Creating a Custom Training Profile](creating-a-custom-training-profile.md) - How to save your crop size settings\n" }, { "path": "docs/guides/label-quality-control.md", "content": "# Label Quality Control\n\n*Case: You want to check your labels for errors before training or after proofreading.*\n\nSLEAP 1.6 introduces a Label Quality Control (QC) module that automatically detects common labeling errors using Gaussian Mixture Model (GMM) based anomaly detection.\n\n## Accessing Label QC\n\nFrom the GUI: **Analyze** ā†’ **Label QC...**\n\nThis opens a dockable panel that can be tabbed with other panels (Videos, Skeleton, etc.) or undocked as a floating window.\n\n![Label QC panel before running analysis](../assets/images/labelqc_preview.png)\n\n![Label QC panel after running analysis](../assets/images/labelqc_example.png)\n\n## Running Analysis\n\nClick the **Run Analysis** button to analyze all labeled instances in your project. The analysis:\n\n1. Extracts features from each instance (edge lengths, joint angles, node spacing, etc.)\n2. Fits a statistical model to learn what \"normal\" poses look like\n3. Scores each instance based on how much it deviates from the norm\n4. Displays results in the visualization tabs and table\n\nA progress bar shows the analysis status. You can click **Cancel** to stop a running analysis.\n\n## Sensitivity Slider\n\nThe **Sensitivity** slider controls the threshold for flagging instances:\n\n- **More** (left): Lower threshold flags more instances (higher sensitivity)\n- **Fewer** (right): Higher threshold flags fewer instances (lower sensitivity)\n- The current threshold value is displayed on the right (e.g., \"0.70\")\n\nYou can also click directly on the Score Distribution histogram to set the threshold visually.\n\n## Visualization Tabs\n\nThree tabs provide different views of the analysis results:\n\n### Score Distribution\n\nA histogram showing the distribution of anomaly scores across all instances:\n\n- **X-axis**: Anomaly Score (0.0 to 1.0)\n- **Y-axis**: Count (number of instances)\n- **Gray bars**: Instances below the threshold (considered \"normal\")\n- **Red bars**: Instances at or above the threshold (flagged as potential errors)\n- **Blue dashed line**: Current threshold position\n- **Red annotation**: Count and percentage of flagged instances\n\nA typical distribution shows most instances clustered at low scores (normal), with a tail extending toward higher scores (potential errors).\n\n### Issue Breakdown\n\nA horizontal bar chart showing the count of each issue type among flagged instances. Common issue categories include:\n\n- **Unusual Visibility**: Unusual pattern of visible/invisible nodes\n- **Unusual Edge Length**: A skeleton edge is much longer or shorter than typical\n- **Unusual Node Spacing**: Nodes are unusually close together or far apart\n- **Unusual Scale**: Instance is much larger or smaller than typical\n- **Unusual Joint Angle**: A joint is bent at an unusual angle\n- **High Hull Area**: The convex hull of the skeleton is unusually large\n- **Likely L/R Swap**: Left and right limbs may be swapped\n\nThis view helps identify systematic labeling issues in your dataset.\n\n### Features\n\nBox plots comparing feature distributions between normal (gray) and flagged (red) instances. This tab shows the top discriminating featuresā€”the characteristics that most differentiate flagged instances from normal ones.\n\nFeatures shown may include:\n\n- **max angle / mean angle**: Joint angle statistics\n- **hull compact**: Compactness of the skeleton's convex hull\n- **max pairwise**: Maximum distance between any two nodes\n- **hull area / bbox area**: Size metrics for the skeleton\n\nThis view helps you understand *why* instances are being flagged.\n\n## Flagged Instances Table\n\nA sortable table listing all instances that exceed the current threshold:\n\n| Column | Description |\n|--------|-------------|\n| **Frame** | Frame index where the instance appears |\n| **Instance** | Instance index within that frame |\n| **Score** | Anomaly score (0.0ā€“1.0). Higher scores indicate more unusual instances. Displayed in red for scores ā‰„0.8, yellow for ā‰„0.6 |\n| **Confidence** | \"High\" (score ā‰„0.8), \"Medium\" (0.5ā€“0.8), or \"Low\" (<0.5) |\n| **Issue** | The primary issue type (most contributing feature) |\n\n**Interacting with the table:**\n\n- **Click** a row to navigate to that frame and select the instance\n- **Double-click** for the same navigation behavior\n- **Click column headers** to sort by that column\n- The table is sorted by Score (descending) by default\n\n## Selected Instance\n\nWhen you select a row in the table, the **Selected Instance** panel shows detailed information:\n\n- **Frame / Instance**: Location identifiers\n- **Score**: The anomaly score with confidence level\n- **Primary Issue**: The most likely type of error\n- **Top Features**: The 3 features contributing most to the high score, with their values\n\nThis helps you understand why a specific instance was flagged before reviewing it in the video.\n\n## Statistics\n\nThe **Statistics** panel shows a summary of the analysis:\n\n- **Flagged**: Count of flagged instances / total instances (percentage)\n- **By confidence**: Breakdown of flagged instances by confidence level (high, medium)\n- **Frame issues**: Number of frames with frame-level issues (possible duplicates or missing instances)\n- **Scores**: Statistical summary (mean, median, max) of all anomaly scores\n\n## Add to Suggestions\n\nClick **Add to Suggestions** to add all flagged frames to the Labeling Suggestions list. This creates a review queue so you can systematically work through potential errors.\n\nFrames already in the suggestions list are skipped to avoid duplicates.\n\n## Additional Controls\n\n- **Export CSV...**: Export all results (scores, features, issues) to a CSV file for external analysis\n- **Fit to Selection**: When checked, the video player automatically zooms to fit the selected instance\n- **Undock / Dock**: Toggle between docked (tabbed) and floating window modes\n\n## Programmatic Access\n\nThe QC module is available programmatically via `sleap.qc`:\n\n```python\nimport sleap_io as sio\nfrom sleap.qc import LabelQCDetector, QCConfig\n\n# Load labels\nlabels = sio.load_file(\"labels.slp\")\n\n# Create detector with default config\ndetector = LabelQCDetector()\n\n# Fit on labels (learns what \"normal\" looks like from your data)\ndetector.fit(labels)\n\n# Score all instances\nresults = detector.score(labels)\n\n# Get flagged instances above threshold (0.0-1.0, higher = more anomalous)\nflagged = results.get_flagged(threshold=0.7)\n\n# Inspect flagged instances\nfor flag in flagged:\n print(f\"Video {flag.video_idx}, Frame {flag.frame_idx}, Instance {flag.instance_idx}\")\n print(f\" Score: {flag.score:.2f}\")\n print(f\" Issue: {flag.top_issue}\")\n```\n\n### Configuration Options\n\n```python\nfrom sleap.qc import QCConfig\n\nconfig = QCConfig(\n instance_threshold=0.7, # Score threshold for flagging\n gmm_n_components=3, # Number of GMM components\n duplicate_iou_threshold=0.5, # IoU threshold for duplicate detection\n)\ndetector = LabelQCDetector(config=config)\n```\n\n### Available Classes\n\n- `LabelQCDetector`: Main detection interface\n- `QCConfig`: Configuration settings\n- `QCResults`: Container for scores, frame results, and feature contributions\n- `QCFlag`: Individual flagged instance with score and contributing features\n\n## Tips\n\n- Run QC after initial labeling but before training to catch errors early\n- Re-run after proofreading tracking results to verify corrections\n- Not all flagged items are actual errorsā€”use your judgment when reviewing\n- Lower the threshold if you want to be more thorough; raise it to focus on the most obvious issues\n- Use the Issue Breakdown tab to identify systematic problems in your labeling workflow\n- QC is most effective when you have consistent labeling practices across your dataset\n" }, { "path": "docs/guides/migrating-to-sleap-1-5.md", "content": "# Migrating to SLEAP 1.5\n\nSLEAP 1.5 represents a major milestone with significant architectural improvements, performance enhancements, and new installation methods. Here are the key changes:\n\n## Major Changes\n\n### UV-Based Installation\nSLEAP 1.5+ now uses [**uv**](https://docs.astral.sh/uv/) for installation, making it much faster than previous methods. Get up and running in seconds with our streamlined installation process.\n\n### PyTorch Backend\nNeural network backend switched from TensorFlow to PyTorch, providing:\n\n- **Much faster training and inference speeds**\n- **Modern deep learning capabilities**\n- **Improved developer experience**\n- **Multi-GPU training**\n\n### Standalone Libraries\nSLEAP GUI is now supported by two new packages for modular workflows:\n\n#### [SLEAP-IO](https://io.sleap.ai)\nI/O backend for handling labels, processing `.slp` files, and data manipulation. Essential for any SLEAP workflow and can be used independently for data processing tasks.\n\n#### [SLEAP-NN](https://nn.sleap.ai)\nPyTorch-based neural network backend for training and inference. Perfect for custom training pipelines, remote processing, and headless server deployments.\n\n## Torch Backend Changes\n\n### New Backbones\nSLEAP 1.5 introduces three powerful new backbone architectures (check [here](https://nn.sleap.ai/latest/models/#backbone-architectures) for more details):\n\n- **UNet** - Classic encoder-decoder architecture for precise pose estimation\n- **SwinT** - Swin Transformer for state-of-the-art performance\n- **ConvNeXt** - Modern convolutional architecture with improved efficiency\n\n### Legacy Support\nWe've maintained full backward compatibility:\n\n- **GUI Support**: SLEAP now uses a new YAML-based config file structure, but you can still upload and work with old SLEAP JSON files in the GUI. For details on converting legacy SLEAP 1.4 config/JSON files to the new YAML format, see our [conversion guide](https://nn.sleap.ai/latest/config/#converting-legacy-sleap-14-configjson-to-sleap-nn-yaml).\n- **Using TensorFlow Model Weights**: Continue to support running inference on SLEAP <1.4 TensorFlow model weights (UNet backbone only). Check [using legacy models](https://nn.sleap.ai/latest/inference/#legacy-sleap-model-support) for more details.\n\n\n## What's New in v1.6\n\nSLEAP 1.6 builds on the v1.5 foundation with additional features and improvements:\n\n### Unified CLI\nThe new `sleap` command provides a single entry point for all SLEAP functionality:\n\n```bash\nsleap # Launch the GUI\nsleap doctor # System diagnostics\nsleap train ... # Training (via sleap-nn)\nsleap track ... # Inference (via sleap-nn)\nsleap show labels.slp # View labels summary (via sleap-io)\n```\n\nSee [Command Line Interfaces](../reference/command-line-interfaces.md) for the full list of commands.\n\n### Label Quality Control\nNew **Analyze ā†’ Label QC...** menu for automated detection of labeling errors including:\n- Temporal jitter (unstable predictions across frames)\n- Visibility inconsistencies\n- Scale anomalies\n- Potential identity swaps\n\nSee [Label Quality Control](label-quality-control.md) for details.\n\n### Instance Size Distribution\nNew **Analyze ā†’ Instance Size Distribution...** widget helps determine optimal crop sizes for top-down models by analyzing the distribution of instance bounding box sizes in your labeled data.\n\nSee [Instance Size Distribution](instance-size-distribution.md) for details.\n\n### ONNX/TensorRT Export\nExport trained models to ONNX or TensorRT format for 3-6x faster inference:\n\n```bash\nsleap export-model models/my_model --format onnx\n```\n\n### Additional Improvements\n- **Real-time inference progress**: Live FPS and ETA display during inference\n- **Filter overlapping instances**: New controls in training/inference dialogs\n- **Crop size visualization**: Overlay showing crop region for top-down models\n\n*For a complete list of changes, see our [Changelog](../changelog.md).*\n" }, { "path": "docs/guides/run-training-and-inference-on-colab.md", "content": "*Case: You already have a project with labeled training data and you'd like to run training or inference in a Colab notebook.*\n\nTake a look at our [example notebooks](../notebooks/Training_and_inference_on_an_example_dataset.ipynb) which you can run on Colab! These will walk you through the entire process of running training and inference on Colab.\n\n### About Colab\n\n[Google Colaboratory](https://colab.research.google.com/) is a hosted service from Google that lets you run Python code (like SLEAP) on Google's servers with access to GPUs for training and inference. It's free!\n\nWhen you start running code in a notebook, Google connects your notebook to a virtual server instance. Think of this as a new computer with Python and PyTorch pre-installed. You can run things on this server instance for at most 12 hours (it will timeout sooner if idle but this depends on how much demand there is for Google's servers).\n\nOnce the server instance times out, you lose all the data that's stored on the server instance. This means that you'll need to install SLEAP from the notebook at the beginning of each new session, you'll need to move your data to the server instance, and you'll need to copy any results off the server instance when you're done.\n\nYou can also pay for [Colab Pro](https://colab.research.google.com/signup) (currently available only in the US) which gives you priority access to better hardware and longer runtimes (they say 24 hours and more lenient idle timeouts).\n\n### Moving Data to/from Colab\n\nThe easiest way to work with your own data on Colab is to use [Google Drive](https://www.google.com/drive/). This is another service provided by Google. It's also free! \n(*If your university has [G Suite for Education](https://edu.google.com/products/gsuite-for-education/) you'll have unlimited storage space.*)\n\nGoogle Drive is similar to Dropbox, Microsoft OneDrive, or Apple iCloud. You can install software and you'll get a special folder which is automatically synced: anything you copy into it will be available from other computers (using your account), and anything copied into it from elsewhere will automatically be downloaded into the folder on your computer.\n\nFor training on your own data, it's probably easiest to export a training job package from the SLEAP GUI. The training job package will contain model files as well as all of your labeled data and all of the images for labeled frames within a single file, so that you don't have to copy the full training videos and you don't have to worry about paths to the videos.\n\nIf you'd rather copy all your videos over to Colabā€”perhaps because you plan to run inference on the full videosā€”you can place the videos in the same directory as your SLEAP project file and as long as each video has a distinctive filename, SLEAP will be able to find them (it tries to locate the videos in the same directory as the project if it can't find the videos at the path they had when added to the project)." }, { "path": "docs/guides/running-sleap-remotely.md", "content": "*Case: You already have a project with training data and you want to train on a different machine using a command-line interface.*\n\nYou need three things to run training:\n\n!!! note\n If you only need to run training and inference from the command line, you do **not** need to install the full SLEAP packageā€”just installing `sleap-nn` is sufficient.\n\n1. You need to install `sleap-nn` on the remote machine where you'll run training.\n2. Labels and images to use for training.\n3. A training profile which defines the training parameters (e.g., learning rate, image augmentation methods).\n\n**Installing sleap-nn**:\n\nSee the [installation instructions](https://nn.sleap.ai/latest/installation).\n\n**Training labels and images**:\n\nUsually the easiest and best way to make the training labels and images available is to export a training job package (\"Predict\" -> \"Run Training..\" -> \"Export Training Job Package..\") and copy that to the remote machine.\n\nAlthough it's easiest if you bundle the labels and images into training job package, there are alternatives. If the files are already on a shared network drive, it may be possible to use the original labels project and videos for training. But this can be tricky, because often the full paths to the files will be different when accessed from different machines (i.e., different paths on Windows and Linux machines or different paths from how the network drive is mounted). To use the original labels and video files, you'll either need to ensure that the file paths to videos used in the project are the same on the remote machine as on the local machine where you last saved the project, **or** if all the video files have distinct filenames, you can place the videos inside the same directory which contains the labels project file.\n\nBut in most cases it's best to create a training job package and just use that for remote training.\n\n**Training profile**:\n\nsleap-nn comes with \"default\" training profiles for training confidence maps, part affinity fields, centroids, or top-down confidence maps (which allow multi-instance inference without using part affinity fields). Any file in the [sample configs](https://github.com/talmolab/sleap-nn/tree/main/docs/sample_configs) can be downloaded/ used, and path to the config file is passed to `sleap-nn train` CLI.\n\nOur guide to [creating a custom training profile](creating-a-custom-training-profile.md) explains how to use the GUI to export custom training profiles. You can also use the `initial_config.yaml` file saved from previous training run as a template for a new training config. You can copy the `yaml` file and edit it in any text editor.\n\n**Command-line training**:\n\nOnce you have your training job package (or labels package and training profile), you can run training using the [`sleap-nn train`](https://nn.sleap.ai/latest/training/#using-cli) command like so:\n\n```sh\nsleap-nn train --config \"data_config.train_labels_path=[]\" trainer_config.ckpt_dir=\"models\" trainer_config.run_name=\n```\n\nThe model will be saved in the `models/` directory within the same directory as the **training job package** (in this case, `models/run_name/`).\n\n!!! note\n If you exported the training package as a ZIP file, it contains both the `.pkg.slp` and `.yaml` files necessary to train with the configuration you selected in the GUI. Before running the [`sleap-nn train`](https://nn.sleap.ai/latest/training/#using-cli) command, make sure to unzip this file:\n\n === \"macOS/Linux\"\n\n ```bash\n unzip training_job.zip -d training_job\n ```\n\n === \"Windows (PowerShell)\"\n\n ```powershell\n Expand-Archive -Path training_job.zip -DestinationPath training_job\n ```\n\n === \"Windows (Command Prompt)\"\n\n ```cmd\n tar -xf training_job.zip -C training_job\n ```\n\n## Remote inference\n\n*Case: You already have models and you want to run inference on a different machine using a command-line interface.*\n\nHere's what you need to run inference:\n\n1. You need to install sleap-nn on the remote machine where you'll run training.\n2. You need a compatible set of trained model files.\n3. You need a video for which you want predictions.\n\n**Installing sleap-nn**:\n\nSee the [installation instructions](https://nn.sleap.ai/latest/installation).\n\n**Trained models**\n\nWhen you train a model, you'll get a directory with the `run_name` of the model.\n\nThe model directory will contain at least these two files:\n\n- `training_config.yaml` (or `training_config.json` from SLEAP<=1.4.1) is the training profile used to train the model, together with some additional information about the trained model. Amongst other things, this specifies the network architecture of the model.\n- `best.ckpt` (or `best_model.h5` from SLEAP<=1.4.1) contains the weights for the trained model.\n\nYou'll need both of these files for each model you're going to use for inference. \n\n!!! note \"Legacy SLEAP Model Support\"\n SLEAP-NN supports running inference on models trained with legacy SLEAP (version 1.4.1 or earlier). \n You can use the `sleap-nn track` command with legacy model files (`.h5` and `.json`) as described in the [Legacy SLEAP Model Support documentation](https://nn.sleap.ai/latest/inference/#legacy-sleap-model-support). \n This allows you to run inference on older models without needing to retrain them with the new backend.\n\n\nInference will run in different modes depending on the output types of the models you supply. See the instructions for [Model Configuration](https://nn.sleap.ai/latest/reference/models/).\n\nFor this example, let's suppose you have two models: centroids and instance-centered confidence maps. This is the typical \"top-down\" case for multi-instance predictions.\n\n**Video**\n\nsleap-nn (which uses `sleap-io`) uses OpenCV to read a variety of video formats including `mp4` and `avi` files. You'll just need the file path to run inference on such a video file.\n\nFor this example, let's suppose you're working with an HDF5 video at `path/to/video.mp4`.\n\n**Command-line inference**:\n\nTo run inference, you'll call [`sleap-nn track`](https://nn.sleap.ai/latest/inference/#run-inference-with-cli) with the paths to each trained model and your video file, like so:\n\n```sh\nsleap-nn track -i path/to/video.mp4 --video_dataset video --video_input_format channels_last -m path/to/models/centroid -m path/to/models/centered-instance\n```\n\nThis will run inference on the entire video. If you only want to run inference on some range of frames, you can specify this with the `--frames 123-456` command-line argument.\n\nThis will give you predictions frame-by-frame, but will not connect those predictions across frames into `tracks`. If you want cross-frame identity tracking, set `--tracking` argument. For optical flow, use `--use_flow`. For matching identities without optical flow and using each instance centroid (rather than all the predicted nodes), use `--features centroids --scoring_method euclidean_dist`.\n\nIt's also be possible to run tracking separately after you've generated a predictions file (see [`Track-only workflow`](https://nn.sleap.ai/latest/inference/#track-only-workflow)). This makes it easy to try different tracking methods and parameters without needing to re-run the full inference process.\n\nWhen inference is finished, it will save the predictions in a new slp file. This file has the same format as a standard SLEAP project file, and you can use the GUI to proofread this file or merge the predictions into an existing SLEAP project. The file will be in the same directory as the video and the filename will be `{video filename}.predictions.slp`." }, { "path": "docs/guides/tracking-and-proofreading.md", "content": "# Tracking and Proofreading\n\nThis guide covers tracking methods and proofreading strategies for multi-animal pose estimation. For a step-by-step introduction, see the [Tracking Tutorial](../tutorial/tracking-new-data.md) and [Proofreading Tutorial](../tutorial/proofreading.md).\n\n---\n\n## How Tracking Works\n\nTracking connects frame-by-frame pose predictions into continuous **tracks** (identities across frames). The tracker:\n\n1. Takes predictions from frame N\n2. Compares them to **candidates** from previous frames\n3. Assigns each instance to an existing track or creates a new one\n\nPrediction and tracking are distinct processesā€”you can run tracking separately after inference to try different methods and parameters.\n\n[:octicons-arrow-right-24: sleap-nn Tracking Reference](https://nn.sleap.ai/latest/guides/tracking/)\n\n---\n\n## Tracking Methods\n\n### Candidates Method\n\nHow the tracker builds the pool of candidates to match against. This determines which previous instances are considered when assigning track IDs to new detections.\n\n#### Fixed Window (Default)\n\nPools all instances from the last N frames together as matching candidates.\n\n```\nFrame: t-3 t-2 t-1 t (current)\n ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”\nInstances ā”‚ A B A B A B ā”‚ ? ? ā† match against pooled candidates\n ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜\n window_size=3\n```\n\nAll instances from frames within the window are collected into a single candidate pool. When matching instances in the current frame, each is compared against this entire pool, and the best matches are assigned.\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --candidates_method fixed_window --tracking_window_size 10\n```\n\n**Pros:**\n\n- Simple and fast\n- Works well when all instances are consistently detected\n\n**Cons:**\n\n- If a track is lost for several frames, its history gets pushed out of the window\n- All tracks share the same temporal context\n\n**Best for**: Most scenarios with reliable detectionsā€”good balance of speed and accuracy.\n\n[:octicons-arrow-right-24: Fixed Window Details](https://nn.sleap.ai/latest/guides/tracking/#fixed-window-default)\n\n#### Local Queues\n\nMaintains a separate history queue for each track ID. Each track remembers its own last N instances independently.\n\n```\nTrack A: [A@t-5, A@t-4, A@t-3, A@t-2, A@t-1] ā† own history\nTrack B: [B@t-3, B@t-2, B@t-1] ā† own history (was occluded at t-5, t-4)\n\nFrame t: ? ? ā† each detection matched against per-track histories\n```\n\nWhen an instance disappears temporarily (e.g., due to occlusion), its track queue preserves its history. When the instance reappears, it can still be matched to its original track even if many frames have passed.\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --candidates_method local_queues --tracking_window_size 5\n```\n\n**Pros:**\n\n- **Supports max tracking**: Since each track has its own queue, the system naturally maintains a fixed number of identities and prioritizes matching to existing tracks before spawning new ones\n- Robust to temporary track breaks and occlusions\n- Each track maintains its own temporal context\n- Better identity preservation when instances disappear and reappear\n\n**Cons:**\n\n- Slightly more memory overhead\n- Can be slower with many tracks\n\n**Best for**: Experiments with a known, fixed number of animalsā€”especially when you want to maintain consistent identities throughout the video. Also good for scenes with occlusions or when animals frequently leave and re-enter the frame.\n\n[:octicons-arrow-right-24: Local Queues Details](https://nn.sleap.ai/latest/guides/tracking/#local-queues)\n\n#### Optical Flow\n\nUses optical flow ([Xiao et al., 2018](https://arxiv.org/abs/1804.06208)) to predict where instances will move, then uses these shifted positions as candidates.\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --use_flow\n```\n\n**Best for**: Fast-moving animals where position changes significantly between frames.\n\n[:octicons-arrow-right-24: Optical Flow Details](https://nn.sleap.ai/latest/guides/tracking/#optical-flow)\n\n---\n\n### Scoring Methods\n\nHow similarity is measured between instances and candidates.\n\n| Method | Description | Use Case |\n|--------|-------------|----------|\n| `oks` | Object Keypoint Similarityā€”distance between keypoints, normalized | Default, works well for most cases |\n| `euclidean_dist` | Euclidean distance between features | Simple and fast |\n| `cosine_sim` | Cosine similarity between feature vectors | Good for image-based features |\n| `iou` | Intersection over Union of bounding boxes | When instances have distinct spatial positions |\n\nSet with `--scoring_method`:\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --scoring_method oks\n```\n\n---\n\n### Feature Types\n\nWhat features are used for matching.\n\n| Feature | Description |\n|---------|-------------|\n| `keypoints` | All predicted keypoint positions (default) |\n| `centroids` | Instance centroid only |\n| `bboxes` | Bounding box coordinates |\n| `image` | Image features from the model |\n\nSet with `--features`:\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --features centroids --scoring_method euclidean_dist\n```\n\n---\n\n### Matching Methods\n\nHow instances are paired with candidates once similarity is computed.\n\n| Method | Description |\n|--------|-------------|\n| `hungarian` | Finds optimal global assignment minimizing total cost (default) |\n| `greedy` | Picks best match for each instance in order |\n\nSet with `--track_matching_method`:\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --track_matching_method hungarian\n```\n\n---\n\n## Track Settings\n\n### Maximum Tracks\n\nLimit the number of track identities. Once reached, no new tracks are created.\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --max_tracks 5\n```\n\nIn the GUI, set via **Predict > Run Inference**:\n\n![set-instance-count](../assets/images/set-instance-count.jpg)\n\n### Connect Single Track Breaks\n\nWhen exactly one track is lost in frame N and exactly one new track appears in frame N+1, automatically connect them. Enable with the `Connect Single Track Breaks` checkbox in the GUI.\n\n### Track Window Size\n\nHow many previous frames to consider when building candidates. Larger windows are more robust but slower.\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --tracking_window_size 10\n```\n\n---\n\n## Track-Only Mode\n\nRe-run tracking on existing predictions without re-running inference:\n\n```bash\nsleap-nn track -i predictions.slp --tracking\n```\n\nThis is useful for trying different tracking parameters without recomputing poses.\n\n[:octicons-arrow-right-24: Track-Only Mode](https://nn.sleap.ai/latest/guides/tracking/#track-only-mode)\n\n---\n\n## Example Configurations\n\n### Fast-Moving Animals\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --use_flow --of_img_scale 0.5\n```\n\n### Crowded Scenes\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --candidates_method local_queues --tracking_window_size 10 --max_tracks 10\n```\n\n### High Accuracy\n\n```bash\nsleap-nn track -i video.mp4 -m models/ -t --scoring_method oks --scoring_reduction mean --track_matching_method hungarian\n```\n\n[:octicons-arrow-right-24: More Examples](https://nn.sleap.ai/latest/guides/tracking/#example-configurations)\n\n---\n\n## Improving Tracking Results\n\nIf tracking results are poor:\n\n1. **Try different methods**: Change candidates method, scoring method, or enable optical flow\n2. **Adjust track window**: Increase `--tracking_window_size` for more context\n3. **Limit tracks**: Set `--max_tracks` if you know the number of animals\n4. **Improve predictions**: Poor frame-by-frame predictions lead to poor trackingā€”consider [adding more training data](importing-predictions-for-labeling.md)\n\n[:octicons-arrow-right-24: Troubleshooting](https://nn.sleap.ai/latest/guides/tracking/#troubleshooting)\n\n---\n\n## Proofreading\n\nOnce tracking is complete, you'll need to review and fix errors. There are two main types of mistakes:\n\n### Setting Up for Proofreading\n\n1. Enable **Color Predicted Instances** in the View menu\n2. Choose a good **color palette**:\n - \"five+\" palette for small numbers of instances (makes later tracks stand out)\n - \"alphabet\" palette for many instances (26 distinct colors)\n3. Set **Trail Length** > 0 to see where instances were in prior frames\n\nColors appear both on the video frame:\n\n![orange-leg](../assets/images/orange-leg.jpg)\n\nAnd on the seekbar:\n\n![orange-track](../assets/images/orange-track.jpg)\n\nYou can edit palettes in the [View menu](../learnings/gui.md/#view).\n\n---\n\n### Fixing Lost Identities\n\nWhen the tracker fails to connect an instance to any previous track, creating a spurious new identity.\n\n**Strategy:**\n\n1. Use **Go > Next Track Spawn Frame** to jump to frames where new tracks appear\n2. Select the instance with the new track identity\n3. Look at the track trail to determine which existing track it should belong to\n4. Hold the **Show tracks legend** key (see [Keyboard Navigation](../learnings/gui.md/#keyboard-navigation)) to see numbered tracks:\n\n![track-fixing-list](../assets/images/track-fixing-list.jpg)\n\n5. While holding the key, type the number to assign the instance to that track (e.g., **Command + 1** assigns to track \"F\")\n\n---\n\n### Fixing Identity Swaps\n\nWhen the tracker assigns instances to the wrong tracks (swapping identities between animals).\n\n**Strategy 1: Visual Inspection with Trails**\n\n1. Set trail length to ~50 frames\n2. Use **frame next large step** to jump through predictions\n3. Look for crossed or tangled trails indicating swaps:\n\n![swap-trails](../assets/images/swap-trails.jpg)\n\n4. When you find a swap, step through frames to find the exact frame\n5. Use **Labels > Transpose Instance Tracks** or the tracks legend to fix\n\n**Strategy 2: Velocity-Based Suggestions**\n\n1. Open **Labeling Suggestions** panel\n2. Select **velocity** method\n3. Choose a stable node (body center, not appendages)\n4. Adjust thresholdā€”higher means fewer suggestions\n\n![velocity-suggestions](../assets/images/velocity-suggestions.jpg)\n\n5. Step through suggestions with **Go > Next Suggestion**\n6. Fix swaps as they're found\n\n**Propagating Fixes:**\n\nEnable **Propagate Track Labels** to apply track changes to all subsequent frames automatically.\n\n---\n\n### Orientation Visibility\n\nIf instance orientation is hard to see, change edge style from lines to **wedges** in **View > Edge Style**:\n\n![wedges](../assets/images/wedges.jpg)\n\nWedges point from source to destination nodes in your skeleton.\n" }, { "path": "docs/help.md", "content": "# Help\n\nStuck? Can't get SLEAP to run? Crashing? Try the tips below.\n\n**First step:** Run `sleap doctor` to check your installation.\n\n---\n\n## Installation\n\n
\nI can't get SLEAP to install!\n\nHave you tried all of the steps in the [installation instructions](installation.md)?\n\nIf so, please [start a discussion](https://github.com/talmolab/sleap/discussions) or [open an issue](https://github.com/talmolab/sleap/issues) and tell us:\n\n- How you're trying to install it\n- What error messages you're getting\n- Which operating system you're on\n- Output of `sleap doctor` (if available)\n\n
\n\n
\nCan I install it on a computer without a GPU?\n\nYes! Install SLEAP normally using `uv` and the GPU support will be ignored. Use `--torch-backend cpu` to explicitly install CPU-only:\n\n```bash\nuv tool install --python 3.13 \"sleap[nn]\" --prerelease allow --torch-backend cpu\n```\n\n
\n\n
\nWhat if I already have CUDA set up on my system?\n\nSLEAP 1.5+ uses PyTorch, which bundles its own CUDA libraries. Your system CUDA installation is not used. The `--torch-backend auto` flag will detect your GPU and install the appropriate PyTorch version.\n\n
\n\n\n## Display / GUI Issues\n\n
\nSLEAP crashes on Linux with ImportError: undefined symbol: _ZN14QObjectPrivateC2Ei, version Qt_6_PRIVATE_API\n\nThis error occurs when the system has Qt 6 libraries (e.g., Debian 12 ships Qt 6.4) that conflict with the Qt libraries bundled inside PySide6. The dynamic linker loads the system's older `libQt6DBus.so.6` or `libQt6Core.so.6` instead of PySide6's bundled version, causing a symbol version mismatch.\n\nCommon error messages:\n\n```\nImportError: /usr/lib/x86_64-linux-gnu/libQt6DBus.so.6: undefined symbol: _ZN14QObjectPrivateC2Ei, version Qt_6_PRIVATE_API\n```\n\n**This is fixed automatically in SLEAP v1.6.1+.** On Linux, SLEAP now ensures PySide6's bundled Qt libraries and plugins are loaded before any system or conda-provided Qt libraries.\n\nIf you're on an older version, you can work around it by prepending PySide6's Qt library path when launching SLEAP:\n\n```bash\nPYSIDE_QT=$(\"$(uv tool dir)/sleap/bin/python\" -c \"import os,PySide6;print(os.path.join(os.path.dirname(PySide6.__file__),'Qt'))\") && \\\n LD_LIBRARY_PATH=\"$PYSIDE_QT/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}\" \\\n QT_PLUGIN_PATH=\"$PYSIDE_QT/plugins\" \\\n sleap\n```\n\nIf you suspect the automatic fix is causing issues on your system (e.g., a native Linux desktop that was working before), you can disable it:\n\n```bash\nexport SLEAP_SKIP_QT_FIX=1\nsleap\n```\n\n
\n\n
\nSLEAP crashes with Could not load the Qt platform plugin \"xcb\"\n\nThis typically has two causes:\n\n**1. Missing `libxcb-cursor0`** (required since Qt 6.5):\n\n```bash\nsudo apt install libxcb-cursor0 # Debian/Ubuntu\n```\n\n**2. OpenCV's Qt plugins conflicting with PySide6's.** The error may mention a path like `.../cv2/qt/plugins`. This happens because `opencv-python` bundles its own Qt and its plugin path takes priority over PySide6's.\n\nSLEAP v1.6.1+ handles this automatically by setting `QT_PLUGIN_PATH` to PySide6's plugins on Linux. If you're on an older version, use the workaround in the entry above.\n\n
\n\n
\nSLEAP GUI doesn't work over SSH / X11 forwarding\n\nWhen connecting to a remote Linux machine via SSH with X forwarding (`ssh -X` or `ssh -Y`), make sure:\n\n1. **X forwarding is enabled** on both client and server (`X11Forwarding yes` in `/etc/ssh/sshd_config`)\n2. **`DISPLAY` is set** ā€” run `echo $DISPLAY` (should show something like `localhost:10.0`)\n3. **`libxcb-cursor0` is installed** on the remote machine (`sudo apt install libxcb-cursor0`)\n4. **Conda base is deactivated** if active ā€” conda can inject conflicting Qt libraries:\n ```bash\n conda deactivate\n conda config --set auto_activate_base false # prevent future interference\n ```\n\nIf you still see Qt library errors, see the entries above. Running `sleap doctor` will report your environment details for further debugging.\n\n
\n\n
\nVideos fail to load with Could not open codec h264 or Failed initializing scaling graph\n\nThis can happen on Linux when OpenCV's video decoder picks up incompatible ffmpeg libraries. Common error messages:\n\n```\n[ERROR:0@70.376] global cap_ffmpeg_impl.hpp:1448 open Could not open codec h264, error: -11\n[ERROR:0@70.376] global cap_ffmpeg_impl.hpp:1456 open VIDEOIO/FFMPEG: Failed to initialize VideoCapture\n```\n\nOften preceded by many lines of:\n\n```\n[swscaler @ ...] Failed initializing scaling graph (Resource temporarily unavailable):\n fmt:yuv420p csp:unknown prim:unknown trc:unknown -> fmt:bgr24 ...\n```\n\nAnd ultimately:\n\n```\nIndexError: Failed to read frame index 0.\n```\n\n**Fix:** Switch the video backend from OpenCV to imageio-ffmpeg:\n\n```bash\nsleap --video-backend ffmpeg\n```\n\nThis persists to your preferences ā€” all future launches will use the FFMPEG backend automatically. You can also use `pyav` as an alternative. To switch back:\n\n```bash\nsleap --video-backend opencv\n```\n\n
\n\n\n## Usage\n\n
\nHow do I use SLEAP?\n\nIf you're new to pose tracking in general, check out [this talk](https://cbmm.mit.edu/video/decoding-animal-behavior-through-pose-tracking) or our review in _[Nature Neuroscience](https://rdcu.be/caH3H)_.\n\nIf you're just new to SLEAP, start with the [high-level overview](overview.md) and then follow the [tutorial](tutorial/overview.md).\n\nOnce you get the hang of it, check out the [guides](guides/guides-overview.md) for more detailed info.\n\n
\n\n
\nDoes my data need to be in a particular format?\n\nSLEAP supports many formats, including all common video formats and imported data from DeepLabCut and others.\n\nHowever, some video acquisition software saves videos in formats not suitable for computer vision processing. A common issue is that videos are not **reliably seekable**ā€”you may not get the same data when reading a particular frame index. This is because many video formats reconstruct images using data from adjacent frames.\n\nIf you think you may be affected, re-encode your videos:\n\n```bash\nffmpeg -y -i \"input.mp4\" -c:v libx264 -pix_fmt yuv420p -preset superfast -crf 23 \"output.mp4\"\n```\n\n**Options explained:**\n\n- `-c:v libx264`: H264 compression\n- `-pix_fmt yuv420p`: Compatibility with all players\n- `-preset superfast`: Enables reliable seeking\n- `-crf 23`: Quality level (15 = nearly lossless, 30 = highly compressed)\n\nIf you don't have `ffmpeg`, install it from [ffmpeg.org](https://ffmpeg.org/download.html) or via your package manager.\n\n
\n\n
\nI get strange results where poses appear correct but shifted relative to the image\n\nThis is most likely a video compression issue. Re-encode your video using the ffmpeg command above.\n\n
\n\n
\nHow do I get predictions out?\n\nSee [Exporting the Results](tutorial/exporting-the-results.md) and [CLI Reference](reference/command-line-interfaces.md).\n\n
\n\n
\nWhat do I do with the output of SLEAP?\n\nCheck out the [Analysis examples](notebooks/Analysis_examples.ipynb) notebook for working with pose data in Python.\n\n
\n\n\n## Troubleshooting Workflows\n\nSLEAP can work with any type of data, but sometimes tweaking configurations or parameters helps improve performance.\n\n### Stage 1: Getting Started\n\nWhen starting off, troubleshoot the model type and basic configurations if you can't get results after initial training:\n\n![Stage 1 troubleshooting workflow](assets/images/troubleshooting_stage1.png)\n\nSee [Configuring Models](https://nn.sleap.ai/latest/reference/models/) for more information on model types.\n\n### Stage 2: Refining Models\n\nOnce you have enough labeled frames and a working model, refine by selecting frames that represent problem areas (overlapping animals, unusual poses):\n\n![Stage 2 troubleshooting workflow](assets/images/troubleshooting_stage2.png)\n\n### Stage 3: Fine-Tuning\n\nIn later stages, squeeze out additional performance by tuning hyperparameters:\n\n![Stage 3 troubleshooting workflow](assets/images/troubleshooting_stage3.png)\n\n---\n\n## Getting More Help\n\n
\nI've found a bug or have another problem!\n\n1. Run `sleap doctor` and copy the output\n2. [Start a discussion](https://github.com/talmolab/sleap/discussions) to get help from developers and community\n3. Or [open an issue](https://github.com/talmolab/sleap/issues) if you've found a bug\n\n
\n\n
\nCan I just talk to someone?\n\nSLEAP is a complex machine learning system, and we may not have considered your specific use case.\n\nFeel free to reach out at `talmo@salk.edu` if you have a question that isn't covered here.\n\n
\n\n\n## Improving SLEAP\n\n**How can you help?**\n\n- **Tell your friends!** We love hearing stories about what worked or didn't work (`talmo@salk.edu`)\n- **[Cite our paper](https://www.nature.com/articles/s41592-022-01426-1)** in your publications\n- **Share ideas** for new features in the [Discussion forum](https://github.com/talmolab/sleap/discussions/categories/ideas)\n- **Contribute code!** See our [contribution guidelines](contribute.md)\n\n??? note \"BibTeX\"\n\n ```bibtex\n @ARTICLE{Pereira2022sleap,\n title={SLEAP: A deep learning system for multi-animal pose tracking},\n author={Pereira, Talmo D and Tabris, Nathaniel and Matsliah, Arie and\n Turner, David M and Li, Junyu and Ravindranath, Shruthi and\n Papadoyannis, Eleni S and Normand, Edna and Deutsch, David S and\n Wang, Z. Yan and McKenzie-Smith, Grace C and Mitelut, Catalin C and\n Castro, Marielisa Diez and D'Uva, John and Kislin, Mikhail and\n Sanes, Dan H and Kocher, Sarah D and Samuel S-H and\n Falkner, Annegret L and Shaevitz, Joshua W and Murthy, Mala},\n journal={Nature Methods},\n volume={19},\n number={4},\n year={2022},\n publisher={Nature Publishing Group}\n }\n ```\n\n---\n\n## Usage Data\n\nTo help us improve SLEAP, you may allow us to collect basic and **anonymous** usage data. If enabled from the **Help** menu, the SLEAP GUI will transmit information such as which version of Python and operating system you are running.\n\nThis helps us:\n\n- Understand which systems SLEAP is used on\n- Ensure updates don't break compatibility\n- Report usage to grant funding agencies\n\nYou can opt out at any time from the menu. To prevent data sharing entirely, launch with `sleap-label --no-usage-data`. Usage data is only shared from the GUI, not the API or CLIs. See the [source code](https://github.com/talmolab/sleap/blob/develop/sleap/gui/web.py) for exactly what is collected.\n" }, { "path": "docs/index.md", "content": "# Social LEAP Estimates Animal Poses (SLEAP)\n\n
\n![SLEAP pose estimation demo](assets/images/sleap_movie.gif)\n
\n\n
\n[![GitHub stars](https://img.shields.io/github/stars/talmolab/sleap?style=flat&logo=github)](https://github.com/talmolab/sleap)\n[![Release](https://img.shields.io/github/v/release/talmolab/sleap?label=Latest&color=green)](https://github.com/talmolab/sleap/releases/)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/sleap?color=orange)\n[![PyPI](https://img.shields.io/pypi/v/sleap?label=PyPI&color=blue)](https://pypi.org/project/sleap)\n[![Nature Methods](https://img.shields.io/badge/Nature%20Methods-2022-purple)](https://www.nature.com/articles/s41592-022-01426-1)\n
\n\n**SLEAP** is an open-source deep learning framework for multi-animal pose tracking ([Pereira et al., Nature Methods, 2022](https://www.nature.com/articles/s41592-022-01426-1)). It provides an end-to-end workflow from labeling to trained models, with a purpose-built GUI for active learning and proofreading.\n\n## āœØ Features\n\n- **Easy installation** ā€“ One-line install with support for all major OSes\n- **Powerful GUI** ā€“ Human-in-the-loop workflow for rapidly labeling large datasets\n- **Flexible models** ā€“ Single and multi-animal pose estimation with top-down and bottom-up strategies\n- **Customizable architectures** ā€“ Neural networks that deliver accurate predictions with very few labels\n- **Fast training** ā€“ 15-60 mins on a single GPU for typical datasets\n- **Fast inference** ā€“ Up to 600+ FPS batch processing, <10ms realtime latency\n- **Modern backends** ā€“ [`sleap-io`](https://io.sleap.ai) for data handling and PyTorch-based [`sleap-nn`](https://nn.sleap.ai) for training\n\n**Let's Get Some SLEAP!** šŸ­šŸ­\n\n---\n\n## šŸ“š Explore the Docs\n\n
\n\n- :material-map:{ .lg .middle } **Workflow Overview**\n\n ---\n\n End-to-end workflow with links to all resources.\n\n [:octicons-arrow-right-24: View Workflow](overview.md)\n\n- :material-school:{ .lg .middle } **Tutorial**\n\n ---\n\n Step-by-step guide from labeling to tracking.\n\n [:octicons-arrow-right-24: Start Learning](tutorial/overview.md)\n\n- :material-book-open-variant:{ .lg .middle } **Guides**\n\n ---\n\n Advanced workflows and best practices.\n\n [:octicons-arrow-right-24: Explore Guides](guides/guides-overview.md)\n\n- :material-notebook:{ .lg .middle } **Notebooks**\n\n ---\n\n Jupyter notebooks for training, analysis, and more.\n\n [:octicons-arrow-right-24: Browse Notebooks](notebooks/notebooks-overview.md)\n\n- :material-lightbulb-on:{ .lg .middle } **Learning**\n\n ---\n\n GUI, training options, skeleton design, and more.\n\n [:octicons-arrow-right-24: Deep Dive](learnings/system-overview.md)\n\n- :material-bookshelf:{ .lg .middle } **Reference**\n\n ---\n\n CLI, datasets, and full API.\n\n [:octicons-arrow-right-24: Reference Docs](api/index.md)\n\n
\n\n---\n\n## šŸš€ Get some SLEAP!\n\n### Quick start\n\nInstall [`uv`](https://github.com/astral-sh/uv) first:\n\n```bash\n# macOS/Linux\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n\n# Windows\npowershell -c \"irm https://astral.sh/uv/install.ps1 | iex\"\n```\n\nThen install SLEAP:\n\n```bash\nuv tool install --python 3.13 \"sleap[nn]\" --torch-backend auto\n```\n\nLaunch the GUI:\n\n```bash\nsleap label\n```\n\n[:octicons-arrow-right-24: Full installation instructions](installation.md)\n\n### Learn to SLEAP\n\n- **Learn step-by-step:** [Tutorial](tutorial/overview.md)\n- **Learn more advanced usage:** [Guides](guides/guides-overview.md) and [Notebooks](notebooks/notebooks-overview.md)\n- **Learn by watching:** [ABL:AOC 2023 Workshop](https://www.youtube.com/watch?v=BfW-HgeDfMI) and [MIT CBMM Tutorial](https://cbmm.mit.edu/video/decoding-animal-behavior-through-pose-tracking)\n- **Learn by reading:** [Paper (Pereira et al., Nature Methods, 2022)](https://www.nature.com/articles/s41592-022-01426-1) and [Review on behavioral quantification (Pereira et al., Nature Neuroscience, 2020)](https://rdcu.be/caH3H)\n- **Learn from others:** [Discussions on Github](https://github.com/talmolab/sleap/discussions)\n\n---\n\n## šŸ”„ Coming from SLEAP 1.4 or earlier?\n\n!!! tip \"New in SLEAP v1.5+\"\n SLEAP v1.5+ introduced major changes including UV-based installation, PyTorch backend via `sleap-nn`, and modular data workflows with `sleap-io`. Check out [Migration Guide](guides/migrating-to-sleap-1-5.md)!\n\n| SLEAP ā‰¤ 1.4 | SLEAP 1.5+ |\n|-------------|------------|\n| Conda installation | UV or pip installation |\n| TensorFlow backend | PyTorch backend (`sleap-nn`) |\n| Monolithic package | Modular: GUI + `sleap-nn` + `sleap-io` |\n\n!!! warning \"Legacy Documentation\"\n If you are using SLEAP version 1.4.1 or earlier, please visit the [legacy documentation](https://legacy.sleap.ai).\n\n---\n\n## Get Help\n\n
\n\n- :material-help-circle:{ .lg } **Help Page**\n\n Common issues and solutions. [View Help](help.md)\n\n- :fontawesome-brands-github:{ .lg } **Report Issues**\n\n Found a bug? [Create an issue](https://github.com/talmolab/sleap/issues/new?template=bug_report.md)\n\n- :material-forum:{ .lg } **Discussions**\n\n Questions? [Start a discussion](https://github.com/talmolab/sleap/discussions)\n\n
\n\n---\n\n## References\n\nSLEAP is the successor to the single-animal pose estimation software [LEAP (Pereira et al., Nature Methods, 2019)](https://www.nature.com/articles/s41592-018-0234-5). If you use SLEAP in your research, please cite:\n\n> T.D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D'Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy. **SLEAP: A deep learning system for multi-animal pose tracking.** *Nature Methods*, 19(4), 2022. [:octicons-link-external-16:](https://www.nature.com/articles/s41592-022-01426-1)\n\n??? note \"BibTeX\"\n\n ```bibtex\n @ARTICLE{Pereira2022sleap,\n title={SLEAP: A deep learning system for multi-animal pose tracking},\n author={Pereira, Talmo D and Tabris, Nathaniel and Matsliah, Arie and\n Turner, David M and Li, Junyu and Ravindranath, Shruthi and\n Papadoyannis, Eleni S and Normand, Edna and Deutsch, David S and\n Wang, Z. Yan and McKenzie-Smith, Grace C and Mitelut, Catalin C and\n Castro, Marielisa Diez and D'Uva, John and Kislin, Mikhail and\n Sanes, Dan H and Kocher, Sarah D and Samuel S-H and\n Falkner, Annegret L and Shaevitz, Joshua W and Murthy, Mala},\n journal={Nature Methods},\n volume={19},\n number={4},\n year={2022},\n publisher={Nature Publishing Group}\n }\n ```\n\n---\n\n## Contributors\n\nSLEAP was created in the [Murthy](https://murthylab.princeton.edu) and [Shaevitz](https://shaevitzlab.princeton.edu) labs at the [Princeton Neuroscience Institute](https://pni.princeton.edu) at Princeton University.\n\nSLEAP is currently being developed and maintained in the [Talmo Lab](https://talmolab.org) at the [Salk Institute for Biological Studies](https://salk.edu), in collaboration with the Murthy and Shaevitz labs at Princeton University.\n\n??? note \"Contributors & Funding\"\n\n **Contributors.**\n\n - **Talmo Pereira**, Salk Institute for Biological Studies\n - **Liezl Maree**, Salk Institute for Biological Studies\n - **Arlo Sheridan**, Salk Institute for Biological Studies\n - **Arie Matsliah**, Princeton Neuroscience Institute, Princeton University\n - **Nat Tabris**, Princeton Neuroscience Institute, Princeton University\n - **David Turner**, Research Computing and Princeton Neuroscience Institute, Princeton University\n - **Joshua Shaevitz**, Physics and Lewis-Sigler Institute, Princeton University\n - **Mala Murthy**, Princeton Neuroscience Institute, Princeton University\n\n **Funding**\n\n This work was made possible through our funding sources, including:\n\n - NIH BRAIN Initiative R01 NS104899\n - Princeton Innovation Accelerator Fund\n\n---\n\n## License\n\nSLEAP is released under a [Clear BSD License](https://raw.githubusercontent.com/talmolab/sleap/main/LICENSE) and is intended for research/academic use only.\n\nFor commercial use, please contact: **Laurie Tzodikov** (Assistant Director, Office of Technology Licensing), Princeton University, 609-258-7256.\n" }, { "path": "docs/installation.md", "content": "# Installation\n\nSLEAP is a tool for tracking animal poses in video. This guide will get you up and running.\n\n!!! warning \"Using SLEAP 1.4 or earlier?\"\n This guide is for SLEAP 1.5+. For older versions using conda, see the [legacy documentation](https://legacy.sleap.ai).\n\n**What do you want to do?**\n\n- [**Use SLEAP**](#install-sleap) (most users)\n- [**Update SLEAP**](#updating)\n- [**Try pre-release features**](#pre-release-versions)\n- [**Develop or contribute**](#development-setup)\n- [**Use SLEAP as a library**](#programmatic-usage)\n- [**Install with pip**](#pip-installation) (alternate method)\n\n---\n\n## Before You Start\n\n??? question \"Why do I need `uv`?\"\n **What is Python package management?**\n\n Python packages often depend on other packages, which in turn have their own dependenciesā€”each requiring specific versions. Without careful management, you can end up in \"dependency hell\" where different projects need conflicting versions. Package managers solve this by creating isolated environments where each project gets exactly the versions it needs.\n\n **Why did SLEAP use `conda` before?**\n\n SLEAP's neural networks required GPU libraries (CUDA) that were notoriously difficult to install correctly. Conda handled this by bundling CUDA inside isolated environments, making GPU-accelerated training \"just work.\" For many years, this was the only reliable way to install SLEAP.\n\n **What changed?**\n\n Starting in SLEAP 1.5, we transitioned from TensorFlow to PyTorch. Unlike TensorFlow, PyTorch bundles all GPU dependencies directly in its `pip` packageā€”no separate CUDA installation needed. This eliminated the main reason we needed conda.\n\n Conda also had drawbacks: it was slow (environment creation could take 10+ minutes), and you had to remember to \"activate\" your environment every time you wanted to use SLEAP. If you forgot, you'd get confusing errors.\n\n **What is `uv` and why use it?**\n\n `uv` is a modern Python package manager that's blazingly fast (10-100x faster than pip or conda). Beyond speed, `uv` has a killer feature: it can install packages as **tools** that are available system-wide without needing to activate anything.\n\n When you run `uv tool install sleap`, it creates an isolated environment behind the scenes, but exposes the `sleap` command globally. You just type `sleap` and it worksā€”no activation, no environment management, no mental overhead.\n\n Because `uv` is so fast, it's even practical to have multiple versions installed or switch between them. But for most users, the best part is that you can just install SLEAP once and forget about environments entirely.\n\n### Install uv\n\nSLEAP uses `uv` to manage installation. It's a fast, modern package manager that handles everything automaticallyā€”including GPU detection.\n\n=== \"Windows\"\n 1. Press the **Windows key**, type `PowerShell`, press **Enter**\n 2. Paste this command and press **Enter**:\n ```powershell\n irm https://astral.sh/uv/install.ps1 | iex\n ```\n 3. **Close and reopen** PowerShell\n 4. Verify: `uv --version`\n\n=== \"macOS\"\n 1. Press **Cmd+Space**, type `Terminal`, press **Enter**\n 2. Paste this command and press **Enter**:\n ```bash\n curl -LsSf https://astral.sh/uv/install.sh | sh\n ```\n 3. **Close and reopen** Terminal\n 4. Verify: `uv --version`\n\n=== \"Linux\"\n 1. Open a terminal (usually **Ctrl+Alt+T**)\n 2. Paste this command and press **Enter**:\n ```bash\n curl -LsSf https://astral.sh/uv/install.sh | sh\n ```\n 3. **Close and reopen** the terminal\n 4. Verify: `uv --version`\n\n---\n\n## Install SLEAP\n\nOne command works on all platforms. It automatically detects your GPU and installs the right version of PyTorch.\n\n!!! success \"Quick Install\"\n ```bash\n uv tool install --python 3.13 \"sleap[nn]==1.6.1\" --with \"sleap-io==0.6.4\" --with \"sleap-nn==0.1.0\" --torch-backend auto\n ```\n\n Check the [version compatibility table](#version-compatibility) for the latest versions.\n\n!!! warning \"Python version matters\"\n If you don't have Python installed, `uv` will automatically download one. Without `--python 3.13`, it may download Python 3.14 which **SLEAP does not support yet**.\n\n Always include `--python 3.13` (or `--python 3.12`) in your install command.\n\nThat's it! SLEAP is now available system-wide. Run it from any terminal:\n\n```bash\nsleap\n```\n\nA window should open within a few seconds.\n\n??? info \"What does this command do?\"\n - `--python 3.13` ā€” Uses Python 3.13\n - `sleap[nn]` ā€” Installs SLEAP with neural network support for training\n - `--with \"sleap-io==...\"` ā€” Pins dependency versions for compatibility\n - `--torch-backend auto` ā€” Automatically detects your GPU (NVIDIA, AMD, Intel, or CPU)\n\n For pre-release versions (e.g., `sleap-nn==0.1.0a4`), add `--prerelease allow`.\n\n??? tip \"Getting an error about `--torch-backend`?\"\n Update uv to the latest version:\n ```bash\n uv self update\n ```\n\n### Verify installation\n\n```bash\nsleap doctor\n```\n\nThis shows your system info, package versions, and confirms GPU detection.\n\n### Just viewing or annotating (no training)\n\n!!! tip \"Try SLEAP without installing\"\n If you only need to view and annotate data without training models, you don't even need to install anything:\n\n ```bash\n uvx sleap labels.slp\n ```\n\n This runs SLEAP directly without a permanent installation. Replace `labels.slp` with your file, or omit it to open SLEAP with an empty project.\n\n---\n\n## Updating\n\n### Check your current version\n\n```bash\nsleap doctor\n```\n\n### Upgrade everything to latest\n\n```bash\nuv tool upgrade sleap\n```\n\nThis upgrades SLEAP and all its dependencies to the latest compatible versions. It remembers your original settings (like `--prerelease allow` and `--torch-backend auto`).\n\n### Upgrade just sleap-io or sleap-nn\n\nIf there's a new release of a dependency but not SLEAP itself:\n\n```bash\nuv tool upgrade sleap --upgrade-package sleap-io\n```\n\n```bash\nuv tool upgrade sleap --upgrade-package sleap-nn\n```\n\n```bash\nuv tool upgrade sleap --upgrade-package sleap-io --upgrade-package sleap-nn\n```\n\n### Upgrade to a specific version\n\nIf you need specific versions (for reproducibility or to match a collaborator), reinstall:\n\n```bash\nuv tool install --python 3.13 \"sleap[nn]==1.6.1\" --with \"sleap-io==0.6.4\" --with \"sleap-nn==0.1.0\" --torch-backend auto\n```\n\nThis replaces the existing installation with the exact versions specified.\n\n### Downgrade\n\nJust reinstall with the older version:\n\n```bash\nuv tool install --python 3.13 \"sleap[nn]==1.5.2\" --torch-backend auto\n```\n\n### Uninstall\n\n```bash\nuv tool uninstall sleap\n```\n\n??? note \"When to use `--reinstall`\"\n Most of the time, you don't need it. Use `--reinstall` when:\n\n - Something is broken and you want a completely fresh environment\n - Installing from local source code (to pick up changes)\n\n ```bash\n uv tool install --reinstall --python 3.13 \"sleap[nn]==1.6.1\" --with \"sleap-io==0.6.4\" --with \"sleap-nn==0.1.0\" --torch-backend auto\n ```\n\n---\n\n## Pre-release Versions\n\nPre-releases let you try new features before official release. They may have bugs, so use stable versions for important annotation work.\n\n### Latest pre-release\n\n```bash\nuv tool install --python 3.13 \"sleap[nn]\" --prerelease allow --torch-backend auto\n```\n\n### Version compatibility\n\nThe SLEAP ecosystem has three packages that work together:\n\n| SLEAP | sleap-io | sleap-nn |\n|-------|----------|----------|\n| 1.6.1 | 0.6.4 | 0.1.0 |\n| 1.6.0 | 0.6.4 | 0.1.0 |\n| 1.6.0a3 | 0.6.3 | 0.1.0a4 |\n| 1.6.0a2 | 0.6.2 | 0.1.0a2 |\n| 1.6.0a1 | 0.6.1 | 0.1.0a1 |\n| 1.6.0a0 | 0.6.0 | 0.1.0a0 |\n| 1.5.x | <0.6.0 | <0.1.0 |\n\nAlways use compatible versions when pinning.\n\n??? note \"Force a specific PyTorch backend\"\n If `--torch-backend auto` doesn't detect your GPU correctly, you can specify it manually:\n\n | Backend | For |\n |---------|-----|\n | `cu128` | NVIDIA GPUs (CUDA 12.8) |\n | `cu130` | Newest NVIDIA GPUs (CUDA 13.0) |\n | `cpu` | No GPU / CPU only |\n | `rocm` | AMD GPUs |\n | `xpu` | Intel GPUs |\n\n ```bash\n uv tool install --python 3.13 \"sleap[nn]==1.6.1\" --with \"sleap-io==0.6.4\" --with \"sleap-nn==0.1.0\" --torch-backend cu128\n ```\n\n---\n\n## Development Setup\n\nFor contributors and developers who want to modify SLEAP's source code.\n\n### Full ecosystem setup (all three repos)\n\n**1. Clone the repositories:**\n\n```bash\ngit clone https://github.com/talmolab/sleap\ngit clone https://github.com/talmolab/sleap-nn\ngit clone https://github.com/talmolab/sleap-io\ncd sleap\n```\n\n**2. Install with editable local packages:**\n\n```bash\nuv sync --extra nn --reinstall\nuv pip install -e \"../sleap-io[all]\"\nuv pip install -e \"../sleap-nn[torch]\" --torch-backend=auto\n```\n\n??? warning \"Note about `uv sync`\"\n Running `uv sync` again will overwrite your local editable installs with PyPI versions. After any `uv sync`, re-run the `uv pip install -e` commands.\n\n**3. Activate the environment:**\n\n=== \"Linux/macOS\"\n ```bash\n source .venv/bin/activate\n ```\n\n=== \"Windows (PowerShell)\"\n ```powershell\n .venv\\Scripts\\Activate.ps1\n ```\n\n=== \"Windows (Command Prompt)\"\n ```cmd\n .venv\\Scripts\\activate.bat\n ```\n\n**4. Run commands:**\n\n```bash\nsleap\npytest tests/\n```\n\nOr without activating the environment:\n\n```bash\nuv run sleap\nuv run pytest tests/\n```\n\n### Use local dev as system tool\n\nWant to run your modified SLEAP from anywhere without activating a venv? Install from local source:\n\n```bash\nuv tool install --reinstall --python 3.13 \".[nn]\" --with \"../sleap-io[all]\" --with \"../sleap-nn\" --prerelease allow --torch-backend auto\n```\n\nNow you can run `sleap` from anywhere and it uses your local code!\n\nRe-run with `--reinstall` after making changes to pick them up.\n\n---\n\n## Programmatic Usage\n\nThe `sleap` package is primarily the GUI application. For scripting and automation, use these libraries:\n\n| Library | Use for | Docs |\n|---------|---------|------|\n| **sleap-io** | Working with `.slp` files, labels, skeletons, videos, merging projects, custom analysis | [io.sleap.ai](https://io.sleap.ai) |\n| **sleap-nn** | Training models, running inference, evaluating predictions, batch processing | [nn.sleap.ai](https://nn.sleap.ai) |\n\n---\n\n## Pip Installation\n\nFor users who prefer pip over uv, or need to integrate SLEAP into an existing environment.\n\n### Create a conda environment\n\n```bash\nconda create -n sleap_env\nconda activate sleap_env\n```\n\n### Install with pip\n\n```bash\n# CPU only\npip install \"sleap[nn]\" --extra-index-url https://download.pytorch.org/whl/cpu\n\n# NVIDIA GPU (CUDA 12.8)\npip install \"sleap[nn]\" --extra-index-url https://download.pytorch.org/whl/cu128\n```\n\n---\n\n## Model Export (ONNX)\n\nTo export trained models to ONNX format for deployment, you need additional dependencies.\n\n[:octicons-arrow-right-24: Learn more about exporting models](https://nn.sleap.ai/latest/guides/export/)\n\n### Install export dependencies\n\nIf you installed SLEAP as a tool:\n\n```bash\n# Add ONNX export support (CPU runtime)\nuv tool install --python 3.13 \"sleap[nn,nn-export]==1.6.1\" --with \"sleap-io==0.6.4\" --with \"sleap-nn==0.1.0\" --torch-backend auto\n\n# Add ONNX export support (GPU runtime - faster inference)\nuv tool install --python 3.13 \"sleap[nn,nn-export-gpu]==1.6.1\" --with \"sleap-io==0.6.4\" --with \"sleap-nn==0.1.0\" --torch-backend auto\n```\n\nIf you're using a development setup:\n\n```bash\n# CPU ONNX runtime\nuv sync --extra nn --extra nn-export\n\n# GPU ONNX runtime (for faster inference)\nuv sync --extra nn --extra nn-export-gpu\n```\n\n### TensorRT (Linux/Windows only)\n\nFor NVIDIA TensorRT support on Linux or Windows:\n\n```bash\n# Development setup\nuv sync --extra nn-cuda128 --extra nn-tensorrt\n\n# Tool install\nuv tool install --python 3.13 \"sleap[nn,nn-tensorrt]==1.6.1\" --with \"sleap-io==0.6.4\" --with \"sleap-nn==0.1.0\" --torch-backend cu128\n```\n\n!!! note\n TensorRT is not supported on macOS.\n\n\n---\n\n## Troubleshooting\n\n**First step:** Run `sleap doctor` and check the output for errors.\n\n??? note \"`--torch-backend` not recognized\"\n Update uv to the latest version:\n ```bash\n uv self update\n ```\n\n??? note \"Force a clean reinstall\"\n If something is broken:\n ```bash\n uv tool install --reinstall --python 3.13 \"sleap[nn]==1.6.1\" --with \"sleap-io==0.6.4\" --with \"sleap-nn==0.1.0\" --torch-backend auto\n ```\n\n??? note \"Installation seems stuck\"\n Large packages like PyTorch take time. Installation can take 5-15 minutes on slower connections. Wait up to 30 minutes before cancelling.\n\n??? note \"GPU not detected\"\n If `sleap doctor` shows no GPU:\n\n 1. **Check driver**: Run `nvidia-smi`. If it fails, [install drivers](https://www.nvidia.com/drivers)\n 2. **Driver version**: CUDA 12.8 requires driver 525+\n 3. **Try explicit backend**: Use `--torch-backend cu128` instead of `auto`\n\n**Still stuck?** Run `sleap doctor`, copy output, and ask at [GitHub Discussions](https://github.com/talmolab/sleap/discussions)\n" }, { "path": "docs/learnings/gui.md", "content": "# Using the GUI\n\nThe SLEAP labeling interface is launched via the `sleap label` command (or legacy `sleap-label`). See [Command Line Interfaces](../reference/command-line-interfaces.md) for details.\n\n!!! tip \"Keyboard shortcuts\"\n Most menu commands have keyboard shortcuts. View and customize them via **Help ā†’ Keyboard Shortcuts**.\n\n---\n\n## Menus\n\n### File\n\n| Command | Description |\n|---------|-------------|\n| **New...** | Create a new project |\n| **Open...** | Open an existing project |\n| **Save** / **Save As...** | Save the current project |\n| **Import...** | Import from external formats: [COCO] (`.json`), [DeepLabCut] (`.csv`), [DeepPoseKit] (`.h5`), [LEAP] (`.mat`) |\n| **Merge Data From...** | Copy labels/predictions from another SLEAP project into the current one |\n| **Add Videos...** | Add videos to the current project |\n| **Replace Videos...** | Swap videos with copies at different paths (useful for moving between drives) |\n\n### Go\n\n| Command | Description |\n|---------|-------------|\n| **Next Labeled Frame** | Jump to next frame with any labels (user or predicted) |\n| **Next User Labeled Frame** | Jump to next frame with user labels only |\n| **Next Suggestion** | Jump to the next suggested frame |\n| **Next Track Spawn Frame** | Jump to where a new track starts (useful for proofreading) |\n| **Next Video** | Switch to the next video in the project |\n| **Go to Frame...** | Jump to a specific frame number |\n| **Select to Frame...** | Select a clip from current frame to a specified frame |\n\n### View\n\n| Command | Description |\n|---------|-------------|\n| **Color Predicted Instances** | Toggle coloring predictions by track (enable for proofreading) |\n| **Color Palette** | Choose colors for instances (see note below) |\n| **Apply Distinct Colors To** | Color by tracks, nodes, or edges |\n| **Show Instances** | Toggle visibility of all instances |\n| **Show Non-Visible Nodes** | Toggle visibility of occluded/missing nodes |\n| **Show Node Names** | Toggle node name labels |\n| **Show Edges** | Toggle edge visibility |\n| **Edge Style** | Lines or wedges (wedges show orientation) |\n| **Trail Length** | Show instance movement trails (useful for detecting swaps) |\n| **Fit Instances to View** | Auto-zoom to instances in each frame |\n| **Seekbar Header** | Plot information above the seekbar |\n| **Crop Size Overlay** | Show the crop region for top-down training pipelines |\n\n!!! note \"Color palettes\"\n - **\"alphabet\"** has 26 visually distinct colors\n - Palettes ending with **\"+\"** don't cycle (useful for proofreadingā€”\"five+\" shows track 4+ as orange)\n - Customize palettes in `~/.sleap/colors.yaml`\n\n### Labels\n\n| Command | Description |\n|---------|-------------|\n| **Add Instance** | Add an instance to the current frame |\n| **Instance Placement Method** | Choose how new instances are positioned (\"Best\" uses predictions first) |\n| **Delete Instance** | Delete the selected instance |\n| **Set Instance Track** | Assign selected instance to a different track |\n| **Propagate Track Labels** | Apply track changes to all subsequent frames |\n| **Transpose Instance Tracks** | Swap tracks between two instances |\n| **Delete Instance and Track** | Delete all instances in a track across all frames |\n| **Custom Instance Delete...** | Delete instances matching specific criteria |\n| **Select Next Instance** | Cycle through instances in the frame |\n| **Clear Selection** | Deselect the current instance |\n\n### Predict\n\n| Command | Description |\n|---------|-------------|\n| **Run Training...** | Train models from your labeled data |\n| **Run Inference...** | Generate predictions using trained models |\n| **Evaluate Metrics for Trained Models...** | View recall, precision, and other metrics |\n| **Add Instances from All Predictions on Current Frame** | Convert all predictions to editable instances |\n| **Delete All Predictions...** | Remove all predictions from current video |\n| **Delete All Predictions from Clip...** | Remove predictions from selected frame range |\n| **Delete All Predictions from Area...** | Remove predictions within a rectangular region |\n| **Delete All Predictions with Low Score...** | Remove predictions below a score threshold |\n| **Delete All Predictions beyond Frame Limit...** | Keep only top N predictions per frame |\n| **Delete Predictions on User-Labeled Frames** | Remove predictions from frames with user labels |\n| **Export Video with Visual Annotations...** | Export video with poses overlaid |\n\n!!! tip \"Random sample (current video)\"\n When running inference, you can now choose \"Random sample (current video)\" to predict on a random subset of frames from just the current video.\n\n### Analyze\n\n| Command | Description |\n|---------|-------------|\n| **Instance Size Distribution...** | View bounding box size distribution (helps choose crop sizes) |\n| **Label QC...** | Open the [Label Quality Control](../guides/label-quality-control.md) panel to find annotation errors |\n\n### Help\n\n| Command | Description |\n|---------|-------------|\n| **Keyboard Shortcuts** | View and customize shortcuts |\n| **Check for Updates** | Check GitHub for newer SLEAP versions and shows a dialog with release notes if an update is available |\n\n---\n\n## Analysis Widgets\n\n### Instance Size Distribution\n\nAccess via **Analyze ā†’ Instance Size Distribution...** or the toolbar.\n\nThis widget shows the distribution of instance bounding box sizes in your labeled data. Use it to:\n\n- **Choose crop sizes** for top-down modelsā€”crop size should encompass most instances\n- **Identify outliers** that may indicate labeling errors\n- **Verify consistency** across your labeled frames\n\nFor a detailed walkthrough including rotation augmentation, statistics interpretation, and programmatic access, see [Instance Size Distribution Guide](../guides/instance-size-distribution.md).\n\n### Crop Size Overlay\n\nEnable via **View ā†’ Crop Size Overlay**.\n\nShows the crop region that will be used for top-down training pipelines, similar to the receptive field overlay. Helps verify your crop size setting captures the full animal.\n\n### Label QC\n\nAccess via **Analyze ā†’ Label QC...**\n\nThis panel uses statistical anomaly detection to automatically find potential labeling errors in your dataset. It flags instances with unusual edge lengths, joint angles, node spacing, or other features that deviate from the norm.\n\nUse it to:\n\n- **Catch errors before training**ā€”find labeling mistakes early\n- **Verify proofreading**ā€”check tracking corrections after proofreading\n- **Identify systematic issues**ā€”see patterns in labeling errors\n\nFor a complete guide including sensitivity tuning, issue types, and programmatic access, see [Label Quality Control Guide](../guides/label-quality-control.md).\n\n---\n\n## Mouse Controls\n\n| Action | How |\n|--------|-----|\n| Zoom in/out | Mouse wheel on image |\n| Pan image | Left-click + drag |\n| Toggle node visibility | Right-click on node |\n| Add instance | Right-click elsewhere on image |\n| Zoom to region | Alt + left-click + drag |\n| Zoom out | Alt + double-click |\n| Move entire instance | Alt + drag on node |\n| Rotate entire instance | Alt + mouse wheel on node |\n| Create instance from prediction | Double-click on predicted instance |\n| Add missing nodes to instance | Double-click on editable instance |\n| Select instance | Click on instance |\n| Clear selection | Click elsewhere |\n| Duplicate instance | Ctrl + drag on instance |\n\n!!! note \"macOS\"\n Substitute **Option** for **Alt** and **Command** for **Control**.\n\n---\n\n## Keyboard Navigation\n\n| Key | Action |\n|-----|--------|\n| **ā†’** / **ā†** | Move one frame forward/back |\n| **Ctrl + ā†’** / **ā†** | Move medium step (4 frames) |\n| **Ctrl + Alt + ā†’** / **ā†** | Move large step (100 frames) |\n| **Home** / **End** | First / last frame |\n| **Shift + navigation** | Select frames while moving |\n| **1-9** | Select instance by number |\n| **Ctrl (hold)** | Show tracks legend |\n| **Escape** | Deselect all |\n\n---\n\n## Seekbar Controls\n\n| Action | How |\n|--------|-----|\n| Select frame range | Shift + drag |\n| Clear selection | Shift + click |\n| Zoom to range | Alt + drag |\n| Zoom out (show all) | Alt + click |\n\n---\n\n## Labeling Suggestions\n\nGenerate suggested frames for labeling via **Labeling Suggestions** panel:\n\n| Method | Description |\n|--------|-------------|\n| **Sample** | Evenly spaced (\"stride\") or random frames from each video |\n| **Image Features** | Visually distinctive frames for training diversity (slower) |\n| **Prediction Score** | Frames with low-confidence predictions (for proofreading) |\n| **Velocity** | Frames where instances move unusually fast (may indicate tracking errors) |\n\n---\n\n[coco]: http://cocodataset.org/#format-data\n[deeplabcut]: http://deeplabcut.org\n[deepposekit]: http://deepposekit.org\n[leap]: https://github.com/talmo/leap\n" }, { "path": "docs/learnings/index.md", "content": "# Learning\n\nDeep dive into SLEAP concepts, workflows, and best practices. These guides go beyond the tutorial to help you get the most out of SLEAP.\n\n---\n\n## Understanding SLEAP\n\n
\n\n- **System Overview**\n\n ---\n\n High-level architecture and how SLEAP components work together.\n\n [:octicons-arrow-right-24: Read more](system-overview.md)\n\n- **Using the GUI**\n\n ---\n\n Comprehensive guide to the SLEAP labeling interface and all its features.\n\n [:octicons-arrow-right-24: Read more](gui.md)\n\n
\n\n---\n\n## Training & Models\n\n
\n\n- **Model Configuration**\n\n ---\n\n Model approaches (top-down vs bottom-up), backbone networks, and hyperparameters.\n\n [:octicons-arrow-right-24: sleap-nn docs](https://nn.sleap.ai/latest/reference/models/)\n\n- **Skeleton Design**\n\n ---\n\n Tips for designing effective skeletons for your animals.\n\n [:octicons-arrow-right-24: Read more](skeleton-design.md)\n\n
\n\n---\n\n## Workflows\n\n
\n\n- **Prediction-Assisted Labeling**\n\n ---\n\n Use model predictions to speed up your labeling workflow.\n\n [:octicons-arrow-right-24: Read more](prediction-assisted-labeling.md)\n\n
\n\n---\n\n## Troubleshooting\n\n
\n\n- **Common Tracking Mistakes**\n\n ---\n\n Understand and fix common issues in multi-animal tracking.\n\n [:octicons-arrow-right-24: Read more](main-mistakes-by-tracking.md)\n\n- **Troubleshooting Workflows**\n\n ---\n\n Diagnose and resolve issues in your SLEAP pipeline.\n\n [:octicons-arrow-right-24: Read more](../help.md#troubleshooting-workflows)\n\n
\n" }, { "path": "docs/learnings/main-mistakes-by-tracking.md", "content": "# Tracking Mistakes\n\nOnce you have predicted tracks, you'll need to proofread them to ensure instance identities are correct across frames.\n\n!!! tip \"Visualizing tracks\"\n By default, predicted instances appear in grey and yellow. Select **\"Color Predicted Instances\"** to show tracks in color. Colors in the frame match colors in the seekbar and the **Instances** panel.\n\n---\n\n## Types of Tracking Errors\n\nThere are two main types of mistakes made by the tracking code:\n\n1. **Mistaken Identities** ā€” The tracker misidentifies which instance belongs to which track (two animals get \"swapped\")\n\n2. **Lost Identities** ā€” The tracker fails to link an instance to any previous track, creating a new track unexpectedly\n\n---\n\n## Fixing Mistaken Identities\n\nWhen two instances are assigned to the wrong tracks, you need to **swap** their identities.\n\n### Transpose Tracks\n\nUse **Labels ā†’ Transpose Instance Tracks** to swap identities:\n\n- If there are exactly 2 instances in the frame, they swap automatically\n- If there are more, click the two instances you want to swap\n\n![Fixing track identities](../assets/images/fixing-track.gif)\n\n### Assign to Different Track\n\nUse **Labels ā†’ Set Instance Track** to assign an instance to a different (or new) track.\n\n!!! note \"Propagation\"\n When you assign an instance to a track, this change applies to all **subsequent frames**. For example, moving an instance from Track 3 to Track 2 will also move all Track 3 instances in later frames to Track 2ā€”effectively \"merging\" the tracks.\n\n---\n\n## Fixing Lost Identities\n\nWhen a new track is unexpectedly spawned, find where it started and reassign it to the correct track.\n\n1. Use **Labels ā†’ Next Track Spawn Frame** to jump to the next frame where a new track begins\n2. Select the instance and assign it to the correct track using **Labels ā†’ Set Instance Track**\n\n---\n\n## Quick Reference\n\n| Action | How To |\n|--------|--------|\n| Select instance | Click in frame, click in Instances panel, or press `1`-`9` |\n| View track name | Click an instance to see its track |\n| Rename track | Double-click track name in Instances panel |\n| Swap two tracks | **Labels ā†’ Transpose Instance Tracks** |\n| Change track | **Labels ā†’ Set Instance Track** |\n| Find new tracks | **Labels ā†’ Next Track Spawn Frame** |\n\n---\n\nFor more tools and tips, see the [Proofreading guide](../guides/tracking-and-proofreading.md).\n" }, { "path": "docs/learnings/prediction-assisted-labeling.md", "content": "# Prediction-Assisted Labeling\n\nPrediction-assisted labeling accelerates your workflow by using model predictions to bootstrap new labels.\n\n**Benefits:**\n\n- **Faster labeling** ā€” Correcting a mostly-correct prediction is quicker than labeling from scratch\n- **Targeted feedback** ā€” See where your model succeeds and fails, helping you choose the most useful frames to label\n\n---\n\n## The Active Learning Loop\n\n```\nā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”\nā”‚ ā”‚\nā”‚ Label frames ā†’ Train model ā†’ Predict ā†’ Correct ā”‚\nā”‚ ā†‘ ā”‚ ā”‚\nā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā”‚\nā”‚ ā”‚\nā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜\n```\n\n1. **Label** a small set of frames manually\n2. **Train** a model on your labels\n3. **Predict** on suggested or unlabeled frames\n4. **Correct** the predictions to create new training data\n5. **Repeat** until accuracy is satisfactory\n\n---\n\n## Understanding the Seekbar\n\nAfter running inference, the seekbar shows different markers:\n\n| Marker | Meaning |\n|--------|---------|\n| **Thick black line** | Manually labeled frame |\n| **Thin black line** | Frame with predictions |\n| **Dark blue line** | Suggested frame with manual labels |\n| **Light blue line** | Suggested frame with predictions |\n| **Red marker** | Suggested frame ready for review |\n\n---\n\n## Correcting Predictions\n\nPredicted instances appear in **grey with yellow nodes**. To edit them:\n\n1. **Double-click** the predicted instance to convert it to an editable instance\n2. Adjust the nodes as needed\n3. Save your changes\n\n![Fixing predictions](../assets/images/fixing-predictions.gif)\n\n!!! tip \"Visual feedback\"\n After converting a prediction:\n\n - **Red nodes** = unchanged from prediction\n - **Green nodes** = manually adjusted\n\n This helps you track which nodes you've reviewed.\n\n!!! warning \"Predictions don't train automatically\"\n Predicted instances are **not** used for training until you convert and correct them. Always double-click to convert before making edits.\n\n---\n\n## Best Practices\n\n1. **Generate new suggestions regularly** ā€” Active learning works best with fresh suggestions based on your latest model\n\n2. **Focus on failure cases** ā€” Prioritize frames where predictions are wrong or uncertain\n\n3. **Don't over-correct** ā€” If a prediction is close enough, a small adjustment is fine\n\n4. **Iterate frequently** ā€” Several rounds of train ā†’ predict ā†’ correct typically yields better results than one large labeling session\n\n---\n\n## Next Steps\n\nOnce you have accurate frame-by-frame predictions, you're ready to:\n\n- **Run inference on full videos** ā€” Predict poses across entire clips\n- **Track identities** ā€” Link instances across frames (see [Tracking methods](../guides/tracking-and-proofreading.md#tracking-methods))\n- **Proofread tracks** ā€” Use the proofreading tools to fix tracking errors\n\n" }, { "path": "docs/learnings/skeleton-design.md", "content": "# Skeleton Design\n\nA well-designed skeleton is crucial for accurate pose estimation. This guide covers best practices for creating effective skeletons in SLEAP.\n\n---\n\n## Skeleton Components\n\n| Component | Description | Role |\n|-----------|-------------|------|\n| **Nodes** | Body part landmarks (e.g., \"nose\", \"tail_base\") | Define what points to track |\n| **Edges** | Connections between nodes | Visualization + bottom-up grouping |\n\n!!! note \"Node naming\"\n Node names are for your reference onlyā€”SLEAP uses their order internally. Choose descriptive names that make labeling intuitive.\n\n---\n\n## Design Guidelines\n\n### Choosing Nodes\n\nChoose nodes that will be easy to locate in new images. It's important to be as consistent as possible about the relative placement of body parts.\n\n### Designing Edges\n\nEdges connect nodes into a graph structure. For most models, edges are primarily for **visualization**. However, for **bottom-up models** (using part affinity fields), edges are critical and must connect all nodes to one another.\n\n!!! tip \"Shallow trees work best\"\n Create a shallow hierarchy with few parent nodes. If a parent node isn't detected, its children can't be grouped correctly in bottom-up inference.\n\n**Good structure:**\n```\n head\n / | \\\n nose ear ear\n |\n spine\n / | \\\n leg leg tail\n```\n\n**Avoid deep chains:**\n```\nhead ā†’ neck ā†’ spine ā†’ hip ā†’ leg ā†’ foot ā†’ toe āŒ\n```\n\n---\n\n## Modifying Skeletons\n\n### Adding Nodes\n\n1. Add the new node(s) to your skeleton definition\n2. **Double-click** an existing instance (on the video frame) to edit it\n3. New nodes will be added and marked as \"non-visible\"\n4. **Right-click** each new node to make it visible, then drag to the correct location\n\n### Removing Nodes\n\nSimply delete the node from the skeleton. Existing instances will update automatically.\n\n!!! warning \"Part affinity fields\"\n If using part affinity fields (bottom-up inference), ensure your skeleton graph remains fully connected after removing nodes.\n\n### Modifying Edges\n\nAdd or remove edges freelyā€”changes apply to all instances automatically. Edges don't affect existing labels, only visualization and bottom-up inference.\n\n---\n\n## Examples\n\n### Mouse (simple)\n```\nNodes: nose, left_ear, right_ear, spine_mid, tail_base\nEdges: noseā†’left_ear, noseā†’right_ear, noseā†’spine_mid, spine_midā†’tail_base\n```\n\n### Fly (detailed)\n```\nNodes: head, thorax, abdomen, wing_L, wing_R, leg_L1, leg_L2, leg_L3, leg_R1, leg_R2, leg_R3\nEdges: headā†’thorax, thoraxā†’abdomen, thoraxā†’wing_L, thoraxā†’wing_R,\n thoraxā†’leg_L1, thoraxā†’leg_L2, thoraxā†’leg_L3,\n thoraxā†’leg_R1, thoraxā†’leg_R2, thoraxā†’leg_R3\n```\n\n---\n\n## Common Mistakes\n\n| Mistake | Problem | Solution |\n|---------|---------|----------|\n| Too many nodes | Slower labeling, harder training | Start with 5-10 essential nodes |\n| Deep edge chains | Poor grouping in bottom-up | Use shallow tree structure |\n| Ambiguous landmarks | Inconsistent labels | Choose anatomically distinct points |\n| Symmetric naming confusion | Mixing up left/right | Use clear prefixes: `L_`, `R_` or `left_`, `right_` |\n" }, { "path": "docs/learnings/system-overview.md", "content": "# SLEAP System Overview\n\nSLEAP is built as three interconnected packages that work together to provide a complete pose estimation workflow.\n\n---\n\n## Package Architecture\n\n```\nā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”\nā”‚ sleap ā”‚\nā”‚ GUI, CLI entry points, quality control, glue ā”‚\nā”œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”¤\nā”‚ ā”‚ ā”‚\nā”‚ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”“ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚\nā”‚ ā–¼ ā–¼ ā”‚\nā”‚ ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”Œā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā” ā”‚\nā”‚ ā”‚ sleap-io ā”‚ ā”‚ sleap-nn ā”‚ ā”‚\nā”‚ ā”‚ ā”‚ ā”‚ ā”‚ ā”‚\nā”‚ ā”‚ Data model ā”‚ ā”‚ PyTorch models ā”‚ ā”‚\nā”‚ ā”‚ File I/O ā”‚ā—„ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā–ŗā”‚ Training ā”‚ ā”‚\nā”‚ ā”‚ Format convert ā”‚ ā”‚ Inference ā”‚ ā”‚\nā”‚ ā”‚ Video backends ā”‚ ā”‚ Tracking ā”‚ ā”‚\nā”‚ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜ ā”‚\nā””ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”€ā”˜\n```\n\n### sleap\n\nThe main package providing:\n\n- **Labeling GUI** (`sleap label`) ā€” Annotate keypoints, manage projects, review predictions\n- **Unified CLI** (`sleap`) ā€” Single entry point for all commands\n- **Quality Control** ā€” Detect annotation errors\n- **Integration layer** ā€” Connects sleap-io and sleap-nn\n\n### sleap-io\n\nData handling and I/O operations:\n\n- **Data model** ā€” Labels, Videos, Skeletons, Instances, Tracks\n- **File formats** ā€” Native `.slp`, plus import/export for COCO, NWB, DeepLabCut, and more\n- **Video backends** ā€” Read frames from MP4, AVI, HDF5, image sequences\n- **Utilities** ā€” Merging, rendering, transformations\n\nDocumentation: [io.sleap.ai](https://io.sleap.ai)\n\n### sleap-nn\n\nNeural network backend (PyTorch):\n\n- **Model architectures** ā€” Top-down, bottom-up, single-instance, centered-instance\n- **Backbone networks** ā€” UNet, ConvNeXt, SwinT, and pretrained options\n- **Training** ā€” Data augmentation, loss functions, optimization, logging (WandB, TensorBoard)\n- **Inference** ā€” Pose prediction, peak finding, multi-instance grouping\n- **Tracking** ā€” Identity tracking across frames\n\nDocumentation: [nn.sleap.ai](https://nn.sleap.ai)\n\n---\n\n## Workflow Stages\n\n### 1. Data Preparation\n\n| Step | Tool | Description |\n|------|------|-------------|\n| Import videos | GUI or `sleap-io` | Load MP4, AVI, HDF5, or image sequences |\n| Create skeleton | GUI | Define nodes and edges for your animal |\n| Label frames | GUI | Annotate keypoints on training frames |\n| Import existing labels | GUI or `sleap-io` | Convert from COCO, DLC, LEAP formats |\n\n### 2. Training\n\n| Step | Tool | Description |\n|------|------|-------------|\n| Configure model | GUI or config file | Choose model type, backbone, hyperparameters |\n| Train | GUI or `sleap nn-train` | Train on labeled data with augmentation |\n| Monitor | TensorBoard or WandB | Track loss, metrics, visualizations |\n| Evaluate | GUI | Check precision, recall on validation data |\n\n### 3. Inference\n\n| Step | Tool | Description |\n|------|------|-------------|\n| Run inference | GUI or `sleap nn-track` | Predict poses on new videos |\n| Track identities | Automatic | Link instances across frames |\n| Proofread | GUI | Fix tracking errors |\n| Export | GUI or `sleap convert` | Save to HDF5, CSV, NWB, COCO, etc. |\n\n---\n\n## CLI Commands\n\nThe unified `sleap` CLI provides access to all functionality:\n\n```bash\nsleap label [FILE] # Launch GUI\nsleap doctor # Check installation\nsleap nn-train ... # Train models (sleap-nn)\nsleap nn-track ... # Run inference (sleap-nn)\nsleap convert ... # Convert formats (sleap-io)\nsleap show ... # Inspect files (sleap-io)\n```\n\nSee [Command Line Interfaces](../reference/command-line-interfaces.md) for full documentation.\n\n---\n\n## Legacy Architecture\n\n!!! note \"Historical context\"\n Earlier versions of SLEAP (pre-1.5) used TensorFlow for training and inference. The diagram below shows this legacy architecture for reference. Current versions use **sleap-nn** (PyTorch-based) for all neural network operations.\n\n![Legacy SLEAP System](../assets/images/systemOverview.png)\n" }, { "path": "docs/notebooks/Analysis_examples.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n },\n \"source\": [\n \"\\\"Open\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Analysis examples\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"XdoDboCtNhUB\"\n },\n \"source\": \"In this notebook we'll show examples of how you might use the predictions exported from SLEAP. We'll work with an [Analysis HDF5](../tutorial/exporting-the-results.md#analysis-hdf5) file (rather than the `.slp` predictions file). This HDF5 file can be exported from SLEAP.\\n\\nWe advise building your post-SLEAP analysis pipeline around these HDF5 files rather than trying to work directly with the `.slp` files used by SLEAP.\\n\\n**Note**: You can work with these HDF5 directly in Python (as we'll do here) or MATLAB without having SLEAP itself installed.\"\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"cZQoiD1YTqdx\"\n },\n \"source\": [\n \"## Example analysis data\\n\",\n \"\\n\",\n \"Let's start by download a sample HDF5. These predictions were created with models trained on our [sample Drosophila melanogaster courtship dataset](https://github.com/talmolab/sleap-datasets). Using these models we can inference on a video clip with 3000 frames. The video clip, resulting predictions, and exported HDF5 are all available [here](https://github.com/talmolab/sleap/tree/main/docs/notebooks/analysis_example).\\n\",\n \"\\n\",\n \"We'll just download the `predictions.analysis.h5` file:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"aPUQm-BhfD1W\",\n \"outputId\": \"83a94815-95a2-4dd5-b1c5-f48aa779b81f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"--2021-01-04 03:24:05-- https://github.com/talmolab/sleap/raw/main/docs/notebooks/analysis_example/predictions.analysis.h5\\n\",\n \"Resolving github.com (github.com)... 140.82.113.4\\n\",\n \"Connecting to github.com (github.com)|140.82.113.4|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 302 Found\\n\",\n \"Location: https://raw.githubusercontent.com/talmolab/sleap/main/docs/notebooks/analysis_example/predictions.analysis.h5 [following]\\n\",\n \"--2021-01-04 03:24:05-- https://raw.githubusercontent.com/talmolab/sleap/tree/main/docs/notebooks/analysis_example/predictions.analysis.h5\\n\",\n \"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\\n\",\n \"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 551501 (539K) [application/octet-stream]\\n\",\n \"Saving to: ā€˜predictions.analysis.h5ā€™\\n\",\n \"\\n\",\n \"predictions.analysi 100%[===================>] 538.58K --.-KB/s in 0.05s \\n\",\n \"\\n\",\n \"2021-01-04 03:24:05 (10.5 MB/s) - ā€˜predictions.analysis.h5ā€™ saved [551501/551501]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"!wget -O predictions.analysis.h5 https://github.com/talmolab/sleap/raw/main/docs/notebooks/analysis_example/predictions.analysis.h5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ZaQ_poYbffRz\"\n },\n \"source\": [\n \"We can set the path and filename to the analysis HDF5. In our case, this is just `predictions.analysis.h5`. If you're running analysis code on your local computer this will be the full path and filename of your HDF5. If you're running analysis code on Colab, then you'll probably copy files to Colab via Google Drive and should use the path to your file there.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"id\": \"ppJTyt2zgD8f\"\n },\n \"outputs\": [],\n \"source\": [\n \"filename = \\\"predictions.analysis.h5\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"u7r5L5WVgKUV\"\n },\n \"source\": [\n \"## Loading the data\\n\",\n \"\\n\",\n \"We use the [h5py](https://www.h5py.org) package to load data from the HDF5. This is already installed in Colab. If your running analysis code on your local machine and have SLEAP installed, then `h5py` and other packages we use are already installed in your SLEAP conda environment. Otherwise, you may need to use `uv` or `pip` to install `h5py` as well as `numpy`, `scipy`, `matplotlib`, `seaborn`, and any other packages you want use in your analysis code.\\n\",\n \"\\n\",\n \"Let's load the file and take a peek.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"Sua2AKkTM8PN\",\n \"outputId\": \"3c6179d5-6b55-4bb9-e839-38941bb9cce8\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"===filename===\\n\",\n \"predictions.analysis.h5\\n\",\n \"\\n\",\n \"===HDF5 datasets===\\n\",\n \"['node_names', 'track_names', 'track_occupancy', 'tracks']\\n\",\n \"\\n\",\n \"===locations data shape===\\n\",\n \"(3000, 13, 2, 2)\\n\",\n \"\\n\",\n \"===nodes===\\n\",\n \"0: head\\n\",\n \"1: thorax\\n\",\n \"2: abdomen\\n\",\n \"3: wingL\\n\",\n \"4: wingR\\n\",\n \"5: forelegL4\\n\",\n \"6: forelegR4\\n\",\n \"7: midlegL4\\n\",\n \"8: midlegR4\\n\",\n \"9: hindlegL4\\n\",\n \"10: hindlegR4\\n\",\n \"11: eyeL\\n\",\n \"12: eyeR\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"import h5py\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"with h5py.File(filename, \\\"r\\\") as f:\\n\",\n \" dset_names = list(f.keys())\\n\",\n \" locations = f[\\\"tracks\\\"][:].T\\n\",\n \" node_names = [n.decode() for n in f[\\\"node_names\\\"][:]]\\n\",\n \"\\n\",\n \"print(\\\"===filename===\\\")\\n\",\n \"print(filename)\\n\",\n \"print()\\n\",\n \"\\n\",\n \"print(\\\"===HDF5 datasets===\\\")\\n\",\n \"print(dset_names)\\n\",\n \"print()\\n\",\n \"\\n\",\n \"print(\\\"===locations data shape===\\\")\\n\",\n \"print(locations.shape)\\n\",\n \"print()\\n\",\n \"\\n\",\n \"print(\\\"===nodes===\\\")\\n\",\n \"for i, name in enumerate(node_names):\\n\",\n \" print(f\\\"{i}: {name}\\\")\\n\",\n \"print()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"2gDkifEXhyoW\"\n },\n \"source\": [\n \"In our example file, the shape of the locations matrix (the `tracks` dataset) is (3000, 13, 2, 2) **after it is transposed** (with the `.T`). We transpose the data when loading it in Python; no transpose is needed when using MATLAB. This is because Python and MATLAB expect matrices to be stored differently.\\n\",\n \"\\n\",\n \"Here's what each dimension of the matrix means:\\n\",\n \"\\n\",\n \"- 3000: the number of frames;\\n\",\n \"\\n\",\n \"- 13: the number of nodes in the skeleton (we've also loaded and displayed the `node_names` dataset with the names of these 13 nodes);\\n\",\n \"\\n\",\n \"- 2: for the x and y coordinates;\\n\",\n \"\\n\",\n \"- 2: the number of distinct animal identities which were found (we have 2 flies in the video clip and they were tracked perfectly, so we ended up with exactly 2 track, but there may be more tracks than animals if tracking didn't work as well).\\n\",\n \"\\n\",\n \"We can get these counts from the shape of the matrix, like so:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"WA5Jxw1_M8PQ\",\n \"outputId\": \"a3a31de2-28a3-4b69-8533-1c5d567aae5f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"frame count: 3000\\n\",\n \"node count: 13\\n\",\n \"instance count: 2\\n\"\n ]\n }\n ],\n \"source\": [\n \"frame_count, node_count, _, instance_count = locations.shape\\n\",\n \"\\n\",\n \"print(\\\"frame count:\\\", frame_count)\\n\",\n \"print(\\\"node count:\\\", node_count)\\n\",\n \"print(\\\"instance count:\\\", instance_count)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"2JKWBGbqjoH6\"\n },\n \"source\": [\n \"Now that we've loaded the data, let's see some different things we can do with it...\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"sS28-dipM8PS\"\n },\n \"source\": [\n \"## Fill missing values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"id\": \"96mH1IPbM8PS\"\n },\n \"outputs\": [],\n \"source\": [\n \"from scipy.interpolate import interp1d\\n\",\n \"\\n\",\n \"def fill_missing(Y, kind=\\\"linear\\\"):\\n\",\n \" \\\"\\\"\\\"Fills missing values independently along each dimension after the first.\\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Store initial shape.\\n\",\n \" initial_shape = Y.shape\\n\",\n \"\\n\",\n \" # Flatten after first dim.\\n\",\n \" Y = Y.reshape((initial_shape[0], -1))\\n\",\n \"\\n\",\n \" # Interpolate along each slice.\\n\",\n \" for i in range(Y.shape[-1]):\\n\",\n \" y = Y[:, i]\\n\",\n \"\\n\",\n \" # Build interpolant.\\n\",\n \" x = np.flatnonzero(~np.isnan(y))\\n\",\n \" f = interp1d(x, y[x], kind=kind, fill_value=np.nan, bounds_error=False)\\n\",\n \"\\n\",\n \" # Fill missing\\n\",\n \" xq = np.flatnonzero(np.isnan(y))\\n\",\n \" y[xq] = f(xq)\\n\",\n \" \\n\",\n \" # Fill leading or trailing NaNs with the nearest non-NaN values\\n\",\n \" mask = np.isnan(y)\\n\",\n \" y[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), y[~mask])\\n\",\n \"\\n\",\n \" # Save slice\\n\",\n \" Y[:, i] = y\\n\",\n \"\\n\",\n \" # Restore to initial shape.\\n\",\n \" Y = Y.reshape(initial_shape)\\n\",\n \"\\n\",\n \" return Y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"id\": \"-zadxB39M8PU\"\n },\n \"outputs\": [],\n \"source\": [\n \"locations = fill_missing(locations)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1dt3i6T9mHUh\"\n },\n \"source\": [\n \"## Visualize thorax movement across video\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"id\": \"ch0Yr25EM8PW\"\n },\n \"outputs\": [],\n \"source\": [\n \"HEAD_INDEX = 0\\n\",\n \"THORAX_INDEX = 1\\n\",\n \"ABDO_INDEX = 2\\n\",\n \"\\n\",\n \"head_loc = locations[:, HEAD_INDEX, :, :]\\n\",\n \"thorax_loc = locations[:, THORAX_INDEX, :, :]\\n\",\n \"abdo_loc = locations[:, ABDO_INDEX, :, :]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"id\": \"S3XvZKKJM8PZ\"\n },\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"id\": \"mMjBC4rCM8Pa\"\n },\n \"outputs\": [],\n \"source\": [\n \"sns.set('notebook', 'ticks', font_scale=1.2)\\n\",\n \"mpl.rcParams['figure.figsize'] = [15,6]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 831\n },\n \"id\": \"lZ9wU0jzM8Pb\",\n \"outputId\": \"ff2a6c7b-0854-4d4f-dbad-4b33fad198a0\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Thorax tracks')\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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50LdfqTESNuBnfkDQqC74A1Ylf6Fh8Q9JOeSyjux9Gd78LEfYJQmKvoqEqg5w9rxCdciue3tEDEocQQlzKHE4H63I30eWwsr1kL/kNxQAkBybyi+kPgQKbCndwpCb32PLunh+J/rGuY0R5hxPlHd7juJfHTMLhdKA91sPkOEVR+Mmk+3h87e/42brfc/OI2QwPiGd4QEJ/XqYQQoh+IkngIFBy5CMaaw6DomH4xIfRnGS8hdknFrNP7En2HlxMHgFEjbiZwoz3ObpzKXGj78E7MGWgwxJCiEtGWXMlz3/9CsVNZQBoFA3ToydS3VZLZnU2/7v6SQw6A1WtNQDE+kQS5R121sf/bgJ4XKg5iF9c/hB/3voy7x/qLkoWfSyRvCXlOgI9BubhoxBCiN6TJHCAdbZVU128BXdLOIERk08owHIx8gsdg5s5hNy9/yZv/xs9urcKIYQ4N81drfxz19vsKjsAwK0pc7kucQZajRa9Vo+qqvx1+7+paKlGq2iICgvjvvTbMBt7dhE9H0n+cbw4dwmNnc18cPgTSprK+bpkDy1drfxs6g/77DxCCCH6lySBA6yufC+gEJ/2P5dURU13cygjJi3m6K4XKT78Id7+w9Hq3QY6LCGEuCi12zp4ZuOfKWmuINEvloVj5hPtE9FjG0VReGjS/f0ei0ajwdfdmx+MvQOA/xxazX8Pf8In2Ru5Nmlmv59fCCHE+ZMkcACpqpP6ij1Y/BIvqQTwOJ3Bg+iR8zmy/S+UZK8mOvnWgQ5JCCEuCk6nk9r2egI9/dmQt4V/7n4HgPvSb+PquGloNJozHOHCuTl5DpsKt/PG/g+obK3h/jHzBzokIc5Zu62DIzV52J127E47ngYPvE0WInvRpVqIi4EkgQOovaUca2cjofHXDHQo/cbDEo5/2Hhqy3Zi7WggOGYmFr/4gQ5LCCEuGKfTiaIoveoq+fbBFXx8dD3Pz/5/vH3gQ/RaPY9M/gHpoYNvjLVOo+XBCffwqy+eZ23uVwwPiGdSeJpr+iIhLgYHK7PYUrSL7aV76bR3nbA+JTCJB8bdSZBnwABEJ0TfkyRwALU3dU/m7ukdNcCR9K+IYTeiKFrqyneTs+efMkZQCDFk1LbV88hnvwEFnpz2I5L8485qv23FewBY/OkzAPxg7J2DMgEEsHY2EmRv4cHkWSzNXMsL215F5+2ByeSF6nRg8UvEJ3g0Fv9ENBq57RCDj1N18vzXL9Nm6yDEHMikiHTGh3VXYV91ZB1FTWUcrsnh0bW/4/dX/UymSBGXBPk0HkCN1RkY3HwxuF3aFdU0Wj2RI75HSNyVHN35D4ozP8DLfxg6vfsZ91VVFbutDZ3OTZ4qCyEuOp/lfkWHvROAj49uOKsksLChlLqOBrxMFpo7W/A0uDM+bFR/h9orqqpi62ykomAjdWW7UFUHnsBUk4HNnVZWW7Vcq+1E77RSX7mP+sp9uJlDSEhfeMbhD532LgxaPRpl8HR5FZcGq8OG4SQV2LeX7KXN1sGNw2dxR+qNPdY9PHkhABlVR1my6e/8fN3vGROayvXDriLO99J+iC8ubZIEDhC7rZ3m+lyCoqYPmWpqeqOF2FELyNr+AiVZKwmKuRx3c2iPbezWNjK//hN2awsAikaH6rQfW6sQnXwrfmFjL3DkQgjRe1a7lY35W5kQnoaHwZ2N+VspbCg5oaDLt3XYOvm/zf9Ar9Xz+yt/hqfRA1QVk97ULzHabe20N5fidNgAFVVVQVVRUeHYBPIGowWjuz96oxetDfl0ddRRkbcea2cDABa/RAKjpmF08yVFq6dp+xscrDpCdrvC/JHXc13cFOoqdlOW/QkZW58jJGYG7sFpFDZV4G2yUN/RRG59AWtzN9HU2QyAVtEwM3YKC8fe3i/XLYaeVUfW8faBFTw+ZRFjv/VQZWXWWt49uBKL0ZPbUuaecv+UoCSenPYjPjz8KdtK9rCtZA8Lx8zn6vjpFyJ8IfqcJIEDpLE6A1QnPsGpAx3KBeVuCcPLf7jrybDFLxEVFeex/vdtTcWubRWNnsDIy7BbW6ir2Aeqk8LMZXh4R2LyCByoSxD9LL++iOe/foUfjLuTgoZiJkaMYWXWWm5NuQ5v06VXQElcurYU76LV2sbshMuxOx1szN/K4+uWcEfqjVybOAP9d1okrA4bL2x7lbqOBh6bsgh/D99+iUtVnVQVfkV7SxmNVRmoquPsdlQ0oDq7f1S0BEVfjndgyglDGp6+/CEOVmbx2r5lvHfoI/ZVZODn7sPVSd/DWrCWkuw1vLt3BWV2W4/93HQmtIqGRP84LEZP1uVtYkJEGiODhp3X9TZWZ1BZ+BUajQ7/sAn4how+r+OJi49TdfJJ9hcA7C4/5EoCd5cd4N2DK4mwhPDIZT845RyZx6UEJZESlER5SxX/t+kfvLLnfcItIYwITOyx3Wc5X5JZnY1DdTI2dCRTIscBYNAZ+uHqhDg3iqqq6kAH0ZdKS0uZOXMmGzZsIDw8fKDDOaWcPS/T2V5LypSfD5mWwONUp4OWhnwq8jfgsHeg0ejQaI2u98EvdCzegckoGh3Kse5AqtNBV0cdh7e9gMU3nvj0+wbyEkQ/uvfDn9Ju6zhh+dykK1kw+uYBiEiI3lNVlcfXLUFVVZ6b9RSKolDeXMkftrxEeUsVbjoTj055gJFBw3CqTo7W5vGfQ6s5XJPDrSlzmZc8p1/i6mipIHffa1g7G9DpPXDzDMb32NyuCoCiQUEBRXF9/lYVbaa2dDt6kzdmnzgCIyZj9AhAd4ZpfxxOB//e+x8OVB6muq2OEM9AfjLhHnILN/Nq3nZidFr0OgMTwtOI1Btx1BxCVRRGjH8QrcmHxZ8+Q217AylBScxJnMGY0JG9vt7WxkKO7vwHOoMnWq0Ba1czqdOfPqvhCGLwaepsJqsml5auNhyqg+0le+mwdfL7q35+2oq5GVVH+fWXLwAQYg7kgbF3Miwgnt98+RfKm6tYet1v0Wl71y5S3VbHjz5+mlHBw7kt5Xp0Gi07y/azPm8LjZ3NaDVajFpDj++z6xJncnfavHO7eCF66Uw5kbQEDoCh2BX02xSNFotfAha/hF7tY/IIJDDyMqoKv6S5LhuLX+KZdxQXFavDRrutA0VRuC/tNlZkfUZ9RyMApc0VAxydEGevpKmcosZSFo653fU5H2oJ5g+znmJl1md8kPkJf9j8Ii9ev4RVRz5nZdZaoPthR38lgLVluyjO+hBUlYhhNxAQcdlZfQdFjbiZqBG9fwCj1Wj5/rG5BL/I/5oXd73FExueA0Cn0fGTtJuoLtiIs+4gdrpbF1EdFBx6F73BzMMjZ/Pfon3srTzMoaojjA4eQWrwcOYkzugxXlB1dV+FNdkbqeto4KbYSTSUbKWufDcoGhLHPoDqtJO1/QXyD7xFeOJ1uFuk5P/Fory5kjXZG1mft6X7d/0dLdZWvE7TU2R32QEMWj3XD7uKDzI/4VdfPI9JZ6TT3sWdqTf1OgEECPTwY17ytXyQuYYDlVmu5SGegXwvfTYzYy8D4PO8zXTZrRysymJ9/hZuHTkXk87Y6/MJ0dckCRwAzbXZ3V1BAwdnpbfBLDTuampLd1BbukOSwEuQ1WEF4O5RNzMrYTpXx0/DoTp5a/9/WZ+3mdauNrLrCnh597s8OuUB16D8ho4mDFo9HgZ5ui8Gh8M1OQCkBg/vsdyg1XNrylzifWN4dvNS/rHzLfZXZDI2NJV7028l0KN/CoVVFW2m9OgqjO7+xKbedcEToCtiJxPjE8HvNy+loaOJK2ImERY7g+DIydi6WtDqTOj07lQWfkl10WY6Wsqh7igzVZXhZjc2tndxuPIw+ysP8/b+/6IBHHSPHXSoKoqioKpOV3pQULCJVKOOMJ9oUlNuxs0zCIDgmBlUFn5J1va/kDT+h3h6R1/Q90H0XllzJT///Fm67F0kByZyVdw0hvnHoVEUtpfu4997/0OLte20SWBFazVh5mBuSb6Oy2MmszF/C3vLM4j2juC6pJnnHNvV8dP4IHMNAP7uvtybdgtjQkf26FY6J3EGAMMC4vh/G//M7rIDTIkaf87nvJjZrW10tlVjcPPBYPI+5XbWzib0Bk8pCNjPJAkcAC31OWh1JnkKeQ40Wj1+oWOpKfkau7UNncFjoEMSfcju6C4CpD/2VFZRFHSKlitiJvFpzhesPLKWrcW7qeto4HB1DjE+EazL3cS/9/6HKO9wnpv11ECGL4TL1qJdhFtCCPLwP+n69NAUroqbyud5mwG4ftjV/ZYAdrXXU5r9MR7eUSSO+QEa7cCMS4r2ieAvs39FRvVRxoR2j4fX6kxodd8UvQmJnUlwzBU0VB7sLjyjKITaOxmvaHE4HWyryaeio4mMpioabV2M0GswKQoKoKLFAezpspFls5Nls0PrEf44QiHy2PHDEmbjFzqWrB1/pSRrJUkTfiTTVgxy20v20mXv4vdX/fyEapwh5u76AK1dbafc3+F0kN9QQkpgIoqiEOjhx/yRNzB/5A3nHZu3ycJzs55CVdXTFnwCSPKPw6Qzkl1b0GdJoKo6cTps2G1t1JXtxm5vB6ez+4GI6kRVHa6fUR2oTqer6FOP43y7dfWEhtYzjBr7zqiy071qbch3/ewVkIze6InFNwGf4O4xml3t9VQWbKS2bAfeQanEjVpw+nOL89Knn3zXXnst5eXlrtdOp5POzk7+/ve/c9VVV5GUlITRaESr/Sazf//990lKSnJt/8ILL/DBBx/Q0dFBeno6v/71rwkLu3SSJVV10liThdk3QZ5wnCP/sPFUF2+mvnI/gZGXDXQ4og/Znd0FKnSangUzon0iGB08glVHPnctq2ytZv6yB12vixpLqW2r77diGkKcDVVVWZe7iaN1+dyReuNpu1venz6fSK8wKlqrSfKP7Zd4muuyydn7KqhOoobPG7AE8DiT3tSjMuPJKIrmlMVbbjk2isDudNBlbUfr6ARUnA4bikaLRmPAjkJ1Zwuv719OVk0uyzI+5tEpD3wTg0cA4YnXUnz4vxRlLCM65Tb5Ph6knKqTrwq3k+gXe9LpGMwGTwCau1pPeYy1uV/R1NnM5Mj+qSwe5X129Sc0iga708FnuV/yWe6X6LV6rk+6ittGdlckLWwo5fF1v+OBtHlcFjaSxprD1JXtxmHvdCVzqOqx/ztxqo4TkjmN1ohGo+v+e1Y0KIoGRdEe+78CihblW+N9e1JO+uPJFignbvCdzU++3uI/DA9LOG1NJXR11NFUk0lt6Q44+PYJ2zZWHaSpJguvgOEnOZLoC32aBK5Zs6bH6zfffJOlS5cybdo017KXX36ZCRMmnHT/V155hY8//pi3336boKAgnn32WRYtWsRHH3102gG/F5P25lLs1hZ8gno/wF10czMHozd60dZYBJIEXlJsx6YD0Z3khuz21BvZX3kYgDifKPaUHzphm79se5XfXPlY/wYpxCnUttfz4s43OVR1lFBzELPOUDpeo9EwK6H/yss7HTYKDr2PVmciPu1/cDMH99u5LjSdRovOZAbMJ6wzAjHuPjwz4xHe2PcBn+V+Sae9q8c4LP+wCTRU7Ke+ch/N9TmYfeMxufsTHHPFgCfK4ht7yzOobK1h/sjrT7rebOzuDdRqPXVL4KbCHYRbQhgT0rf3XQ57F5UFX+B0dNHRWoWiKHh4ReF0WLFZm/ENGYPZN+5YAtZ9D3td0kzX+F+bw8aqo5+7KgW/sO0VAP657wNyj6xirMmARmvEJygVRfNNQgdKdyKn0aFodGg0ejRaPW7mEMw+/fMwqb84HVaqi7dSlvMpoKI3ehGffh9GN18yNj9L7r7XGD7xIek510/6tQ/Ee++9x7x58zAaz24A7Pvvv8/ChQuJje3+I37ssceYPHkye/bsYdy4cf0Z6gXT1lQCgKdPzABHcnEz+8TSVHcUp9MuXXkuITZHd8l4/UkG6cf4RHDj8FkM849nS9FO8hqKALgl+VpsTjuVLTXsKT+Iemx8kBAXgqqqZFYfJcYnkuc2v0RBYwnXJV3JLcnX4tZPc/udrbry3ditLcSnLxyyY99Sg4exJnsDmwt3clX8VNdyRVGIHbWA6uKtNNUeobUhn4bK/VTkr0erc8Nh78Do7k/UiHl4+vc/qkwAACAASURBVMScouVE9IV2Wwe1bfU4VScO1YnD6cChOrA7HXyUtRY/dx8mhKeddF/zsSEhLafoDtrQ0UR+QzG3pczt08YE1emgMOM9Gqsze8xn3FyX7dqmvmJfz50UDcMULX9InY3dEkGHrZ3f7Hib+1Y++s31KAotqsqmTju3XfZjLN6Rl/RDCY3WQHDMFQRFT+/uVnq8xRJIHPe/ZG17nrz9b5B82WNovjOljjh//Xb3vG3bNgoLC5k/f36P5Y888gg2m43Q0FBuv/12br31VgBaWlooKysjJeWbYikWi4WoqCiysrJOmgQ2NzfT3NzcY1llZWU/XE3faW8pR6f3QG/0GuhQLmq+IenUV+6jqSZLWlUvIfZjX6T6UyT2d6TeCEBufSEAPm5e3Jw8B42i4eOjG9heupc2WzueMlZUXCDvHlzJR0fWuV4vGreAGbGTBzCibl3t9ZQc+Qg3z5AhXURrVNAIAtx92VG6r0cSCKAzeBAafzWh8VcD0Fh9mNbGQhz2DmpLt9PVXkv27pfwDUknZqRMWt+XihvL2Fq8m5r2enaU7HX1AjmZu0fffMr5+4w6I3qNjpZTtATuq8gEYNwZuiD3hsPeSd6+12lpyCMgYjKRw2+ipT4fW1cz3oHJ1JXv6u6erGhxOLpc3TdV1Ult6Q7qSja7jpVi0JFh7b52owI/jU6mzn80f9/1Ngdb6pjmF99ncQ9miqI5oQuqm2cQUcm3UJjxPg1VB/AL7Z/uvENZvyWB7733HlOnTiUi4puBsq+//jppaWloNBq2b9/Oo48+it1u54477qC1tbs/t8XSs7qT2Wx2rfuuN954g7///e/9dQl9zum001SdiYd3lLRUnCeLXwI6gyf1FXslCbyEfDMm8PQfTbE+3WUeJkWMcZWK93XrfrCyMmstY0NTifQOw/0M85gJcT72VWS4EkA3nYnJkWMHRQIIUJq9ClV1ED1y/pD+vtFoNIwLH83neZtP6BL6Xd6BI/AOHAFAeOJc7NZmSo6sor5iL+7m0O7WCnHe7A47v9+0lIbOJvzcvEn0j2VCeBq+bt7oNFo0ihaNoqDX6jBoDcR4n7rgiqIoeBo9aDnFmMD8hiKMWgPhXiHnHbfD1kFR1oc01WThdHQRHHMFofGzATD7ftMNMyDi1J8BIXFX0dVeS1XhJlTVyUOh48jO34jJK5LwwBF4W0JpsbYD8EHmGsIswViMngScpmhUbXs9zZ0tdDms1LU38nXJHiaGpxHtHY6vmzeextM/FFVVlS57F4qiwXiWk9k3d7Zg0pswnKZ1zqk6qW1vwKk6CfYMOKvjfpdvSDoV+RsozFyOtbMJd3MYHt6RMs9nH+mXJLCqqooNGzawdOnSHssnTZrk+nnatGnce++9rFq1ijvuuANPz+7BvS0tLT32aWlpca37rnvuuYebbrqpx7LKykruvPPOvriMPleRtx67rQ3/sJOPiRRnT9Fo8Q1Jo6b4a+y2dvlAuER8Mybw9B9NY0JH8tPJ3yct5JueA+mhIwn2DGDVkc9ZdeRzQs1B/PKKh/F1O3UZ6oHQ0NHEJ9kbSQ0ezsigYQMdjjhHXXYrf9r6L3QaHf+8/veYjSf/nrrQVFWlqugrGqszCYycgrs5dKBDGnDjw0bzSfZGdpUeYGr02VVl1OoMaHX+xI5awJEdf6M0+2M0OhMB4fL9fb4+y/2Suo4Gfj71h6SHnv9DXLPB86QtgQ6ngx2l+0kNHt5jXsnecti7KM5aQVNtFg5bOyaPIMISZuMdmNzrY2k0Otw8g4lOudW1bJxfXI9tLCYzd426ibcPrOCJz58FusfBh5gDMeqMuOlNzEm8gi67lb9u/zcFDSUnnGd32QGg+7t0Qvho7E4Heq0ejaKgUTS0dLXiVJ0oiobKlmrKW6q6Ywkb5Wpd1Wt1OJ1OuhxWVFV1FXvptHWyu/wgvm7ejA8ffSwRPFafV9HiUB1Y7Tb2Vhyiuq0OgPSQFK5Nmtnr7zxFUYgbdTeFmcsoz/2s+5oMnkQn34ZXgHx/nq9+SQKXLVtGcHBwj4IwJ6PRaLr/sOhu8QsLCyMjI4ORI7s/FFpaWiguLmb48JNXBrJYLCe0HA5m9ZX7MPvGu540ivPjFzKG6qLNNFQeICBi0pl3EIOe7TtTRJyKoihMjEjvscykM/Ls1U/w+r7lFDaUUNhYyqJVT6BRNDhVJ75u3lgdNqwOKzaHnRifCJ6Y9uBp55Y6X9tL9vLfw5/yuysfx6DVU9JUzm+//CsNnU2sy9vEi9ctwd0grZUXo9z6QqwOGwvH3D6gCaC1s4mynE9orsvuriJ47EGK0d3f1Uox1A0LiMNi9ORQ1ZGzTgKP02j1JI37X47sXErJkZWYfeMwuZ982g9xZk6nkw35Wwk1B/VJAgjdxWGaOppo6mymqLEMu9OO3elgR+k+mjqbmR49sVfH62ipwG5rw2HvpLrka1rqclzrokfejm9wWr+3rl+bOJMk/zhautrYWryL0qYKcusL6bJbaehs4uOj61FQUFEx6oz8eMK9mHRGjDoDPm7e1LbVU9Zcyfr8zewpP4SPyYt2eyfaY8mwp8EDvUaHQ3VQ017vOm9FSzU2hw2b047NYUOj0WLQ6r9JolXVNZ2E3WlnU+EObE67615eVZ1oNFo0KK4uvF5GM0dr88iszuZv1/0G715+57qZgxk2/kHaWyvoaqulNPtjcve9SkD4JCKG3zSkezqcrz5PAu12O8uWLWPBggU9BuFmZmbidDpJSkpCo9GwY8cOXnvtNR588JsS7/Pnz+fVV19l4sSJBAUF8dxzzxEdHc2YMWP6OswLrqujAWtHPUGRU8+8sTgrbuZQTB5B1FfulyTwEmE/y5bAU3HXu/HD8XcD3VXlXt37PjXHnkQmBybirnfDoNXTYe9ifd5mfvLJ/2N2whWoqspNw2dh6sNCHp22Tv789csAvH9oFdckXM4vN/4Jm8PGbSlz+U/GatblbeLG4bP67JziwlBVlY+y1mLUGpgSNTBFy1RVpbp4C2U5n6A67bhbwnE3h6HRGdDpPQiKmnpJF5ToDY2iIdo7gqKm0nPaX6t3I270PWR+/UdqS7YTnnRdH0c4dGwp3kVZcyUPjr+nz46Z4BfDyqy1LFr9JI5jQwqOSw0azpgzJJvtzWU0VB3EYeugrbmE9uaefyfegSkoGh2Rw2+6YL2OtBotSf7dLYRjw1J7rMuoOsr20r3YnQ4C3H25JuFyPAw94wr08GNEYMIJ42AvlO8WaCtvqWLxp8+wKmsdd6fN6/XxFI0WD0s4HpZwLH4JFGS8T03pNty9wvEP65s5F4eiPk8CN2zYQGNjI/Pm9fwlV1VV8dxzz1FZWYlWqyU0NJSHH36Y22//ZrD1woULaWlp4Y477qCjo4MxY8bw4osvXhLTQxx/kmTxH7oD9Puaoih4+Q+jungLTodNKkddAs5UGKY30kNTSA78JU2dzZiNnidUagw1B/Lm/v/y4eFPAViR1d3VREEBBTQoaDRaRgUN57Epi3r1tLGwoZRnvviz6/XHR9fz8dH1GLUGnpnxCHG+UWTV5LLm6AauiZ/ep8mn6H9fFmxjf+Vhrkm4fMDGndZX7KX06Cp0Bk9iRt7ePfesPBE/pSjvMD7L+RKH03HKIiOnY/IIwOwdQ3N99pk3FiflVJ28c3AFkV5hTO2jydIBRgQksjJrLQ6ng59O/j7+7r7dU4hodIRagnp0BVVVlY7WCqwdjTgdVurKd9FcnwuqE53eA63eDb+w8fiFpAEKJo8A9MbB1eMsJSiJlKCkgQ7jtL77WRRqDmJq1HjW5m3iphHXnFfvCZ3Bg/i0/yFz63PUlGyTJPA89HkSOGvWLGbNOvHJ9owZM5gxY8Zp99VoNDzyyCM88sgjfR3WgDP7xROdMh+TR+BAh3JJMfvGUVX0FS0NeXj5S//wi93x7qC6M3QHPVtGnYFAz5N33bou6UqCPAMwaPVsK9mLt8ni6l4DKk5V5WhtHrvLD1LUWEq0z6mLE3xbm7WdJZv+Rputgx+Ov5tYn0h+/vmzOFUnD09e6Jrw+Mbhs/j1ly/w1Po/cF3SlVweM0lu4i8Cqqry8dH1RFhCuHt0759o900MTiry12Nw8yV58iPS4ncWQsyB2Jx2Gjub8XP3OadjmP0SKc/9FGtHAwa3czvGUPZF/tc0dDRx+8gb+vThfkpQErePvIFJEekEm099j+V02ik89B4NVQe/tVTBJ2gkYYnXYnTz7bOYxImuS5zJpsIdfF2857znR1UUDYGRl1Fy5CPam8tkHsFzJBOsXSBGN1/5gOkHZt94tDo36sr39EsS6LB14HTa0RtPnJD4UuGwdaDRGQfFPFjn2x20t46XDR8VfPJxurVt9Tz86a/4MOszfjr5+6c9Vru1g7/teM01if1jUxa5jv/i3N9hddh6VHhLCUpi/sjrWZbxMS/uegt/D18pFHMRKGwspaS5goVjbkd3Di1KfaG5Npuu9lqiU+ZLAniWvE3d1YMbOprOOQn0DRlNee5nVBdvITxpbl+Gd8mzOmy8c3AlcOrP23Ol02i5acQ1p91GVVUKDr1HY9VBfIJGERQ9DY3GgFbvhsEkU3ZdCFHe4UR4hbKpcPt5J4HQXTm0NHsNdeW7zykJbLd2kN9QTLRPeK+mlVKdDjpaK1G/0/XY6O6H7iKbnkqSQHFR02j1+IakU1u2o0+rhDbVHqWpJouakq0A+IWNJzTu6kvqy8LpsFGRv57Kgo0YTD5Y/BJRUTH7xOIXOjDjcG192B20L/h7+DIrfjqrj64nt66QeL/ok27XZm3n558/S1VrDQA/mfg/PealOlXxme+NmM21iTO5+8OHOVydI0ngRWBP+SEUFCaeYvLq/tbZVkP+wbfRGTzxCUo98w4C6J5TFKCxs+mcj2F088UrYDhVRZswuvvLWPRe2Fm6n1ZrG09P/4nrd3GhqKpK6dHVNFYdJDByChHDbrig5xfdFEXh8uhJvHXgvxytzXONeTxXOr07nl5RVBdvweKf1KuGAKfq5LmtL5FZnY1Bq2dGzGV4u1mYFDGGkNO0JtutbeTue422pqIT1nl4RTJswo/P6VoGyuC40xLiPPiHjaWmZGufVQntaK0kd9+/QXWiaPSoTht1ZTtx2DqIG313H0Q8OFQVbaayYCMA1q4mGmsOY7e2Ule2EzfPINwt4Rc8pr4cE9hXbhw+izXZG/mi4OseSaCqqnTYO3HXu/HavmVUt9byw/F3MzEi/bRzkX2XUWcgwhJKfsOJXypi8DlUlUWsTyQW04XpHeCwd2K3tlNZuJH2plLaW8oAiBt9t4yD7gUfV0tg83kdJ2bk7RzZ8XeKsz4EkETwLB2szMJi9LygY9lU1Uld2W4qCjZg7ajH7BMnLbgD7PKYiXx0ZC2/2PBHgj0DMGoNxPlF873h16DX6jFqDb2qmO0VmExLQx65e19FozWSkH7/sbm4T9+z6WBlFpnV2fi4eWExeLKpaAfttg7eP7QKXzdvjDoDeo0ehe7kVXU6cDq6sHU2oaByZfgopsX1nAHBzTPoXN6SATV47rSEOEdu5jAMJh9a6vPO+wu5ramE7N3/RFE0JE/5GUY3X9dTxOriLZfMnISqqlJXvgtPn1iSxv2va7nd2sbBTb+j+MhHDBv/4GmO0D9c3UH7aExgXzAbPUn0i+HzvM00dbbg7dbdqlfdWsv+ysOkh45kb/khpkVN4PKYc/v7i/WNZG/5oRMqqonBxWq3klNXyJzEK/r9XC31uTRUZVBTug1UJwBunsH4hY4jIHIyHgPwkOZiZjGZUVBoOI+WQACtzkTi2AfI3vMvirM+pLnuKO6WCHxD0mTIxymoqkpOXQExPhHnNV/f2XDYrdRX7sXW2UxjTSYdLeXojV6EJcwhKGraoBj2MJSZjZ788vKHWZ+3heauFvZUZFDUVMbG/K2ubeJ9own3CmFe8rUEfmsIxckERU3FLySdQ1uexWnv5Oiuf+BmDiEk9ip8gk5dFfZwTQ5aRcPfrv2Na8L7suZKvijYRnNnS/dUUsemvmhvKaerswEAg1ZLgc3O+yWHGJk0h2ifi/tzePDcaQlxjhRFwdMnlqaawzgd1l6NkWmqOUJl4Rc47B047Va6OupA0ZCQ/n3XF7qiKPgEp1JdvJnmumx8g0f316VcMM21R+hqryUk9soey3UGD0JiZ1Ke+xnNddlY/C5sNVtXYZhB1BII8NCk+/nLtlfZVX7ANR/ScXuPjQG8JuHycz5+nE8UXxZso669AX8PuZEcrLLr8rE77YwISOjX87Q2FpG9+1+Ais7giW/waHyCR+PpHdWv572U6TRafN28qTzWZft86I1mho3/EYUZ79Nce5TG6kxqSrczYtJP0Q1QtdjBbEXWZ5S1VDK7Hx6eOOxWbF2NNFQdoqHyAB2tFa51Wp0bgVFTCY2bhbYXvTNE/4r0DuO+MbcB0GptI6smlzZrOzaHnayaHAoaS/iyYBtFDaU8fflPzlhJVGfwIHXaU9itbdSW76IyfyP5B97EOyiViKTrTzqMp7ChhHBLiCsBBAizBHPXqJvoaKmgPG8d1s627rF/OjuWqFEEx8zE3RJKdUczj69bwhv7l/P/rljct2/OBTa47rSEOEf+4eOpr9hDZcEXhMaf3bxrLfV55O77NxqtAbNvHFqdEbNfAgHhE08YZOzhFYlO70FTTdYlkQSWZq9Bp/c46ZiigIjJ1JRsozT7Y4ZPXHxBW6ZsTjsKimtC28HCz92HX898FLvDzuqj63HTm5gVPx1FUThYmUVjZ/MpxwuejeMVQ/MaiiQJHMR2lx3EoNUzvJ+SQIe9k4q8z6kq2oSiaEm+7FGMMjF5n4nyCaeooaRPjqXVGYkb3T3XXVPNEXL3vUpNydYTHqwNdY0dTSzPXEOMTwTT+nBaCFV1UlOyjbKcT3E6ugDQG70IiLgMs28c3gEjUAaocJM4e54Gjx7j54/Pa/h57mZe3vMuD378NLelzMWpqgR6+jEudNRJK8tqdSa0OhNh8dcQEjOT4iMrqSvbSXNdNsmTH8Fg8u6xfVFjGSODTxxDWF95gMJD76GqDsx+CfgEpeLpHUVAxGTXNiFmEzePmM27B1dS1VpDkGdAX70dF5wkgeKSYPaJxeI/jJrS7YTEXnnGD39VdVKa/TE6gwfJkx89Y0UnRdHg4RV5wiSyF6Oujno626oIS7j2pGOKdHo3whKuoTDjPzTVZOIdmHLBYrM7Hei0ukHbJVKn1Z1QhS41ePh5HzfSOwytoiGvvogJA1RwRJxZdl0Bcb7RJ8w52RdsXc0c3fkPujrqMHkEEZt6lySAfSzGO4K95YcoaSonwiu0z47rFTAMT59YKvI3Ehg5Ba1O5v087pOcL3A4HTw06f4+nQ/1+BANncFMSOwM3DxDMPsloBlkvUjEubkqfipeJjP/3P0Ob+z/wLVcr9UT4hmIoijoNToWjplPrG/PHhIarZ7o5FvwC0knZ+8rHNr0O7wDk/EPn4TB5E2HrYOGziYC9G60NhahKBpaG/KpLdtJZ1s1Wr078aPvxdMn5pTxTY4cy7sHV7Kz9ABzh128D37kX4u4ZARETCJv32s01hw+bV9wgM62atqbSwlPvO6sS/qafeNoys6iq732or45a649CnTfuJyKb3Aa5XmfU3DwXWJG3YV3QN+W9D4Vu8M2YGX3B5JBqyfSK4z8+uKBDkWcQnFjGbn1hdya0j+FJQozl9PVUUdU8q34h43rl3MMdVfHT+PDrE/5PG8z96Xf1qfHDom9ipw9/6Sx5jB+Iel9euyLVU1bHauPfM7o4BGEmvuuaEZlwZdUF2/By384cWn/M2gfGorzMz58NGNDUylsLCG3voguu5XCxhI67V2oqkpmdTYvbHuVq+OnE+zpz5jQ1B5/C2bfOCKSrqc460MaqzNprM4EoMrePbWDvXw7R2t3u7bX6T3OuvtwoIcf0d7h7CzbL0mgEIOBl/8wdHoPGqszzpgENlYfBsD7DNt92/HxcQ1VhwiO6f/CEP2lrmIPJo8gTB6n/lJWNFpiRt5Ozu5/kbfvNfQmb/xC0giJvapfKxLanPZBVRn0Qor1jWJ76V4pDjMI/XPXO2zI34JWo+3TLm0OWweq6qSp9gjNtUcIjpkhCWA/8nHzIjkgkeza/D4/tqd3FAaTN4WH3qO59ihBUVNx8wwZ0l0SNxftxKE6uTttXp8dsyJ/A+W5n6E3ehGTeqd8Vl7iNBoNsb5RJ7T2AXySvZG39v+XN4+1FJp0RixGTxRFQ6g5iBuGXc2qwn3sbWgFIMYShALkt1QBMCr5JiLMgaiqE41Gh6d3dK9qSowLG8UHmZ/Q0NHEx8eGicxLvvb8L/oCGpp3W+KSpCgaPH1iqa/Yi87giVZrQOWbIh4aRdv9WlWpK9+Dh3d0r6q5mTyD8fCOoqbka4Kip1+UVcYaqw/T1lhEWMKcM355enpHM3L6L6jIW0drYyGVBV/QUJVB0rhF6I0nn/fufNmdDvSaoVn2Ps43ig35WyhuKiPK++KuOHYp2Vm6nw35W/B39+Xu0TcT6Nk3vQCKMj+gtmyH67XB5ENQ9OV9cmxxagl+Maw6so4uuxWj7uxv+M5Eo9UTN/peSnPWUF+xl/qKvZj9EkhIX3hRflecr9LmCpZnfMww/zjCLSF9cszqoi2U536GmzmMpHGLpNjLEDcncQbXxF9Om62dT3O+pKatDlVVaepqYV9FBvsqMnps32yzEmoJguYqvEwWkiInoT+Ph9oTwtNYnrmGZ754nvKWKpIDEyUJFGIghSVcQ0t9LtVFm44tUQD1hO0URUtYwpxeHVtRFAIjp1Bw8B1a6nMveOXM8+Wwd1F8ZAV6o4XAqKlntY9O70bEsBtQVZXq4i2UHl1F9u5/MXziQydtEextddbvsjntQ7I7KHR3fXlt739Yn7eF+8fMH+hwxDE7Svdh1Br427W/RttHf5vlueuoLduB0T2AwIjJaHRGvPyHS2XJCyDBLwaH6qSgoZhhAfF9emx3SxiJY35Ae3MZ1SVbqSvbRXnuWsISZvfpeQa7LruV57a8hEbRcF9633yWVRZspCznU4xufiSN+19JAAXQ3VJoNnpya8p1rmV2h51nvnyBcEsINw2fRV1HA15GMyHmIBRFobGjCaeqnlcCCN1j+W9LmcvyzDV4Gc38ZOJ953s5F5wkgeKSYvIIZPSMX5/Qpa6qaDMKCgGRl51X9xHvgGS0OjfqynZfVEmgqqoUHV6OrbOR6JT5vR48rygKQVFT0endKcx4n7Lcz4g4NumuqqrdVbjqc2msOoRXYDLRI+ahPYcbWrvDPqjmCLyQLEZPJkWMYVPhDu5IvbFfio+I3mnuauXrkj1MCE/rswSwoeoQFfmf426JIHHsIrR92BolzizhWBXf9w59xK+u+Gm/dCd0t4QRNeIW7F2tVBZsxOybgMWvbxPOwWx93mYqWqr5ycT7+mQeNVtXM+W56/D0jiFhzMLzetAoLn06rY7fzHzU9fq7vTe83U6cMuJc3Zw8h+uHXYWCclHeu1x8EQtxFr77xR50li1fZ6LR6vEJTqW+Yh+q03HRjPdobcinofIAAZGX4Rc65pyP4xc6hqaaw9QUb8Xim4CnTyyVBRuoLNjo2qax6iC51lYSxny/18mmzWkbsmMCobtwxaaiHWwu2snV8dMGOpwhr7ixFIfTwdQ+GgfY3lxG/oE30erciBt9tySAA8DLZGF4QDxZNbk8/OmvcNe7uTqLHB8+cPz/YeZgfjzx3AqPKIpC9Mj5HP76T5QcWcGIyY8OifFrTqeT/x7+lFifSC6LHHvux3FYcdi7UFUnZdlrUFUHUcm3SAIoBp3zbVEcSEP3bkuIc+TpHUNt6Q4622pwMwcPdDhnpbnuKCgawnvZBfZkQuOvoan2KLn7XnUt8/COJiH9+2h1BioLv6Qsew2ZW/7AiMmP9upG1+50DLqJ4i+kBL8YYrwjWJe7iavipg6Jm8bBrKKle2LxKK+wM2x5ZqrqpCznExSNjpQpPzvrqsSi7z01/Se8e2AF5S1V3/o39s2/NQVosbaxpXgX1yRcTqJ/7DmdR6d3JzTuaooOf0BD5X58Qy796V/yG4pptbZx/5jbev35Ze1spLkuh672GioLvuTbQzn8wsZh8rh452MTYjAaundbQpwjD68IAJpqDw/aJLC9uYzy3LUERF6G2SeWhqoMPCzhffIU1eQRwMipT1BfuR+HvQujmy8+QSNdraJBUdNRUCjN/pgDX/ySkdOeQm80n9Wx7U47+ouwS0VfURSFWQnTeWnX2xytzevzMUuidypaq9Fr9fi6e5954zOoK99Dc102gZFTJQEcYAatnnvTbz3tNh22Tu798Kfsrzx8zkkggE/wKIqzVlCQ8T6q6sAv9Nxbxy4GO0r3oVE0jAw89RREJ9PZVsORHX/FYe8EQGfwJDByCjqDBxqNDp/g0f0RrhBD2tC92xLiHBndA3C3hFNXvpfgmBn9eq7cukLyG4qZHj3xrCvZOZ128g68ibWjnqbaLBRFi6o6CBpxc5/FpTN4EBh52UnXKYpCQMQkSrM/RlUd5Ox5maTxPzyrCZRtDnufVuy7GE2OHMvLu99lb0WGJIEDrLKlmmAPfzR9UN2xtSEPjc5EeFL/zDMo+pab3oTF6EljR9N5HUerM5E07ofkHXiT4qyVeAeOvCiLmjhV5/9n7z6j46quh40/d7qmaTTqvVdXyR2MwRhjOg6hlwCBNw2SEBJCGiEJgZD4T0JIqIEQamgJxmAD7rh3GzdZktV7l2ZG0mjafT/IFlYk25I16ue3lpbtuW1LljR333PO3hyoPkK724nH58Hj8+D1+fDKXnyyj06Pizkx2XxRsoPsyEmYdf178Afg9TgpPPAaXo+ThMk3YQmbgkKpETMhBGGIiSRQEAZIkiSCIqZRmb8Sl7MFjW7wowR9C0nxnwAAIABJREFU2VK6i2d2vAqA2+vmyvRF/TqurnQLro4molKWUFvSVSU1NuNarMPYwFih1JA570c0Vu6hrmwzR7f9mYw595+1tYTH58Gg0A9TlKOTTqUl3hJDQWPxSIcy4VXb64g0D77JtSzLtNYfwxKSKW5sxxCLzkyL0zbo8xgscSRNvY283c/RXPslIdH+6zU5XD48+hnvHv74jPuc3L4w8bx+n9frcZK/50WcbbXEpF8z7kdKBWE0EUngMKpx1PPYhqe5Y/rXmRs7fDfkgv9ZQrOozF9Jc82XhCdc6PfzV9lreW7XG4QbQ3G42thWtqdfSaAsy9SVb8UQGEdE4qLuvmMDLdDiD3pTFPqMazBY4ik++CZ1ZduITr3sjMdM5Gbxp0oJTmBTyU58Ph8KxcTrMTYa1DkaqLTXcGHi3EGfq7O9AY+7DaM12Q+RCcPFEmCm2Tm4kcCTDJYEdIYwGip2jbkksKylkg+OrCQ5KJ77596FWqFCpVChVChQSF0fVfZafrn2TwSodORETenXeb3uDvL2vEiHvZKY9Gv8VsBNEIT+EXcXw2hF7mrq25v487Z/sOb45pEORxgEnSGMAFMULfW5fj+3x+flsQ1/RUbm0YUPsCRlAQVNJfzks9/zUe7qMx7b2d6A29lCcNQMJElCoVCNSAJ4KmvENAJDMmmo2IHP5znjvhO5RcSpUq2JOD2dVNiqRzqUCWtP1UEAzos992q6JzlaSgAwWhIGfS5h+Fh0gbQ4bbS7O3C6nYM6lyRJhETPpq21lA5HrZ8iHHpen5c/bn4OGfju7DuINkcQZgzBqrcQqDNj0hoxaPSkBieybMkveXzxT8/a67XdXsXe1Q9xYMOv6bBXEhw9SySAgjACRBI4TFweF9sr9mHUGAgzBPOPvW9zsMb/CYQwfAyBcbS1lNBhr/HbOXdVHOCOD35AY0cz58fNJERv5aKEeQCUtVby9sHlNLQ3nfb4pup9gERgSKbfYvKHkNi5eNxtHNvxDB53+2n380zgZvGnSg1JBOB4U8nIBjKBlTRXEKgz9+oxdS7sTYUoVQGiuuEYY9GZaWxv5r5PfsU3/vsjHl3/FOsKt7C2cAs7K/bj8XkHdD5r5AwkSUlD5a4hitj/dlTso769ifvn3EWc5cxVcuMtMcSYI8+4j8/rIn/Pi93/jk67kvis6/0SqyAIAyMeuQ+TXZUHaHO18+uLHiAhKIYfrHyUF/e8xd+u/J1fig4Iw88aMZ2Gih0c3f4UenMsao0RrT6YmPSrkc7h/7SspZJX9r5DhCmMr2ddwbwTU4YjTGE8ufhnuH0efr3uKf6y7WXumPZ1MkJ7Ti3rsNdQXbQWc3AamoCg017H5/Nhczkwa4zDNtUwMCSTyKTFVBet4eDG32GwxBNgiiQiYSEa3VeNW7umg47dnjv+EmkMw6DRk99QxMVJfRfgEYZWcUs5iZbBN7r2ely01B0mKGLqOf1eEEaORde1hrnN1U5sYBS59cfJrT/evf3+OXexIGFOv8+n1hoJDJtEQ8VOIhIuPOsa6ZHW6rTxz73vEm4M7X4/Gqy6sm143e1EJi0mPGFBvwqGCYIwNMQ70jCJC4zmpslXMyksDaPGwD0zbqK+rZHDtXkjHZpwjkzWZBIm3XTiXzJtrWXUlW3B3lgw4HO1udr57Ya/YOu08/9m3Mr8+FkoTxkRS7LGkx6SzC1Tr6XWUc9ftv+DfVWH8fl83fvUlW8FIDL50jNe67cb/8K3PnqYm9+/j+d3vUG7q2PA8Q6UJElEpVxKas69WMIm4/O6qS/bSmXBpz32E2sCu0iSxJSwDPZWHcI7wNEGYfA8Xg8VtmoSgmIHfa7W+qP4vJ0ER4qCF2NNUMBXD6ieuuwRXlm6jOeufpznrnocoEdC2F+RiQvxeTupr9jptziHysr89dhdbfxg7t093o/OlezzUlO8HkNgPJHJi0UCKAgjTNxtDZM4S3SPqRSzoqcToNaxo2I/UyNG19Q9of+Co2cSHN11c+fzuvly429prj2IOSR9QOdZU7gZu6uNX1/0AFlhqafdb2nmEhIsMfxh07M8uflZYsyRJFvj+VrWZTiaizFZUzBa4k97vM/no6CxhHBDCJGmMDYUb2NL6S4mh6ejU+kwaPRcnX4JkaawAcXfX+aQ9O6vTfmxFdSVbUap1hGbdjWSQnmiWbyYDgowLy6HHRX7KGwqHVSfMmHgym3VeH1eEiyDTwIdrSUolBqMQQmDD0wYVsH6njMqTFojJxsfZEdOIq+hcMDn1JtjMATGYWvIIyp5sR+iHBoen5cNxduZGTWV1OBEv5zT1lSA19NBWPx8USVXEEYBkQSOEI1STXJQPMXNZSMdiuAnCqWawNBMWuqOEJd5XXfz9LNpd3fw0bHVTIvIZHL42ZPH6ZGTeP6aJ1hzfDN7qg7yRckO9lUd5CYdTJ8074zH1rTV4/F5uCbjUhanXMCO8n18fvwLWp12ajz1VNvr2Fy6i9umLiXsRKI4VAlhdOrlOFqKqS/bit4YRUjMbDxetygMc8LJG6/i5nKRBA4DWZb52eo/cGnKgu5pm4l+GAm0NxZgtCSIqaBjUFxg14PbKeG9G5+nhySzv/oILU5b97TR/jIFp1JTtB6Pqw2VxuCXWP1tee7ntDptLEqe75fzybKPmuINKJQaAoMH9pBUEIShIe62RlBiUCwf563F1unArDWOdDiCHwSFTaG55gCOlq5Ruf5468sPaXO1szTzzO0TTmUNsHDTlKu5acrV7Ko4wFNbX+RlF5xffoR7wqdj1PZ9Y7GldDcSEtmRkwCYG5vTo11JSXM5j2/6O//c9y4ACknBry78PpP7uAkaLIVSTcac73N48x8oP7YcvSUer+wT00FPCA4IwqgxUNJSMdKhTAgtThvFLeW8uOctLku5CJ1KS/ggi8K4XQ6cbXWi99kYFaDW8cQlDxNl6t0rMidyCu8cWsH+qsMsTOp/XzwAa/g0aorW0VS9n7B4/yRZ/ra1dDfJ1nhyIicP+lz15dupLd1MZ3s91sgclOoAP0QoCMJgiUeTI2h2zHQANhZvG+FIBH8xh2QgKdQ01x46674+2cebX37ImsLNnBc3k0lhaed0zVnR0/h2WBSpASa2Vuznhd1vnvZ6X5TsYHJ4OiEGa5/7JATF8rcrf8efLv0FP7vgPowaPb/b+Fde2fsO+6oO4/K4zinG05EkBYlTbwdJwdGdfwdAJZJAoGtdYGJQDCXN5SMdyoRQ39bY/fcva46SGpw46KJdba1dMz0Mgaefoi2MbinBCeg1vZOWOEsUenUAe6vP/rv+fwWYItGbY2is2uOPEP3O5/NR01bP5LD0QU/bLDnyHmW5/8XjshMUMZ2ESTf6KUpBEAZLJIEjKC04iQhjKKvyN4jiD+OEUqUhMCSdxqq9eNxnLriypXQ3K46tJiggkDunn3uJ7A57JYFuGw/kfJ3LUi9iV+UBtpTu7rXfnsqD1Lc1sjDxzFNGdSotCUGx5ERN5veLHiI9JJnPj3/Bk5uf5aHVj/Pa/g/weM/c728gjJZ4Umfci5eumw1vp3+aM48H8ZZYylorxe+HYeDyurv/Xu2o44L4wTf0bmspBUmBIXDwVUaF0UUhKZgfN4tdFQf4zfo/897hj2l3dSDLcr+Ot0Zm026vxNlWP8SRDlx9eyNen5dw4+BamtibCmms3I0lbBJTFjxC0tTb+r1MQhCEoSceuY8gSZK4depS/rztH2wv38f8+FkjHZLgB2Fx82mpO0xN0Vpi0q8+7X5flGzHoNHz9ysfQ60897YITTUHQFIQFDaZ60Iy2Vt1iL/tfJXYwEjiTylx/0neWgJ1ZmZHT+/3uSNMYfz24gepczSwo2I/Hx9bw8r8ddQ66vnpBd8955j/l9GSQMrs+2HVo7RU7uSgLR/Z50VSqIhIXIhCqcHlbKG+fBs+nwefx0lg6CQSp9yKUqXxWxyjTaIlFrfPQ5W9ltjAqJEOZ1w7NQmUJInz4gY/hdPRXIzeGIlCOX6/Ryey26dfh9PbSW79cT44sooPjqzCojNzXdblxJgjz7jGOyh8KhV5H9NafxSd4cJhjPrsNpV0VS5Ntp77CLbP66Ys978oVQHEZV0/rn9PC8JYJUYCR9is6GmEGoL5+85/8cfNz7Eyb5146j/GmazJBIVPpbZ0M7bGvkuIN7Q1cbg2nytSFw4qAQRorT+G2ZqCSmPAojPz2MU/QaVQ8fT2V3C42oCu4iLHGgq5IG4WmgG+GSskBRGmMJZmLuHlpcuYFJZG8RBMUfSdeEKs05jwuNuRZS/uzlbKjy2n9Mh7VBeuxuNyoDgxXbS1/ghVhZ/5PY7RJCGoK4kfiq+30JPb15UELkqazx8X/xzNIH8unW31OFqKMVqTz76zMCbpVFrun3MXf7/yMX52wX1cl3UZHW4n/9z3Lr/b+DQHqo+c9liNzoLOEIatMX8YI+6fw3V5xJojB1UYqeTIuzjb6ohJvxq1RtQ8EITRSIwEjjClQsmd06/n/7a+yN6qQ+ytOkRdWyN354h582NZXOZ1OFpKKT70FpPnP9yrH9L64q6efhedZWrm2XjcHTjb6ggKn9L9mlVv4fapX+PV/e+xrnAr12Qs5vldr6NRqlma1f/iM32RJIloUwRlrVWDOk9f3CdGYhLSr2R6bA6SpET2uelsb0Kh1CAplChVASiUKmSfh8IDr1NXtpWQqNkEmCL8Hs9oEGUKR61UU9JSwQL635RaGLiT339Xpy8iyjz476e2llIAQqLFDI/xTpIkcqImkxM1maWZl7GldDcv7XmLgzW5TD9RhKsv5pB06su24fV0olRphzHi0/P5fBQ3l5/ze5MsyxQdfJOW2oOExswT3/+CMIqJkcBRYGb0VP7fjFu5Zcq1TApL44uSHf1eVyCMTiqNgYRJN+BxOXoVifH6vKwv2sb0yCxCDcGDuk5z7UFAJjA0q8frl6ctJD4wmoO1RznWcJySlgquybjUL1VoNUp19w2zP7lOrDPUKDUoFCokSUKh1BBgikCrt6LRBaJUaZAkBQqlhtjMpUiSkrw9z9PZ0ez3eEYDpUJJXGCUKA4zDE5OB9X4aepmm60ChVKLzjA0LVaE0Umn0nJJ8nwSLbFU2mvPuG9gSAay7MXRUjxM0Z1dSUsFTk8nKdaEs+7rcrZSWbCK0iMfUJ73Me5OG3Vlm2mpPUhI9JwzLocQBGHkiZHAUUAhKViccgEARo2BI3X5lLdW9WguL4w9puA0tPoQmqr39Xgaerguj6aOFr6Zc9Ogzu/zeajIW4FWH4re3LvwxKSwNFYVbKDFaUevDuDajEsHdb2T1EOUBJ6cjtff6qA6fQhpM79F/u4XOLz5CQJMkQSGZKEzhKJQalBrjBgsCWO+KXGCJZYdFfuQZXnMfy6j2ckkUO2nPpWOlhIM5hjRH3CCMmmNtLnaz7hPV/9IJfbG4wSG+L8Nz7nYUroLpUJ5xhFMgOqi9VQVfg6yD5XagMfdRl3pJgAMlnhiM65BMcgp1YIgDC2/vzv97W9/IzMzk+zs7O6PBx98sHv70aNHufnmm5k2bRoXXXQRr7/+eo/jnU4nv/71r5k9ezY5OTk88MADtLS0+DvMUSsnajJqpZpX9r2Dz+cb6XCEQZAkCUtoFo6WEny+r6ppFjQWIyExdZC99zrsVfi8LkJj5vSZHJwsbFHeWsWlKQvQ+mlhvlqpxiv7/L521d09EtP/m3CjJYG0Wd8hNPZ83J0OaorXUXL4HYq+fJ283c9hbyrwa4wjITEohjZXO43t43O0c7RwnzISPVgedzsd9iqxHnAC02sCzpoEKpQaTNYUWuoOD1NUZ9bqtLGmaAuzoqedcdZIa0MeVcc/RaMNJGPOD5i28DekzfwukUmLic24lrQZ3xbFkARhDBiSkcCZM2fyxhtv9Hrd4XBw7733cuutt/Laa6+Rm5vLt771LcLCwrjssq61Sk888QSHDx/m448/RqfT8dBDD/Hwww/z4osvDkWoo06wPog7pl3HP/e9y5H6fKYMQZNuYfgYLPHIpZvosFVhsMQBXUU+Ik1h6NS6sxx9Zu22ribilvCpfW5PC0nib1f+jk6Py6+VJU8maW6fB6Ufy32fnA460EI5RksCRksCselX43Y58HldeN0dHNv1N+xNRZiDz63/4miRYOkqzlDcUn7a/o7C4Lm8XT0wB1uoCaCl7gggExh8+uqQwvhmUOuxddr54+bnmB09/bQN5c0h6VTk5eFytqDRWYY5yp4+yl1Np6fzjLNGZJ+XiryPUakNZJ33YPd6d5M1CZM1abhCFQTBD4Z1nsrq1atRKBR873vfQ6vVMn36dG644QbefvttoGsUcPny5fzwhz8kPDycwMBAHn74YTZu3EhVlf8LUYxWFyXMRalQ8mXN0ZEORRikk02iHa1dRSI8Xg/HGgoHVXr7JHtTISqN8Yw3DuHGUOIs0X6dRqhWdN0k+3tK6MnpoCfPP1CSQolGF4jOEIrBEodaa8bdafNniCMizhKNhCQqhA4xt8+NQlKg8sODjeaaA2j1IegDz726ojC2mbQG7K429lYd4vndb7CtbG/f+wV1JU6O5pFdF+jxedlUupPZMdPP+P7UULkLZ1st0WlX9ip4JgjC2DIkSeDhw4eZO3cuCxcu5Mc//jHl5V03L8eOHSMrKwuF4qvLTp48mWPHjgFQUlJCZ2cnU6Z8VekwOTmZgIAAcnNze13HZrNRUVHR46OmpmYoPqVhpVPryApNYX/V6JgiIpw7jS4Qjc5C24kk8MvaXOydDubFzhjUeZuqD9BcexBrxPRhXyd2cqTE5e8ksHs6qH/Wkai1gbicY38KpU6lJdIUxrH6vtuNCP7h8rj9Mgro87qxNxViDkkXazgnsBhzZPffA7Um3j20os/9AkyRKFQ67E2FwxVan/IbirB1Opgfd/pqnq6OZiryV6IzRhAcmTOM0QmCMBT8ngQuWbKETz75hO3bt/POO++gVCq5++67aWtrw+FwYDKZeuxvNptxOBwA3X/+7z4mk6l726lee+01Fi1a1OPjtttu8/enNCLSgpOpsNfg8XrOvrMwqhksCdga8nB3Onjv0MeYNIazLro/E1n2UVmwCm1AMJFJl/gx0v45maT5eyTQ34U5DIGxtLWU9liPOVbNjJ7G4bo88hpG9kZxPHP53H55AFFfsQNZ9mIJyfRDVMJYNS0iE7VSzezo6VyZvohqRx0Ha3o/zJYkBWZrKq2NeSMQ5VdW5a9HKSlICzn9lM6qwjX4fG6Spt6O5MelAIIgjAy/J4FpaWlER3dNPwsPD+fxxx+nvr6e/fv3YzQaeyVzNpsNo7FrAfLJP+12e4997HZ797ZT3Xnnnaxbt67Hx1tvveXvT2lERBhDkWWZGkf9SIciDFJY3Pl4PU4O5K2kuKWc7MjJg5pyVl20DpezmajUy1BpDH6MtH9OJmn+Hwk8tzWBp2OypuDzuWlrLfPL+UbS0hNrdMQU8aHj9nrQnONU5JNk2Udj1V60+lDMIWI94EQWqDPz0jVP8oN53+Ty1IUY1AFsKN7W576moCTczhZczpEpglff1sieqoMsSbkQa0DfywsqC1bRWLWbkOjZBBjDhzlCQRCGwpCvCZQkCUmSkGWZjIwMjh492qPq5ZEjR8jI6Cp+kpCQgFar5fDhr6ZBFhYW0tHR0b3PqcxmMzExMT0+IiLGR9Po1JBEAI7W549wJMJgGS0JBIZkcrhyPwDXnWPDdln2UZa7nOrC1RiDkgkKm3L2g4ZA95pAP4+wnVwTONgb8ZNM1mRAwjHC06z8wag1kGyNZ3/VkZEOZdxyeV2DHoWuyPuYDnsl1ojpfopKGMsMGj0apRqtSsOU8EyKmvp+IGWwnFg73lI6nOF1e//ISmRZ5vK0hX1u7+xooqZ4I4bAeGLSrhrm6ARBGCp+TwJXrVpFU1MTAI2NjTzyyCNYrVays7O59NJL8Xq9PP/887hcLg4ePMj777/PLbfcAoBOp2Pp0qU888wz1NXV0drayrJly7jwwguJjp5YPfMijWGE6K3sE+sCx4Xg6FkUdjgI1hqINA3sKarP66bkyPsc2PAo9eVbCQzJJDXnnhGbjjP000H9kwSq1Hq0+hDaTlRRHesuiJ9NYXMp28r2jHQo45Lb6znn9hAeVxvH9/+LurItBIZkEpk8/NO0hdEt3BhCXXsjnj5a6wSYutYPFh98k4aKncM6ImjrdLCxeDszoqYQbgztc5+6si0gSSRNuwOlSjtssQmCMLT8ngSuWLGCK664gmnTprF06VI6Ozt59dVXMRqNGI1GXn75ZTZt2sTMmTP5/ve/z3333cfll1/effwvfvELMjMzufLKK1m4cCFarZY//elP/g5z1JMkiZzIyeyrPkx+Q9FIhyMMkiU0izqfgnDJB3L/+z/Kso/CA/+isXIXBnMMsRlLSc6+a0Sb8A7VdFDPiZFFdT+bxfeHwRxDu63Sb+cbSZckzSfcEMKnBRtHOpRx6VxHAjvbGzi0+Qla649gjZxB0rTbRYN4oZdkazxen5fjjSW9tikUKgJDu9aJlx79gMObn6S1YXjWCJacqDo8NaLvNaxedwcNFbuwRkxDowsclpgEQRgefu8T+MILL5xxe1ZWFu++++5pt+t0Oh577DEee+wxf4c25tw85Rq+KN3J+uJtvRZr+04kEgpxszEmtLmdtHjdTNNI1JVvIzz+grMeI8sy5XkrsDXmE5G0iOiUc5tG6m9DNR3U5XWjVCh7VA8eLL05mqaa/bg77ai1prMfMIppVBpmRE1hXdFWfD6fX79OArS7nRjU+gEd42yr58jWZYBM4tTbsUZMG5rghDFvcng6EhKHanPJCE3utT1p2u24O204HbWUHH6XyoKVBA7DutIaRx0AM6L6Xl5QV74dn7eTsPgFQx6LIAjDS9xFjGJGrYFJoakU/M9I4JbS3fzo09/yuw1PI8vyCEUnDERJS9fT1lhjKLUlG5H7mBL0v2qK1lJfthVzcDpRyadv3jvc/D0dtLG9mXcOrWBj8Xa05zgd73RO9mlztJT49bwjJSEolk6vi+oTN26C/7S529GrA/q9v8/noTxvBSIBFPrDqDGQFBTHkbq+1/krFCq0AVYCQzMJT7iIDns1LmfrkMe1p/IgoXorIXprr20d9uoTa9CTMJhjhjwWQRCGl0gCR7n0kGTKbdVsKtkJQJW9lmd2/JNqex1H6ws4WNu75LQw+hQ3d61Ly05bgrvTdtapPrbGfKoK12AMSiI5+65RNb1M48c+gdX2Oh5e/QT/PfopCknB1zL9O9ppCIxDUqjGTxJo6UpqTz5UEPyn3dWBXtP/JLDs6AfYGo4REjNXJIBCvyQGxVJuqz7rfubgVKDrfWAoebweDtflMzsmu1dPyw57DXm7nwdJIi7zuiGNQxCEkTF67iyFPl2a0jUFY23hZgCWH/0chaTg2at+T7A+iA8OrxSjgWNAcXMZwQFBxETloFIbaK45cNp9W+tzKdj3ChpdIElT70DhxzVy/qD240jgR7mfY+t08OuLHuCFa/7AtZn+HfFUKFToDKE428bHyFlMYCQqhar7oYJw7lxeN6vy1/OfI6t4bf8HNDtbMfaz5Upj1R4aq/ZiCk4lPuvrQxypMF5EmcOxdzqwdfbue3yqAFMkGp2F5tqDQxpPha0aj89DsjW+x+uy7KPo4Bv4vC5Sc+4VLSEEYZwaXXeXQi8GjZ6bp1zDO4dWUNBYzMaS7cyNzSHUEMzV6Zfwr/3vU9JSQWJQ7EiHOiY4XG10elwE64OG9bpFzWUkBMUgKZSYrCk4Wor73K+1Ppfj+/+JRhdE+uz7UGt798ccad1J4CDXBHa4nWwt38vFiecxOXzo1r7oDGG0tY6PkTOVQklsYCSHa48hy3L303uvz0ubqx2j1iDWCfdDc0crj65/qrsPq1qpJsYcyYL42Wc91uNup/zYCnSGcFKyvznUoQrjSJSpq4VVpa0ac2jqafeTJAXWyBxqSjYO6XrmouaulhWn3j943B0c3/cyzrY6EibfdKLVjiAI45FIAseA+XGzeOfQCl7a8zYA16QvBrpKxr/x5X/ZXr5XJIH94HC18Z0VP8fldXNRwjwSg2KZFT2NEEPvtRD+VNfWSJW9lsXJXcVgDJZ4mmu/xOVsQaPr2Zi3ufZLAFJy7hm1ldg0ipPVQV2DOs+aws10ejpZlDzfH2Gdls4QRnPNQXxeFwo/rzkcCdMjJvFh7mf8buPTmDRGggICOVBzhGp7HXNjc3hg3j0iETyLTws2UOOo567sG7g05UKUkqLXdLi+2JuLOL73ZXw+d1eV3lE2Si+MbinBCSgkBYdqj5F5hiQQ6EoCi9fTVHOA8PgL6GxvwNZYgCz7sIRNHvT7gyzLrCvcQqjeSqQprPu10iPv0dZaRnjCQqyROYO6hiAIo5t4BxsDwowhJFvjKWwqxaDRdyd8Jq2RFGvCaReaCz0VNpV2r2PbVLqTjSXb+df+9/n2rNu5OOm8IbtuUVNXA+CM0BSgq3k8gKO5GGtkdvd+sizjaCnDHJIxqqffqLvXBJ77SKAsy3yY+xnxgdGkWBP8FFnfAoyRgEyHoxZD4Nh/WHLj5KuoddSzrXxvr207yvdxc/k+koLi+H8zb+01zUsAj8/LhuLtzIyayhVpF/f7uM72Bgr2/gPkrkIwpqCksx8kCKcwa43EmCMpbDp7U/gAYzh6UzSNlbtpt1XQVL2ve1tD5S4y5nwfSVKc83rxw3V5FDSVcOvUpd0PjerLt9JSd5jw+AuJSbvinM4rCMLYIZLAMWJSWBqFTaXcMOlKlKc0Cc8MTeHjY2twejrRiSauZ2Q/sQ7j6csfJcIURm79cf66/RVe2P0G6SFJRJsjhuS6xc3lKCQFsYFRAOhNUajUBiryPyEofGp30/fKgk/pbK8nOGrmkMThL0qFEoWkGNSawM3dvVHCAAAgAElEQVSlu2hztXPX9Bv6NQIzGPoTjZg77NXjIglUKpQ8cN693Gy/hiN1+aQGJ7KuaCtfz7qcj/PW8tGx1RQ1l/HzNU9ySdJ8ZkRPJSdy8pB/nceKfVWHaHXaBjQC3dWu5WNkn5es8348qh/SCKNblDmc0pb+rekNiZlNWe6HdDiqMVlTiUq5lLbWciryVrB/7c8BCVNwCkZLIobAuF4tJRwtJfi8LkzW1F4//7n1x5EkiUtTFiD7vFQVrqameD16cyzRqZcjCML4J5LAMeLrWVcwNyaHlOCEHq9nhqawPPdzPivYyNLMJSMT3BjhPjFypVKqUUgKJoWl8YsF9/PT1U+wqWQnt0y9dkiuW9JSQYw5sruqpqRQEpl0CeV5H1H45RvEpl+FrbGA2pINmIJTCU8Y/f2Y1Er1oJLA1cc3EW4MZX78LD9G1TdNgBWFUkOHo2bIrzWcIkxhRJyYxnV3zo0A3Dbta8yNzUECXtz9FmuLtrC2aAsPnvf/mBsrpnb5ZB8f5n6GNcDC9Iisfh/naC6mtf4oYXHzRQIoDIo1wMKB6iP92zcim6rCNZiCkkmcehuSJGG0JOBsq6Wp+gA+byeO5mLsjQUARKdegTk4jbryrbS1lOFsqwVAodSg0VnQBFixhk/DFJxCbu0xYk3hOKr3U1iyEZezGb05htSce7ofTAqCML6JJHCMCFDreiWAAJPD0pEkidz6ApEEnsXJQibqU9bxJATFEqgzn7Va22CUNJczJSKjx2uhcedhazpOa/0RWuu7bgg0uiCSp905JtYZaRQqXL6BJ4GyLPP58S/IbyziG9O/3mNUe6hIkgKtPhRne/2QX2s0ODkF9FuzbmNdYVcS+NKet0kKiqPaUcfxxhImhaV1T0+eSCpaqylsKuXWqUsH9L1nbyoAJKJS/NvCRJh4QvVWnJ5OmtpbsOotZ9xXqQ5g6oWP9JryGZ91PfFZ13f/2+vu4Niuv1NZsIrKglUAGC2JhMcvAEmJx+XA6+mgtT4XW8MxXLJMfksbU7Rqyo99iEptICb9asLi5o+qdkSCIAyt0X+3KZyRWqlmUdJ81hdtpa6tkTBD8EiHNGp5+kgCoWudhq3TPiTXbHHaaHa2dvd3O0mSFCRPu4PmusPIJ+IyBMahHCNTehUKJauPb+Kq9Euw6My8tv8DSlrKaXO1AyBJEhJSd/sSlVIFskxrpx1bp4MgXSAXJswdtnh1hlDaWsuG7XqjQbI1nmRrPPPjZ/ObDX/m/pWP9Nj+0jVPYgkYncWHhsqBmqMAnBc7Y0DHtbWWE2AMHzM/n8LoNT1yEq8d+ICdFfu5PG3hWffvT1KmVAeQMft+WhuOAV3FsPTm6F77ed0dOFpK+TB/A+6Wg1ycvoSkoFjMIRkoVWO/aJYgCAMjksBx4LKUC1lbuJldFfu5Kv2SkQ5n1PpqOmhfSeDQjASWNHe1JuirequkUI7ZJtOtThsAP1j5aySpK9lLsMSQZI1HgYQPGVmWkU/+KcsgQSThmLUm7s6+obvAzHDQGUJprvkSn9eNYhivOxpkhaXy6MIf8VHu54TorUyJyOAv217mkfVP8YdLHsao7eqNV9ZSSVBAIKZR2JbEH9xeN6uPf0FqcCJhxpB+HyfLMm22cixhk4cwOmGiiDZHEGoI5lhDYb+SwP5SqgN6FBo73T6BoRkc2P0OMeZIZmVcIdYKC8IEJpLAcSDOEk1QQCAl/VxsPlH1NR0UuqqslrVUDsk1D9bkolQoSbSM/YIkp7oidSGrCjYAXV/Pe3JuZuEQVlgdrK8qhNaMi+IwAzUpLI1JYWlAV1Jz69SlvH1wOQ+vfuJEi5SuKeUAOVFT+OHcbxKg1o1gxP73acFG6toauX3adQM6ztXRiNfdjiEwbogiEyaaREts9wPC4dbQ1kSVvZZbplwrEkBBmOBEEjhOJFhiKWgs7tFAWujJ43MjSVKvtUBDNRIoyzLbyveSHTkZvSbA7+cfSbdN+xo3T7kGnVo3Jr7n9Kauyqwd9qoJmQSeSpIklmYuwaDW84+9b1Pf3kSMOZLYwCgCtSb2VR3iH3v/zQ/m3j3SofZQZa9Fp9SedR1VX1o6Wnn/8CekhyQPuEBOW2vXzfpE/74R/CchKJbdlV/S4XYO+8OWrWV7ADh/GIpyCYIwuokkcJyYE5PNC7vfYHPpLhYkzBnpcEYlt9fTaxQQupLANlc764u24fa6WZJ6oV+uV9RcRlNHC9eGXeqX840maqW6ezrnaE8AATQBQShUOtrtVSMdyqixOOUCkq1xaJQaYgIju19/bf8HrMxfh1lr5M7p14+a/98HVv0GgPduer77tRpHPe8c/Ij9NUe4OPF8FiWfj0pSolR0fXh9XjrcTl7a8zadXhd3Z9844Ou2tZYhKdQEGERVUME/EiwxyMjsKN837DMo8hoKiTKFi/oBgiCIJHC8uCB+Fh8fW8PHeWtFEngabt/pkkATMjIv7H4DgNTgBJL80GT704IN6FRa5onS/CNOkhTojZETrjjM2fT1fX7b1KVU2WtZlb+e5o5Wvj/nrl7raEfS8tzPOT9uJuuKtvDfo591v74yfx0r89ed9rjrJ11BknXgUzptjQUYgxJE2XzBbzJDUwhQ6/jv0U+ZFTMNo8YwLNeVZZn8xiKyI8X6VkEQRBI4bqiVaubEZrM893NcHhcaUemrF4/Xg6qPoiAmbc834I/z1vLDefcM6lodbic7y/dzQfzsCVeBcbSyhE2iIv8TOhy1otfbGaiUKh6e/11e3vtv1hZtoam9mV9c+H3U+Ohsb8DWkIc1aibagKA+j5dlGY/Ljiz7kGWZltqD2JsK8XqcSAolSpWOyOTFdNirqCvbikqtxxqZQ1D4FBTKs//eevvgct4+uByAFGsC35p5Gz7ZR6WtBqVCgcfnxePz4vV5USqUqBUqwozBpIckD/hrIfu8ONvrsYRNGvCxgnA6Bo2e+2bfyf9tfZGffPZ7vjPrdrJCU0GSUCtUQzb6XtfWgK3TQVpw0pCcXxCEsUUkgeNIVmgq/z36KXuqDnFe3MBKoE8EpxsJDNJ9laSlhySztWwPNY56rk5fzIyoKWjPIaHeXr6XTq+LixLnDSpmwX+skTlUFKyi6MvXSZv1XdSa8VkF0x8UCgW3pp6P11bOhoYi/rHqYc4P+OrnoKpwNeHxC4hIvBjV/4xilB9bTn35th6vKdV6AoyR+Hxu7HXHaak7DIBaa6bdVoGtMZ/yvBVMOu/HqLXmXvH4ZB8SEotTLmBm1DSe2PQ3AH6/6CEUiq4S+ucyync2zvYGkH1o9f2vJioI/TE7Zjr3z7mLl/a8xROb/t79enBAEI9f8tNzWvt6NgWNxQCkBif6/dyCIIw9IgkcRyaHpxNqCGb18S+YGp7RXfpd6HK6JDAjJIX58bM5UH2Eh+d/l3/tf5/Npbt4evvLJAbF8viinw54OtyO8n1EGsPEm+0ootaasEZMp6l6H8VfvknKjHtR9PH9MJHJPi8t9UepK9uCo7mI2UCxSsmOTjdfm3IN+oAgJBTUlW+htnQTDZW7iEy6BEv4FFRqPe22CurLt2GwJBASNRMkCZVaT2BIZvd0SkdzMcf3v4paayJ91veQZR8NlbuoOv4ZhQdeJ33293r1RnN6OpGRCTOEMD0yi6cuewSlpOhOAIeKrTEfAFPQwEcRBeFsFiTMYXrkJF7Y9QZ7qg4C0NjRzKcFG7ht2tf8fr1DtXkEqHXEnrIGWBCEiUvcAY0jCknBJUnz+fehj/jm8p8wLSKLXyy4f9QUdhhpXdNBe3/LKxQKfjD37u4ql/fPvYtvzriJ5bmfszz3cw7UHGFmdP/7+Xm8HvIbi5kXO0N87UeZhMk3o1LrqSvbwv61PwdAb44hMDQT2eelufYg1ohswuIvQKUeXxVdT0eWfdga8rE15VNfvh3Z50FSqLBGZhOVvASppYonNz/Hro5Ovp7Utb41KGIqrfVHqSz4lIr8T6jI/6T7fEqVjtSce1Cq+q56aAxKZNrC3wBS989HZNIiQKLq+KfYm45jDk7rcUy7uwMA/Yn/k9jAqF7ndTlb6LBX01J3hIbKnQCExV+A3hyDWmPCZE3uV+PtU9mbjqMNCEartw7oOEHoL7PWyI2Tr6a4uZyYwEgUkoKPjq0mUGfmqvRFfr3WsfrjTA5L71UhWxCEiUkkgePMlWkXE2oIZn/1YTaX7uKLkh1iSuIJpxsJPOnUhE2vDuCq9EtYnvs5dW2NA7rO3upDtLs7mB0zNhvBj2eSJBGVsoQAUxT2puM0Ve+n3VZBu+2rHpvVRWtwtBSTNvPbIxanz+ehqXoftsZ83J0OZJ/7xOteJABJAcggy30e3/erdB8jI8OJNXuujiZk2QuAVh9KRMJFWMIno1LrAchSG1ApVHyY+xnXZi5BpVAiSQosYZMJDMmktTEPj8uBx9UOQGBoxmkTwJP6SsbC4uZTdfwzig++TXL2XRgtCd3bOtxO4Ksk8CRnWz2VBavwuDtwtBSD7Ouxva50c/fftfpQjEGJWEIz8Xo6Uaq0mKwpSFLX54OkQJIkaorXY2vIx+dz09ZaRmiM+P0pDK2EoBiev+YJAGydDn6+5kleP/ABenUAF/upemiH20m1o44LROE4QRBOEEngOKNRaZgfP4u5MdmUtVbx0p63CTOEkBWWOtKhjTi3193d1qA/jBo9SklBi9M2oOtsLN5OkC6QqeGZAw1RGAZKlY6Q6FmERM8iccotQNdoGLIMkoKaorVUFa6moXIXIdGzhy0ud6cdR3MR9qZCGqp2I/s8KBRq9OYYFCotyKBSq07E6+1KWjjDSPNpR6FPjMCdSHoM5hj0gTEEhU9DrTX1StB0ah33zfkGf93+z66RhPD0r86kUGIJzRrspw6AUqUhKmUJ1UVrydv1bPfr4fELOFaXB0D10fc5cPwjVGo9Ko2hu9qr0ZJIUNhkgqNmoTdHdyewHk8HXncHDZW7sDcdp7FyF42Vu84ai0pjJMAYSWBoFmEJC/zy+QlCf5i1RpZd+kt+tW4ZL+x+gzcOfMCUiEzmxEwnSBfI8aZSPs5by/fn3MXUiP6/xxQ1d/2sJFhihip0QRDGGJEEjlMqpYqHzv82v1j7R36z4c8kBcURZQrn+slXEmUafGVEj8+LaoxNKTnbSOD/UkgKAnVmWjr6nwTurTrE3qpDXJNxqZhyM4Z0jQR1/T08cSFNtQcpy/0QgzmOAFPEkF/f5Wzl6PY/43V3jabpDOFEJF5EUMT0UbFuMTtyMiatkWd3vcZTlz3Sa0TOXyKTFmGNyKZg70t0dnSNwNeWbqLV0zW2GRqSjtVgobn2IJ2tXduTp9+JJazvkvdqjRG1xkhM2pUAeNzttNQdprOjCQkJpUqHLHu7Kpn6fICMUh1AaOx5o+LrLkxMek0Av7n4QdYWbuZIXR47yvexo3xfj32e3Pwcz1z5W0L6OVV5S+lutEoNk8LSzr6zIAgTgniXG8fCjCH8/pKf8vKef+P2udlStpstZbsJ1Jn55YLvkxB0bk8ED9Ue44+bn+PrWVfwtazL/Bz10PF4PejVZ56m9r8sOjMtztb+nd/n5aXdb2HWGrnMTw3nheGnUKhIyb6bo9v+TNHBN8ia9+CQ94irLlyD191O0tTbMYdkoFRph/R6A6VXB/Dtmbfxf1tf5KHPfk9aSBIVrdVMi8zCrDWhUijRqXRYAwKJMIYSYQo752tp9VYmX/CzHq91lu2B7a+QmLqE2MAoYtOvwe1yoNaaB7TuVqXWD+voriCcK7PWyHVZl3Nd1uU4XG0UN5fz2Ma/ApBqTaCgqYSffv4EOpWWS1MWsDRzyWnP1eZqZ2PJds6Pm0nAAN8DBUEYv0QSOM5FGEP51UU/ALpGqZ7a+hKtTltXWerFDw/4fLIs88red3B53fz70EdclDiPoDHSB8/t86Aa4NN9i85MY0dLv/bdWLyNZmcrP53/3X4/nRVGJ22AlbiMpZQceZeGyp2ExvpnXc6pfF4XtaWb6exopLFqLyHRswmKGL3rSGfHTOeBeffy/O432Fq2B4DS1so+950ankmgzoRCUiDLMjqVlqywtHNuXdP+P2sCJYUSjW5s/N4RhMEyagxMCc/ge7O/QUFjMVenX8LyY6tZX7QVh6uNfx/6iKvTL+lz9onNaefRDX/G6/NySdL8EYheEITRSiSBE8iMqCm8fcPf+CRvLa8f+A/H6o+TEZoyoHM0dbRQZa9lcfIFrCnczNPbX+H+OXcSaggeoqj9x+1zD2g6KECYIYR91Ye5d/lDXD/pSi5JvqDPabAer4d3D39CXGA02ZGisfR4YI3KobpoLXXl2/2eBLbW51J8+B287naUaj16UzQRiRf79RpD4by4GcyOnkZLpw2jxoBSUuA90Zzd4W6nvq2RdYVbqLTXUuOow3ei4m59WyOrCzfx9PaXmR83i7mxOcyMnoqin9U6T1YHFaMYwkR2UeK87kJvN0y6koM1uZg0BopbyslvLCIztPfa/+XHVlNpq+Hu7BsH/H4vCML4JpLACeiihHm8ffAj/rL9ZZ5a8siA+gl+ePQzAC6In4NFZ+b9Iyu575NfsSB+DufFzSQnqu+1OaPB6VpEnEliUCzQVbHtn/veZV3hFjJCU7gqfRHhxtDu/XZW7qfVaeO7s+4QawHHCUlSYI2aQXXhGlzOFjS6wTdvbq0/RuXxT+mwV6FU60ma9g2Cwqf4Idrho1Kqeox0nyy2ZNQaiDCGMiU8o9cxTreTP2x+lpLmiu5p6T86717mxfZvZLDD7URCQjfKpskKwkgJ1gfx3NWP0+7u4J7lD7Gn8mCvJDC/oYhP8tYyM3oal6ctHKFIBUEYrUQSOAEZtQa+N/sOntnxKq9/+R/uzbkZjUrTr2NXF24CIMESTUZoMtmRk3n38Ao2le4kr7Go30mg1+el1lFPpCl82HrpdRWG6X91UIA5MdnsrNjPdVmX8/nxLzhaV8Dnx7/A7mrjgXn3AFB+ogpr+IlG1sL4ERyZQ3XhGoq+fJO0Wd8ZVLEQj7ud4/tfASAofCpxWV/vrmI53unUOn6z8EEkSaLVaeMXa/7IcztfxxpgIT2kZyP2hvYmXt//HwqbS/HJPjw+L+3uDgLUun6PHArCRKFXBzA5LJ3Pj3/BtRmXYtaZAOj0uPjr9lfQqwO4J+emEY5SEITRSCSBE9T8+NlsL9/HxuLttHS08rMF9/W6wSprqWRt4RZ8+JgfN5soc1dV0eSgeHQnpmWlBCfwywt/wNsHl/PxsTVnrRoqyzKr8teztmgLlbYaJCSeufK3PUbVhspAq4NCV5W2ny24D4C0kCQAXtr9FlvKduOTfUhIPLvzNXw+Hw+e/y1xkzrOaPXBRKdeTmXBKlpqD2GNzD7nc1UXrgEgYcotBEfm+CvEMePkw55AnZnvzbmT3274C4+s+z8WJ1/AvNgcIoxhfJK3ls8LN+H1eZkUlkaoPhiVQolKqSLFmjCyn4AgjFI3TLqSX61bxoq8tdw+7WsA7CjfR317Ez85/9sE64NGOEJBEEYjkQROYA/Mu4dX9r7D+uJt3PzefagUKtRKFR1uJwZ1AG0n1uEArD6+qfvvN065qte5koLi8Mo+dlbs4/y4WX1eb33RVl4/8J/up/oAMjLfX/lr/nL5o0Sbh7YU/7lMB+1LanBidxJbYaumqLmMu7Nv7J46Kowv4QkXUl++jcaqveecBDpaSqgr20pQxPQJmQD+r0lhaTy5+Of8bM0fWFO4mTWFXzV1T7Em8I3p15MRmnyGMwiCcFJaSBLTIjJZmb+Oq9IuxiN7WVWwnkhTGLOiR2+xKUEQRpZIAicwtVLNt2bdRmpwIlX2WhSSgnJbNfuqDpEWkkSKNYEL4meDJLGzfD9euWuUb0ofTdBzIiejVWl5dd97zI3J6bUuzif7+DD3cwJ1Jq6fdAVXpF6MjMyr+99j9fFNrCnczF3ZNyCfKCQxFM5lJLAv0yKzkCSJlfnr2Vy6i0CtiYVJ/q8eKYwOkqTAGjmDmuL1uJytA65KKcsyJYffQ6HSdverEyDJGsdr1/2FTws20O52olfrSA1O7HNNoSAIZ3Z3zk08sOo3fGvFV+1V7sq+YdiWWwiCMPaIJHCCU0gKFiV/VTZalmVaO+1YdOYe+12beekZz6NRafha5hLeObSCSlsNcZbo7m0ur5untr5IraOe++fcxYKEOd3b7p1xCzX2etYVbUWBxNqiLXxv9jeYG+vf0RKfz4dP9qH2w0igNcBCRkgy64u2AvDghd8XBSvGueCoGdQUr6Opeh8RiQMrsGBvKqCzvZ74STf6pbjMeBKg1nFd1uUjHYYgjHlRpnBunnINXxTvoNpRxzUZi7k8VRSDEQTh9EQSKPQgSVKvBLC/5sRk886hFWwv39edBK4v2soLu98EYGbUVOb1kdzlRE3mYG0un+SvA+CZHa+SGBTr13WCbp8HYMB9Ak8nMSiO3PrjKBVKUoOT/HJOYfTSGUIxWBJoqNxNeMJFA3q6XlnwKSq1Aeso7gEoCMLYd7K5vCAIQn/4NQlctmwZGzdupLq6Gr1ez4IFC3jooYcICvpqUXJ6ejparRal8qvpgu+88w7p6elA14jN008/zQcffEBHRwc5OTn87ne/Izo6utf1hNElyhROWnASn+St5eqMS3B0tnUngDdPuea0b06Xpy5kSngGbq8Hl9fFbzf8hdcP/IeH5n/Hb7G5fW4Av0wHBUiwxAAwLTzzjIVwhPEjJHoWpUfe58D6RwiNmUt02pVnTQZtjfm02yqITb8WhbJ/FXgFQRAEQRCGml+TQKVSybJly0hNTcVms/HQQw/x85//nBdeeKHHfv/4xz+YM2dOn+d4+eWX+eSTT3jzzTcJDw/nySef5Dvf+Q4fffQRCoWovDiaSZLEndnX88u1f+L3G5+htLUS6JryeWnKgjMeFxsY1f3vqzMWszz3c3LrC/psfjtQB2ty2VS6E8Bv0zbnxGTT1NHCoqTz/XI+YfSzRmTj6mjB1phHbekXaAKCCIvr+//f3lREa8NRmqoPoAmwEhI7d5ijFQRBEARBOD2/ZlUPPvggWVlZqNVqgoODueOOO9i1a9eAzvHOO+9w7733kpSUhMFg4KGHHqK4uJi9e/f6M1RhiKRYE8iJmkKFrRqL1sQvFtx/xgSwL1elX4JZa+TP217G4Wo751j2Vh3ih6se5fdfPMOmkp0E64PIifJPY+6Ta5kCz3HqrDD2KJRqolIuJX32fRgtiZQfW05zzZfd272eTlzOVtydDo7vf4Xaki+QZR/xWTcMqr+gIAiCIAiCvw3pncn27dvJyOhd6e3HP/4xbrebqKgobrnlFm688UYA7HY7lZWVTJ78VcNxs9lMfHw8ubm5zJrVs/WAzWbDZrP1eK2mpmYIPhOhvyRJ4mcXfG9Q5zBrjdw/5y6e2PR3Vuat56YpVw/4HEVNpfxpy/Po1QHcOnUpS1Iu7G5LIQiDIUkKkqbdzrFdz1KetwJzcBoAR7f/GZezBZM1FZ/XRcacH6A3x4jqfIIgCIIgjDpDlgSuWrWK999/nzfffLPH6//617/Izs5GoVCwY8cOfvKTn+DxeLj11ltxOBxAV+J3KpPJ1L3tVK+99hp///vfh+pTEEbQ9MhJ5ERO5r+5nzI7ZvqAevDJssxrBz5ArVDx1JJHsOpFRUbBv9RaMwmTbiR/zwsc3vJHFEo1LmcL0FUNNCRmLoZA0TdSEARBEITRaUgW2a1cuZJHH32U559/nkmTJvXYNm/ePHQ6HRqNhgULFnDXXXexYsUKAIxGI9A1Ingqu93eve1Ud955J+vWrevx8dZbbw3FpySMgG/Puh2dSst/jqwa0HEFjcXk1h/nxslXiQRQGDImazJRKZfhcbfhcrYQnXYlSdPuIDzhQqJTrxjp8ARBEARBEE7L7yOB77//PsuWLeOFF15gxowZZ91foVAgyzLQNeIXHR3N4cOHmTKla+2W3W6nrKyMzMzeDcrNZnOvUUNh/AgKCGRJyoV8lLuaX6/7Py5OOp+LEudR19bI6wc+oKylq/DMxUnnszRzCdA1Cvjml/8lQKVjcfLA1iIKwkBFJi0iJHo2Xo8TnaGrpUlQ+NQRjkoQBEEQBOHM/JoEvv766zz77LO88sor3UncqY4cOYLP5yM9PR2FQsHOnTt59dVXue+++7r3ufnmm3nllVeYO3cu4eHhLFu2jISEhH4llML4c23GpWwr28OxhkKONRSysXg7R+sLAEgPSaakpYK3Dy4nRG9lfvwsCptKOdZQyC1TrhVrAIVhodaaUGtNIx2GIAiCIAhCv/k1CXz88cdRqVR84xvf6PH6ypUriYqKora2lmXLllFTU4NSqSQqKooHHniAW265pXvfe++9F7vdzq233kpHRwczZszg+eefF+0hJiiDRs8fFv+MTSU7ee/IJxQ0FjM3NofLUi4iKywVh6uNn63+A//a/x5RpnBe3fcuaqWaJSkXjnTogiAIgiAIgjAqSfLJuZjjREVFBYsWLWLdunXExMSMdDiCH/lkHxJSr2qLR+vy+c2Gv3T/+0yN6QVBEARBEARhvDtbTiSaVwljhkLqezQ4KyyNJy55mLq2BsIMIaQEJwxvYIIgCIIgCIIwhogkUBgXUoITRPInCIIgCIIgCP0gFtoJgiAIgiAIgiBMICIJFARBEARBEARBmEBEEigIgiAIgiAIgjCBiCRQEARBEARBEARhAhFJoCAIgiAIgiAIwgQikkBBEARBEARBEIQJRCSBgiAIgiAIgiAIE4hIAgVBEARBEARBECYQkQQKgiAIgiAIgiBMICIJFARBEARBEARBmEBEEigIgiAIgiAIgjCBqEY6AEEQBEEQBEEQBAC3201FRQVOp3OkQxn1lEolFouFkJAQFIqBje2JJFAQBEEQBEEQhFGhoqICk8lEQkICkiSNdDijlizLuN1uamtrqbi1hMsAACAASURBVKioIC4ubkDHi+mggiAIgiAIgiCMCk6nk+DgYJEAnoUkSWg0GqKjo2lraxvw8SIJFARBEARBEARh1BAJYP8NdBpo93F+jkMQBEEQBEEQBEEYxUQSKAiCIAiCIAiCMEAul4sf/ehHzJ49m+zsbNLT09m5c+dIh9UvIgkUBEEQBEEQBOH/s3ffcVLV5+LHP+dMn92Z7b0Xyi4dlioIiAVrRINYSPQqiWkmJl5jrunxpl3vNf5MYnKD5pqoidFo7AoqoNIFVvrCVrb3OlumnfP7Y2FhZYHdZbbB8369eAkzpzyz7syc53yf7/MVA7Ru3Tr27NnDhg0byM3NPe/jdXV18aMf/Yg5c+Ywc+ZM7r//fpqbmwMQ6ekkCRRCCCGEEEKIASorKyM5OZng4OCAHO8Xv/gFBw4c4I033mDjxo10dHTw0EMPBeTYnyVJoBBCCCGEEEIMwPe//32efPJJdu3axYwZM/j617/e85zf7+fSSy/ljTfe6LXP448/zhe+8IU+j9fV1cWrr77Kt771LWJiYggJCeGhhx5i06ZNVFZWBjx+WSdQCCGEEEIIMSo1VO6ivuKTYTlXZMJsIuJz+rXtz3/+c2JjY9m5cyfPPvssABMmTAC6F3G/5ZZbePHFF7n++uuB7sTw5Zdf5nvf+16fxyspKcHtdjNlypSexzIyMrDZbBw+fJj4+PjzeWmnkZFAIYQQQgghhAiglStXkpubS3FxMQAbN27E5/NxxRVX9Lm9y+UCwOFw9Hrc4XD0PBdIMhIohBBCCCGEGJUi4nP6PTo3msTExLB06VJefPFFHnroIV566SVWrFiB2Wxm165dfOlLX+rZdu3atT3zCtva2ggPD+95rq2tLWBzDk8lI4FCCCGEEEIIEWC33XYbr776KqWlpWzevJlVq1YBkJOTQ25ubs+fnJwcUlNTsVgsHDhwoGf/wsJCOjs7mThxYsBjkyRQCCGEEEIIIQJs/vz5OJ1O7r//fmbPnk1KSsoZt7Vardx444088cQT1NbW0tLSwqOPPsrixYtJSEgIeGySBAohhBBCCCFEgCmKwqpVqzh48GDPKODZPPzww2RlZXHttdeydOlSLBYL//Vf/zUkscmcQCGEEEIIIYQYoPvuu6/Xv48cOXLaNklJSURERHD55Zef83hWq5VHHnmERx55JGAxnomMBAohhBBCCCFEgLW3t/P0009z++23YzKZRjqcXiQJFEIIIYQQQogAeu6551iwYAF2u5277757pMM5jZSDCiGEEEIIIUQArV69mtWrV490GGc0KkcCNU3jscceY8GCBcyYMYN77rmHioqKkQ5LCCGEEEIIIca8UZkEPvXUU7z55ps899xzbN68mfj4eL7yla+gadpIhyaEEEIIIYQQY9qoTAJfeOEF1qxZQ3p6OkFBQTz44IMUFxeze/fukQ5NCCGEEEIIIca0UTcnsK2tjYqKCiZPntzzmNPpJCUlhcOHDzN79uyex1tbW2ltbe21f3V19bDFKoQQQgghhBBjzahLAl0uF9Cd+J3K4XD0PHfCX/7yF373u98NW2xCCCGEEEIIMdaNuiQwODgY6B4RPFVbW1vPcyfceeedrFixotdj1dXV3HHHHUMbpBBCCCGEEEKMUaMuCXQ4HCQkJHDgwAGmTJkCdCeApaWlZGVl9drW6XSeNmIohBBCCCGEEEPN4/Hw0EMPsWXLFrxeLx0dHfz1r39l7ty5Ix3aOY3KxjC33norTz/9NMXFxXR0dPDoo4+SmprKrFmzRjo0IYQQQgghhGDdunXs2bOHDRs2kJube97H++tf/8rKlSuZNm0al112WQAiPLNRNxIIsGbNGtra2rj99tvp7Oxk1qxZ/OEPf0BVR2XOKoQQQgghhLjIlJWVkZycfNqUtcGKjo5mzZo1FBUV8dJLLwXkmGcyKpNAVVV54IEHeOCBB0Y6FCGEEEIIIYTo5fvf/z6vvfYafr+fGTNmsGDBgp7n/H4/S5cu5cEHH+T666/vefzxxx9n9+7dPPvss30ec/ny5QC88sorQxs8ozQJFEIIIYQQQogPi7ezsXjrsJxradoCFqfN69e2P//5z4mNjWXnzp09Sd2ECRMAMBgM3HLLLbz44os9SaDf7+fll1/me9/73tAEP0BSXymEEEIIIYQQAbRy5Upyc3MpLi4GYOPGjfh8Pq644ooRjqybjAQKIYQQQgghRqXFafP6PTo3msTExLB06VJefPFFHnroIV566SVWrFiB2Wxm165dfOlLX+rZdu3ateTk5AxrfJIECiGEEEIIIUSA3XbbbTzwwAPcdtttbN68mYcffhiAnJycgHQTPR+SBAohhBBCCCFEgM2fPx+n08n999/P7NmzSUlJOev2Pp8Pv9+Pz+dD13XcbjcAZrMZRVECGpvMCRRCCCGEEEKIAFMUhVWrVnHw4EFWrVp1zu3/8Ic/MHXqVH74wx9SWVnJ1KlTmTp1KhUVFQGPTUYChRBCCCGEEGKA7rvvvl7/PnLkyGnbJCUlERERweWXX96v4332mENFRgKFEEIIIYQQIsDa29t5+umnuf322zGZTCMdTi+SBAohhBBCCCFEAD333HMsWLAAu93O3XffPdLhnEbKQYUQQgghhBAigFavXs3q1atHOowzkpFAIYQQQgghhLiISBIohBBCCCGEGDV0XR/pEMaMwf6sJAkUQgghhBBCjAoGgwGv1zvSYYwZnZ2dg2o6I0mgEEIIIYQQYlQIDQ2lpqYGTdNGOpRRTdd1Ojo6qKioIDo6esD7S2MYIYQQQgghxKgQGRlJeXl5n2vuid5MJhMxMTE4nc4B7ytJoBAXmPaWUtqbS3FGjscaNPA7Q0IIIYQQI0VVVZKTk0c6jAueJIFCXEA8XS3k734Kv68TNd9Exoy7cEaMH+mwhBBCCCHEKCJzAoW4APi9nbiaSyg9/Ap+Xxcp2SsxmGxU5L8z0qEJIYQQQohRRkYChRjDdM2P19PGoa2P4fd1AhCZOI/IxDlompeyvFdprN5LeOy0EY5UCCGEEEKMFpIECjHKuZqKaW3Ix2CyoigGwuNm4OlqxtVYRFXxBnyeNgBUg4WYlEXEZ14FQER8DnVlWyne9xxVhevJmnc/qmHgLYSFEEIIIcSFRZJAIUYxT1czR3evRddOrpdTlvdqr22MpiDSpt5+2tw/g9HC+JyvUHLgBVobjlKR/zZJEz83JHF63a6eJFVRlCE5hxBCCCGECAxJAoUYpbweFwe3/De65iN7wQMAVBa8i65pKKoRg9FCaPRkQqMnnfEYJouDzJlrKN73PLWlmwkOSycsZkpA4+x0VXNo6/8AYAuOI2v+/SiKTDcWQgghhBitJAkUYpTxul2UHPwHrfV5AKRMugVbcCwAGdPvGvDxFEUhZdLn6Wgtp3jf8+iTVxEeN+O849Q1P53tNRTve77nsU5XFZ1tVdidCed9fCGEEEIIMTQkCRRilOhsq6KhKpeako0AWOyRRCcvIjJh9nkf22C0Mi7nXvJ3r6V4/99AUc+rWUxnWzUlB1+ko7UMgLSpq3GEpbHvw0doqTskSaAQQgghxCgmSaAQQ0zXdbxdzXS0VdFUsxeTORivx4XZFkZ8xlUoikJ1ySYqjr4FgKqaiEldTFzGFQEtq7TYwsiaex8Ht/4PJQdeQFUNhEZPRtd1dM2HrvkAUFTjWRvI+H1d5O95Cq+7hZDILOLSLycotHtR1+CwDGpLtxCVfAlGkz1gsQshhBBCiMCRJFCIIaTrOiUH/k5jVW6fz1cXfdDzd2tQNBnT78QaFD1k8RhMNtKn3kHRvucp2vc8cemXU1e2Fa+79ZStFKJTFqGqJhoqd6EoKuNmfQmD0UJ1ySZqS7eArpE25fbTykrjM67g6K4/UnH0LZKzPy9NYoQQQgghRiFJAoUYAp2uGqqLN+JqLsbT2Uho9GQi4mcTFJJIV3sdqsFEU/Veao59BEBIZBZpU1djMJqHPLbgsDQmzPkaBz7+JZUF72I0BxOTuhSTOQhd16gt3ULt8bhOOLj1v0HXAR1rUDTxGVcRFjv1tGM7wjMIj5tJfcVOnJETCIs5fRshhBBCCDGyJAkUIsAaq/dSvO85ABzhmUTEzSIufRmKagDAZHECEBSSTOKE60ckRostnJRJK/F7O4lOWdSr7DQmdQl+Xyea34vJ4sDVVMzRXf8L6KRNXX3OuYSpk1fR1lhIXfn2fiWB7o56WhuO4ulqxRmRiSM8s8/tutpraarZj8/TjjNiPCFREwf0moUQQgghRDdJAoU4D7rmx+93AwqKotLRWkbJgX9gsUWQOWsNVnvkSId4RpEJc/p8XFGU7vl8x6cFOsIzyJr3LTS/h+CwtHMeV1FUIhPnUlW4ntaGo9idiRiMVjSfG9Vowe/rorn2IC31ebjb6+h0VfXsW138AXZnIkaTHdVgorn2IBZ7FKpqoNNV3bNdbenHRMTPImXSLbIchRBCCCHEAEkSKC4KLXV5dLqqiUycE9CGJYWf/oWW+sO9HjOY7KRP/+KoTgAHaqDdPiMTZlNV9D75u9eefUNFJTxuBtHJizDbwqgqXI+7owGftxPv8aTPZHFgNNmwBkUTmTgXd0cDlYXv0VC5G0U1kZJ982BflhBCCCHERUmSQHHB0/weCnKfBqC6eAPOyPFYbJHEpl12XnPwvO5WWuoPExSSTFjMVHRdBwXCY6djtoYGKvwxyWwNZULOV+jqqMfv7aC+Yic2RzwWewRGUxAmczAmSwhmqxPLKclyctZNvY7j6Wo+/WcZAZGJcyk58CL15TuITlqAzRE3HC9LCCEuOLqu0+7toMvrBqChs4lXDr2LoijMSZhOTvwUjKoRj+bF4/fi03zouo6ma+i6zr6aPAyKylXjFqNKZYYQY4YkgeKC52ouAcAYlsmTpYe42n+EJH0vBqOF2LSlgz9uU/dxkyZ8rmeJBHFScFhaT/loTOriQR3jTMm0oqgkTbyB5toDFO17jolzvzUsTXWEEOJCoukav/jwd+yrOdzn83sq9/f7WJtKtvHzZd/FaJBLSyHGgoC+U//85z/zxhtvUFpaisViIScnh+9+97skJib2bHPZZZdRV1eH0Xjy1I899hhLl568GH/mmWd45plnaGpqIisri5/85CdMnDi2m0C4mkuoK9tOctaNGIzWkQ7notLWWAiKSkvEFJqKPmWL38JtIRFUVewmPOkSTAbToJYycDWXoKhGbM74IYhanIvRZCd10kqK9j1HQe6fyZj2BVTD8URQ6Z6jKfMFhRCib16/l62lu9lXc5iU0ESuyFjIoboCtpbuIid+KrdMvo7Kthqau1rRdA2TasJsMGFUjaiqgoKKqij4NB/byvbwScVentrzAl+ZvXqkX5oQoh8CmgR6vV5+8IMfMHnyZLxeLz/72c/46le/yhtvvNFru5/+9KfcdNNNfR7jrbfe4sknn+RPf/oTWVlZrF27ljVr1vDuu+8SHBwcyHCHmUJj1W6sQVHEpS8b6WAuWJ2uGgpy/4zP3dbzmKZ5cYRnUuvrLnU51lLBr1qOP/nyt4ixOvnB9OsxGM1Yg6IJDk0953n8vi4aKj8hOCQFVZW7niMlLHYa8R31VBa8y95NP+n1nKIaGTfzHgxG24DnNApxgk/z89ynL/NJ5T4evvQbJDhjRzokIc7biwfe4OVD76DrOjaTlV9e/hBGg5ErMxfz9TlfxGTo7gyWGpbUr+MtTJnD/9v2NBuKtqDrOl/OuR3D8Y7YQlyoGjqasJms2E22kQ5lUAJ69Xrvvff2/N1isbBmzRquv/56mpubCQ3t3xypF154gZUrVzJ9+nQAvva1r/HCCy/w/vvvc+ONNwYy3GEVHJqCI2IcdeXbiU1bKiMUQ6Ti6Ft4OhuJSlqAqpoAHUU1Epkwl0+LtgJw98xVuLuaqSzeRK7bQ01XKwWHX8WqKoDC5IXf7TVPrS+NVXvw+7qIy7h86F+UOKu49GXYHfF0umqA7rUMdV2nquj940tbQMb0OwmNnjyicYqx6fW89bydvxGAp3b/nR8v/fYIRyTE+dlYtJV/HnybcRFpXJKcQ1pYUq8SzhMJ4EB9dc4Xcfu9bCzeSmVbDT9Y/E0sUqYvLlAen4evvvEwDkswf7ju55jH4O/6kGYi27ZtIzY29rQE8NFHH2XOnDlcd911rF27Fq/X2/NcXl4ekyefvFhTVZXs7GwOHz69Xr21tZXy8vJef6qrq0/bbrSISpiLt6uZurLtIx3KBcnd0UBLfR6RifNIzlpB4oTrSJxwPQnjrsZiD8flaSfIZGP5uCV8bsqNfPnqX3Lb9M8DkDj7a2TNux8UhbqybWc9j9ftorJgPTZHPMFhGcPx0sQ5hERlEZu2hNi0pcSmXUZc+jISxl2D3dl9F7vw079QsOfPtNYfweftGOFoxVhR397IywffZlL0eFZNvp6DtUepaqsd6bCEOC9vHv0AgG/Nu5trxl9GVtS4gBzXbDDx4CX3cu34ZRypL+TXHz/Z3TBNiAuM1+/lp5seB6DN7eKVw++OcESD06+RwO9973v861//OuPzV111FU888USvx3bt2sXjjz/Ob37zm16P/+pXvyI7Oxur1cq+fft48MEHaW5u5sEHHwTA5XLhdDp77eNwOHC5XKed9y9/+Qu/+93v+vMSRoXQmCnYHPE0VOwkOnnBSIdzwakt2wKKQmzaZX0+7/F5sJ4yH9NgshFxvKtku99PSkQCYTFTqDn2ESZrKDEpi047htftIn/PWnzeDtKnfXFQcwnF8IhJWURMyiK6OuqpzH+Xppq9tNQfxmC0kjThc4THz5L/f6KXdk8H/7vrea7KXMyk6PH848Ab+HQ/98y8FbvZxosH32Rj8VZunzp2q1LExW1n+aeUtVRyy+TriA4O/DJGiqJw54zPYzNZ+OfBt9lS+gkLU/pek1aI0UjTNTw+Dxo66KDR3QVXB3Rdw+3z8Nvt/0d+QzHXjV9GtauO9QUfcVP21ZgHOYo+UvqVBP7whz/ku9/97hmfN5t7D4Hu2LGDb3zjGzzyyCMsWbKk13Nz5pz8MJg5cybf/OY3efTRR3uSwODgYNra2nrt09bWRmTk6R9Wd955JytWrOj1WHV1NXfccUd/XtawUxSVsOjJVBa+h8/TjtEcNNIhXTA6XdXUlW0jLGYKFltYn9t4NN9pXctOlKp4te7R6KQJN9BUvZfyI69Tmf8O6dO+gN/vJsiZiMUeScmBv9PZVklK9koc4elD+6JEQFjtkaRPW43XfQMtdXlUFr1HycF/UHb0DaIS5xMclnq8iYwBiy0c8xl+f8SFrcvbxfN7/8X2sj1sL9vDF6bdzIcl27kiYxGJId03i2bGTWZT8TZWTb5e5juJMenv+19DURQWp84b0vOsyFrOtrI9PLf3X8xNnDHoElMhhtO7+Zt49tOX8Wq+c2573YTL+eL0m9lXfZhdlft45dDb3Drlc8MQZeD0KwkMCgoiKKh/CcuHH37IAw88wC9+8QuuvPLKc26vqr0rUidOnMiBAwdYvnw5AJqmcejQIa655prT9nU6naeNGo52zogJVBaup75i53ktTyB6Kz/6FgCJ46494zZevxfzZ5q4mFRTz3MAJouTqYt/SF3ZdqqKP6Ag988ABIWmkpB5Fa0NR4lLv5zIRLmzOdaYLE4iE+cQHjed+opPaKzaQ3XxB1B8chuD0Ub2gu9c9Os8Xoye/ORZtpftAbpHM57d+zIAX5h2sonZ0rQF7K7cz97qQ8yMn3LaMTTNh+Zzo+t+DCa7NI0So0prVxsVrdXcPvVGooIizutYuq7j93VSX74Dn7cd1WAmKnEBRrOdppr9+H1d3D7lBh7d8ie+/c5PSQyJ5/PZ15AZkRqYFyPEedJ1vacaqM3t4pncl/j42E4Abp1yAya1u3O8enybE2tgqopCqDWEOYndvUsmx0wgwRHLkfqiEXgV5yeg31Dr1q3j4Ycf5rHHHmPx4tPXBSspKaGuro6pU6diMpk4cOAATzzxBNdee/LC/dZbb+VnP/sZV155JRMnTmTt2rUAXH75hdGAwx6SRHBYOlVFHxARn4PJ4hjpkMY8ze/F1VRMZPzss47ieP3e0+5Gnhi69/hP3vUxWZzEZ15JRHwOZUdep6XuIO3NJZQdeROjOZjoPspExdihGsxEJ19CVNICutpr8B+/aO9y1VB6+F9U5L9D2pTbRjpMMcwO1h7t+fuXZt3On3Y9z5LU+VhNJ0vIZ8ZPwWkJZmPxNmbGT8Hd0UDNsY+oK9uKohjQdX+vY1qDokmdtErWERWjwoHjv+NZUZnndZzq4o1UFr6Hrp3o56AAOlWF74Gigq4BoKKwJDSKIx4feyr3YzVauH/+Ped1biECodZVz31v/YjvXfo1psVm86uPnyS/oZiZcZNZM+s2IoPC+30sVVH55RUPjcmGjwFNAn/961/T2dnJ/fff3+vxtWvXkpOTQ2trK4888ghlZWUoikJMTAw333wz99xz8kPh2muvpa6ujvvuu4+mpiays7N56qmnxvjyECcpikLShBs4vP1xKvLfJnXyqpEOacxrrj2A5ncTGjP1rNt5tdOTQNPx8tATI4GnstjDyZxxF/UVOzl28CU62ypImXQLRpM9cMGLEaMoCrbgk+3+HWHpdLRVUl++neikS+TC/SLi9XvRjl+4XpGxiMszFnJ5xsLTtjOqBhamzGFd/occOfQKroodPRe84XEzsdgjMBgtKIpKV0c9dWXbyNv5O2JSl5AwbvmYvEgQF46NxVuxGa1khKUM+hgV+e9QXbwBk8VJVNIygkKScEaMx9VUTGtjAbrmw2gORlWNdLRVMLfiE+ZaNF7zWzlSfRB3Z9MZp2wIMVz21RxGR+eZ3JeIC44mv6GYW6fcwE3ZVw/qeKfeLBxLApoEbtiw4azPT506lddff/2cx7nrrru46667AhTV6GN3JhCZOI+Gyl0kTrhekorzVF/5CWZr2Dnn6Hn8PsyGM5SDnqX+O+h4h0nVaCUiftZ5RitGs4RxV9NYuZu6iu2SBF7AdF1nW9ke/nX4XWpd9XT6ugBYPW0F1004e9XJktR5vH10A/88spGVyVNImvA5rEHRfTYZiklexLFD/6SmZCNedwupk1dJIihGTHlLFbMTpp02N/5cutrrOHbwJVzN3bXzQaEpZM64B+Mpa6MFh6URHJZ22r4J466loXIX8YfXk9dSz6dbH2PavG9iDYo6vxcjxHnwHq/+qmqrpaqtlsvSL2FF1vIRjmr4yYSFERKVOI/68u3UlW2TxePPg6ermbaGAuLSl53z4srr9xL0mQU9zzYSeII1OJa0KbcTHJoqF3AXOKPJTljcDJqqPyVp/PUYxugCsBebjUVb2V6+BwXl+BwOFUVRCLU6iQ6K4NrxyzCoBraV7eZofTFH6wvJbyzBqBpZln4JYbYQouwRLEie1TPv40yszflMMRvZ6/ExLzSb8cExZ9zWbAsjc+YaSg68QGPVHjS/m7Spq2WuoBh2Hr+Xxs5mYgbYEdTT2cShbb/pKf2MTrmUxHHXoPSzMZLRZCMmZRFzTGFs+Pj3VHjc2Hb9L+NmfQnbWd47QgylU2/8r562ghsmnruHyYVIvolGiN2ZgDNiPNXFG4lJuRRVOmcNSkPlbkDv1whdX3MCT/zbq505CVQUhfC4GecVpxg7ohLn0VCxk7qKHcSmLhnpcMQ5vFfwMWt3/w2A9LBkNF1D03W8fi+fuPaio/Pq4fVMjp7A9vLuxi/xjhiWpS/kC9Nuwm7uf6Lf1lhIRf673JQ0hYbaSv6+/zUuTZ171s6HiqKQOnkVqmqkvmInedufYMKcr2Ewjs3yITE21bc3oKMTE9z/ETi/z8PR3X9C13xMnNs9ejfY39vJ0eNRFAVXRDbelsMc2vrfRMTnEBGfQ3BYmtxgFcPKdzwJ/M3VPybBGXuOrS9ckgSOoKikS2htOEp7S5ksNzAIuq7TULmL4LB0LPZz3930aL7Tk8Djd+RPbQwjLm5BIUkEhaZQkf8OtuBYQiInjnRI4gxa3S7+L/dFwmwh/OeyB0/reKjrOu8Xbua5va+wvXwPGeEpPHzpN3BYBj7H3OtxUbTveUwWBxOm3sHdjaX854dPsK1sD5emzj3rvoqikpz9eSz2CCry3yFvx29JnbyKoBApORbDY19NHtB9A6S/mmr24u6oJzlrBUEhSed1frPRTJQ9nEa/n6x536KyYB0NlbtoqNyF3ZlI6uRVveZoCzGUPH4vCsqA3g8XIkkCR1BwaPfkbFdzsSSBg9BYtQd3R32/l9roa4kIg2pAVdSzloOKi0/a5Ns58smTFO97nkmXfFe6+I5S28v24NN8PLTwq322vFcUhSsyF7EodQ7NXa1E2cMHtb6fz9tJ/u61+DwuJsz+KkZzEJNjJhDviGFd/qZzJoEnYolNuwyzNYxjh17myCd/ICFzOXZnIqrBjC04VipCxJDw+X28fOgdEpyxpIf1/8ZDc81+zLZwIhPnBySO1LAk8uoKsAXHkTnj33B3NFJXvp2akk0c2vo/AEQmzMUZMR7VYEI1mFFVEyarU5btEQHlO75udF9zuS8mkgSOIKM5CGtQDK7mkpEOZVg11x6koXIXAKrBgmowoWt+dM2HrmvYnYnEpC7G09lAe0sZna5qYlIuxWg+uValq6mYY4dexuaIIzxuZr/O21c5KHSXhPZnYVBx8bDYw0mftpojO39PQ+VuYtOWjHRIog+H6vIJt4WSHn72bodWo4XYAZTBfVZF/tt0tlWSnP35nuYXqqJyVeZi/i/3RQoaSvq9/ll43AyCQlIoyP0z5Uff7HncZA0lPuNKIhNmDzpOIT5L13We3fsKLV2tfGPunaetzXwmfm8nbY0FRMTnBOxCeUbsJHaWf0pFWzWJzjgs9nASx19DZMJs6sq2Ulu6mfqKHdRX7Oi9o6KSMe0LhEZPDkgcQnj6GBS4SUesGAAAIABJREFUGMlPYIQ5I8ZRV74dn7fjgu0Sqvk9tDUVo6omOtrKKT/yBopqwmILR/O70TQfimJAVY1ompemmr1UFa5HO2WeXnXxBjJn3IPX00Z9xQ7am49hMNlJn/qFfjdZ6KscFMCsGmUkUJwmKCQZiz2S+oqdkgSOUvkNxYyPGNoqCl3XaarZR1jsdKISe4/4LU6bx9/3v8a6gg8HtAi2xR5O1rxv0dleg+Zz42o+Rm3pZo4dfJHSQy9jdyaQNPHG8y7BEyK/oZh38jeSGZ7KlJj+l7bXlW9D07yEx/fvJmt/nDj/vurDJDrjeh63BkWRNPFzJI6/Dk9XE5rfi+b34vd78Ps6KTvyOkX7/saE2V+V94QICJ/fh1EqLyQJHGkRCbOpLd1MWd5rF1z78IaqPbQ3l1BXtq3X40ZTEFnz7++zvEPX/FQUrMPrbulOihWFztZK2poKKch9GgBVNRGdvJCY1CWYrSH9jsfr9562RAQcHwmUJFB8hqKoRCXOo/zom7TUHyEkcsJIhyRO0dzZQl17A8szlwzpeTydjfi9HTjCM057zm6ysTh1HhuKtvCFaTfhtPa/bFg1mAhyJgLgCM8gNnUx9RU7aGsqpqn6U/J2PIEjYhzO8EyikhZiMJoD9prExeP9ws1YjRZ+uORb5+x8e6rmukPYnYkEh6YGLJbo4EhSQxPZWLyNq8ctPW2EUVENfc7vt9qjyNvxBHk7f0ds2lLi06/od3dSIfri1XwyEogkgSPO7ognNGYqjVV7ussgUxaNdEjnrbZ0C7WlW3B31PU8FpU0n9DoKRhNNiy2iDO23ldUA4njrzntcXdHPZ2uWgxGMzZHQq/1ifrDr/nRdK1nXcBTmVQjHikHFX0Ii51OdcmHFH76FyZd8u9YbOEjHZI4Lr+xBIBxEaevTRZIrpZjwMn1Qj/rqnGLWVfwIf+X+yLfmn/PoM+jqAaikhYQlbQAd+ZyyvJeo72llLaGfCoK1hEeO43o5IXYHQlyASz6pcPbybay3VySMhvbABaz9nk7aG8pIza1f/PtB+LyjEU8tfvv7CjPZV5S/0YZbY5YsuZ/m6J9z1Fd9AGuxiLSp63GZHEGPD5xcfD6vQNeL/NCdOEMO41h6VNuJygkmerijWhjPBnxedqpyH8Hd0cdsWnLmDjnPsbNupfkrJtwRozD7kwc1NprFnskodHZOMIzB5wAwsl1AM84J1BGAkUfzNYQxs/6ErrmpbZ0y0iHI05xoOYIRtVIetjQloc11+zHZHFic8T1+XyiM46laQvYVraHyraagJzTYo8gc+bdTFv6k+41SkNSaKzKJW/Hbzm84wk626oDch5xYVtf8BFuv4fL0hYMaL/mmv2ga4RGTwp4TEvT5hPniOa1vPUD2s8aFEX2/G8Tn7kcV3MxBzb/mqbqfQGPT1wcvJqvz0GBi40kgaOAohqITVuGz9NGW0PBSIfTb1WF73Fo62Pk7fgdjdV7AagoeAfN72HCnG+QMG45QaHJOCMyRzTOlq5WSlsqgZNLQpzKZDBKYxhxRjZHHKHRU2io3DXmb9JcKHyan62lu5gVPwXzEJZJ6rpGa0M+IVHZZy3Vv3nSNRhVAy/sfz3gMYTHzWDCnK+RveDfiU1bSmdbJYe2/Q8drRUBP5e4cLR2tfHKoXeYEJkx4NHyxqpcLPYo7MfLlQPJZDCxPHMJhY3H+Lhk54D3j0tfRubMNaCoFO17lmOHXqajtUI+m8WA+DQfJhkJlHLQ0cIZMQ4UldaGI4REjf51ydwdDVQWvofRHITP46J43zHaGvKpr9hBWOz0nuUvRpLL086fd/+DLaW70NEBSAlNOG07syojgeLsohLn0ly7n4aKnUQlDeyuugi8vLp8WtxtLEqZM6TncTUVo/nd55wXFR0UwZUZl/Lm0Q8oba4guY/PmfNlC44hYdw1hERmcXT3WioL15M5498Cfh4x9um6zq8+fpIun5u7ZqwcUHdPze+hrbmYmJRLh6x9/rL0S1hf+BG/2/kMxc1lXJKcQ8Y5OvyeKiRyAtMW/5Civc9RX76d+vLtmG3hx9d0VbrjVlSMJjvRyZcMeoF7ceHy+L19DgpcbOQnMEqoBhMhkROpL99BfOZVo/5Dq7pkE4qikj3/2/h9bo7s/D31FTswmoKIS7tspMOjy+fmxxseo6ylkknR41mSOh+nNZisqHGnbWsymPD4PCMQpRgrHBHjsDsSKD38L1obC0mddAsGo2Wkw7ponVj4OruP93Mg1Rz7GIPR1q+yuOXjl7Ku4EMeXP9z0kKTmJc0kxuzrgp4TMFhacSkXto9N6qpuGfJCiFOeC1vPQWNJdwy+boBJVcA7S2loGs4woau667ZaObBhV/h8a1P8eaR93nzyPvYTFaMqhH1eBKnKAqqouIwB/HF6Tcz+TOdTVWDmYwZd9HeUkpr/VFqSzfTWJV7/Fkdv68LgKqi95kw+2vSVVT04vP7BlxF4u5o5MgnT6LrGsGhyYTHzkA1WkAHTfNiC47BGhQ9RBEPDUkCR5HYtKW01B2iufYgEfGzRjqcM9L8HhoqdxGRkIPJ4sRkgYlzv0Ft6RYiE+dhC44Z6RDZU7mfspZKVk9bwQ0TrzzrthajGZenfZgiE2ORoqhkzLiL8qNv0VT9KRUWB8kTbxzpsC4qB2qOYDNZiQqK4NXD60gJTSTYEnTuHQeoufYA7S1l+P1uWuq6P4v7c1MuOiiCHy65n//e8keKmkopairlcxOvHJLRlJiUxdSX76As7zUmzr1PGsWIHpuPfcLf9r1KeljyOb/7+tLWVAwoBAWwK2hf4h0x/NdV36exs5n3Cj6m09eFT/Oh6zq6rqOho2kam0q28fMPf8s359/N/KTe10WKohIcmkpwaCrxmb1fa3trOdVF79PaUEDhp8+QPf87vdYaFhc3r+YjSO3fsmy6rtFQuZvqog/wulsIDsugufYgzbUHe20XHJrGhDlfG4pwh4wkgaNIUEgyBqON6uINhEROHLUfWO0tZeiaj9Cok3fHLfZIkiZ+bgSj6u3NIx8QbgvtV/t4i8GM2y8jgeLszNZQ0qfeQRE6DZW7SRx3LaqsMzRsfrbpcQBun9qdfK+etiKgx9d1nS5XDYV7nwVdRzWYMZqDiUyYe+6dj5sYlcFvr32Ev+9/jXfzN1HYeGxA6wf2l9FkIzbtMsqPvE7+nqdJzr4Jax+t9cXF5+NjOwm1OvnJZd/BPIjPp9b6I9idA+/APVjhtlBWTbn+jM9fkbmIX330e36z9SleCX2X5ZmLmRqbhdPiwHKWkZwgZyIZ0++irbGQo7v+SN7O3zM+594BLSslLlwD6Q7aVL2PYwdfRFEMpE5aRURCDp6uZrzuNnTdDyioBtOY7B4uSeAooigqieOv49ihlzi0/XFCoybhjBxPaFT2SIfWi6u5GICgUTDvry9VbbUUNJZw5/TP92u432K0UNVWy4aiLVyWfskwRCjGssiEOTRV76W1IZ/Q6NH13rwY/G3fq0yKHs+02MH/7NsaC2hrLELXNXTdj675aG0soMtVDYrK5IUPYbFHDOrYNpOVz0+6lnfzN3Go7uiQJIEA0ckL6Wqvpb58Owc3/xpHeCaO8Ex8HhdBoamExUy5oNadFefm1/zk1RWwKGUO1kGUq/t9btpbh2ZpiMEaF5HGb697hFcOvcu7+Rv5313P9zw3JWYiWVHjyAxPYXpc3yXbjvAMUrI/z7FD/yRvxxNkL3igew1icVHrXiewfzdJmusOYjQFMXXJj3o+U83W0D7Xuh5rJAkcZSIT5wA6VUUfUF++nbqyLYzPuRdH+Mh22DxB1zWaqvdiDYoZtR+kh2qPAvT7IvHE3dI/fvIcgCSC4qyCw9JRVCOupsJ+JYE+bwfAqH2/jDUKCvfOXt3v7XVdx+dxYTQHoWt+Sg6+SFP1p8cPpqIoKopiwGiyEZW0gPC4GYNOAE9wWoJJcMTyztFNXJW5pM8RixONCQZbLqooCinZNxOVOJfqkg9pqc+jrfF4d+nSzdSFpTN+1pelVPQisrF4K52+LqbGZg1q/xPzAYPDUgMb2Hmym2ysnraCm7KWc7ShmEN1R3n18DqKGo+xvyYPRVF49ub/d8aRz8jEuRhMNor2Pkvhp3+R94XA6/f1ayRQ1/y0NhwlJDLrgrypJkngKBSZOJfIxLn4vB0c+PhXVBVtGDVJYHtLKZ2uapKzAluKFSi6rvNq3npigqOId/ZvbmJRU2nP3//4yXNEBUUwJWb0d2gVI0NVjQQ5k3A1l5x1O13zU1n0HtXFG1EVA3EZVxKdshBVOpKdl/+94ZeE2vpX0qX5PRQf+AfNNfuwOxMx28JprtlHWOw0kibcMKSLTd886Rqe2P5ndpTncmnqyZJSj9/LK4fe5pVD75IRnsLKSdcxM37yoM9jdyaSPvUOAHzeThqr9tDRWkZD5W4q8t8hccJ15/1axNjw8bGdhNtCmRk/ZVD7d3+mKQSHjM4qH7vZxvS4bKbHZfeUhb99dAPP5L5El8991vLXsJipxGcup7LgXQ7v+C3jc74sN+YuYl7N26+RwNaGo/i9HYTFDP4zejSTq5FRzGiyE52yiKrC9bg7GrHYR77euL2lDIDQ6MF9yQy1kuZyalx13JtzB2o/79pckbGI/IZinrz+5zz47n/yTv4mSQLHIE3XKG+pwmgw4vP7MKoGHJZg3D4PGjoGRcWgGrAazFhN59d9NzgsjeriDbg76rH0MRdL1zWK9v+N5pp9GEx2dM1PRf5btDYcwe/txO934wjPJDx2GopiwGQJGRXv79FK13UUFFZkL+93AlhV+B61pVvwedsxmoLoaC2no7WcmNTFJI4f+sRoQfIsnt/3L3aWf9orCXxq19/ZVLINgMLGY/zq49/zvUVfY1psNobzHJ0wmmxEJ3dXMnjdLmpLNxMeNwO7M/BLVojRxaf5KWw8xuUZizAO8veotT4PuyMewzDNBwwE6/GmTW6fGyzBZ902Nu0ydM1HVdH77P/oF1iDojGZg0kYf+2oaGgnho9XO/tIoK7rtLcco+zIGxhNQYREDm50fbSTJHCUi0yYTVXhezRUfkJ8ZuDbjQ9UZ1sFRrMDk8Ux0qH06eOSHSgozBrAndAlafNZkjYfgGUZi3gj7z2O1hcxPnLoWmSLwPtr7j95O3/jObczqAbmJ81iXuIM5iROH9S5IhPmUnPsI2qOfURy1k29ntM0H8X7nqe59gAR8bNJmbQSgCM7f09bYwGqasLmiOtZ36qbQuaMfyMk6sL8ojlfXs2Hjo7F0L+W3vXlO6ksXI/NEUfihOsJj5tBVdH7mK1hRCbMHuJou6mKypToieyq3Iema6iKyquH17GpZBuXpy/ky7PvoNXtYs2rD/Krj59kRtwk/uPSbwTs/MlZKzi49b8pPvAC2fO/M2RrvonR4VhzOR6/lwmD/N7ydDXT3lJKwrirAxzZ0LIeL7V292OZJ0VRiM+8iqCQFBoqP8Hn7aCl/jCu5hKy5n8biy1sqMMVo4TX7zvjOoHd3+F/o7l2PwDJ2TdfsOXDkgSOcmZrKI7wDBqq9hCXMTTtxgeio60SuyN+RGM4E6/fyzsFm5gcM77fowWfdWXmpbxX8BE/+OBR5ifNYuWka0kMiQtwpCLQvH4v6ws/BuD6CZeTGZHKseYKjtQXcklyDgbFgF/X8Gk+Pi7ZweZjO9l8bCd3zVjJ1eO6myD4NB86gK4DdP+d7jmjn33fWezhhMVMpaEql4Rx12E4Zc5X7bHNNNceIDp5IYkTru/Zd9ysNbiaj2F3JGA0d49MaX4PmuajaO+z1FfslCTwDE6s43m2boAneN2tlOW9it2ZxMQ5X+/58o7PGHi7/PM1LS6LTSXbOFR7FIvRwt/2vUqiM467Z64CuucO3ptzB/+763lyqw7icrcHbNkLiz2ChMzllB99k/aWY+dc8F6MbUfqCwEYHzG4JLC9pXtahCN8aNfeDDTz8RtDXT53v/cJiZpISFR3tY+rqZiju//EoS2PEpW0gLDYqdiC46Tz8wVM13V8mg/TGf4f15fvoLl2P+Fxs4hLv2zMrf03EJIEjgHhcTM5dvBF2ptLRnRhYL+vi05XDSGpo7NU8r3Cj/Frfq4eN/jF6qODInhk2b/z7N6X2Va2m9yqA/zm6h8TYZc7hKNZbtVBfJqPhy/9Rk+XuM+uKXXC8nFL8Pg8/Md7v+KZ3Jf4qGQH9R2NtLpdfW4/PTabb82/hyBz7/kjkYlzaazaQ3PNPiIScgBwdzZRXbKR4LD005ZMMRithERO6Pn3qYsXh8VOo7lmH7rmv2DvOJ6PLn/3BV5/RgLL8l5D03ykTl414j/L2fHTCDLZ+NfhdbR7OrAYLfz88u/2KkNalrGQOEcMP9n4GC/sf501ObcF7PyRiXOpLFhHQ8UuSQIvcEfri4iwhQ36u8rVVIyiGLA5xtZNzxMjgZ5BLvMUHJZG+tQ7KMt7nZpjH1Jz7EPM1lCSJt5IaHTfHUfF2ObTfABnHAlsqt6LLTiOtCm3DmdYI+LCa3VzAQqLmYpqsFBfsXNE42is/hR0jZCo0ffBqOkabx3dQFpY0oBKQfuSHJrA9xd/k4cv/QZdPjd7qw8FKErRXx6fhy5vV7+331WxD4c5qN9zOc1GMz+57DvMSZxOeWsVbp+HhSlzuG3K57h96o09fxYkzeLT6kM8tnUtuq73OkZwaBoWexQVhevw+zy4Oxo5svP3+L0dxKVfPqDXGxI5Eb+vi9YT3R1FL/0dCfS6XTTVHiA6+ZJRMcfHbDRz1bgl7K/Jo6iplGXpl2DrYz5qdvQ4ZidMY33hR7xz9Nwlzf1lMFoJi51GQ9Vu3B2NATuuGF10XedQXT5Z0YMbxTuxGHZwWNqYa1xlOb4URlc/ykHPJDR6MpMWfpfxOV8hPnM5nq5mCj99hrydv+vpgyAuHF7/8SSwj5FAXdfoaKvAEZ4x3GGNiLH1br9IGYwWwuOm01C5m/jM5SO22GlbYwFmaxhBIckjcv6zeT3vPeraG7htyg0BK5mdEjMRVVGpba8PyPFE/3R4O/n22z+lqauFr835IonOOHKrDrAwZQ5xjpNlGc2dLbi8HSQ64yhuLiMjPGVAjTUclmD+/ZJ7z7ld7P5oXjn0DkfqC5kYdbJLr6IoxGdcQfH+v1FbuhlPZwNeTxvjc74y4C+QkKgsDCY7taWbcUaMH/Gy79HG7fcCJy/4zqStqRB0jfC4mcMRVr/cnH01XT43qaGJLE6dd8btvjH3Ln6y4TH+tv81lqTN7zNZHIzYtKU01XQvdjx+9lcCckwxupS1VNLc1Up21OCSwNrSzfh9nUQmzD33xqPMieoAt7//5aB9UVUjjvAMHOEZBIemcezQP2lvLiVvx29JmngDUUmXyOfyBcKrdX+f9DUS6OlsRPN7sAWPrRHxwZIkcIyITlpIfflODm7+NZkz7x72JSO87laaqrtbq4+2D0Kf38fbRzeQFZXJJcmBa/pgUA2EWp00drYE7Jji3PZVH6apq/tn/uTOv/Y83tTZwpdn38Guir1sPvYJBY0l1LY3MDthGuUtlcw4w2LB5+vGrKt4J38j7xV+3CsJBAiLnU5NyYdUFrwDdDeMGcwdRFU1EpU4n+riD2hrzMcZMT4gsV8o3L7+lYO2N5egqqZRNW/ZZDBx14yV59zOZrJyz6xb+cEHj7K1dBfLMhYG5PzWoGiikhZQU7JpVM/pFoO3ufQTVEVlTsK0Ae/r9bioyH+HoNAUwmKnDkF0Q8sygMYw/eUIT2fywu/S0VpBQe6fu0vM/T5i05YE7Bxi5HhPlIP20R20o60KYMyVRQ+WlIOOETZHLCnZN6NpXqqLA1cu1F9VRR8AOjEplw77uc9la9lumrtauSn76oAnqDaTlS7v+d1hFANT0lyOoij857IHex4LsTg41lJBfUcj/7X5j2wt201tewMAn1Tsxa9rTIwcmvINq9HCopQ5bC/bQ7uno9dziqKQPv1OQqKyiYifTXzm4JuPxKUvw2CyU3r4VXRdO9+wLyju4/N9zOdIAl3NJdhDkkd8LuBgjYtII8kZx8vHR54DJSZlEYpioKbko4AdU4wOmq7x8bGdTIvNxmkdeNfuiqNvomt+UrJXjsnFsE9UBwQyCTzB7kxg8qL/wO5MoiL/LRqr9wb8HGL4nSgHNfYxEtjpqgKUUTGdYDiMvXf8RSwycS6RCXNpby0/bX7SUNI1P03VewmLndarmcVooOs6bx35gERnHFNjAt9Z0Wq00Onr/9w0cX40XePVw+uId8QwPjKdR6/6Pj9e+m3mJc0kv6GY327/P4Cess85idNJDkkgLTSJqbHZQxbX4tR5eDUfO8s/Pe05iy2MzBn/RurkW85r8XHVYCIhcznujjraGgOXAFwITlzgWc8yJ9DrcdHRWoFjBJtnnS9FUbh+4hXUdzTyww/+m7KWyoAc12RxEpEwm8aqPXS2VQfkmGJ0yKsroKGjiUUpcwa8r675aazeS2TinDF70WsNUDnomaiqkfE5X8ZsDaMs71V83o5z7yRGtZPfJ6dPL+hsq8Jij0Tt53JEY52Ug44xNkc89RU78LpbMFtDh+Wc7a3l+LzthEZPodpVx6dVB7ky41JUdeTvIRQ1lVLcXMaXc24fkjJVm9E6oAYl4vwcqDmCpms9DV5SQhMBei6GD9cVoCoqP15yP3/f/zp3TV9JZNDQL7KeGZ5KTFAk28p2szR9wZCdJyI+h4r8d6iv2IkzYmy1ah9KJzr/mc+SBLbUHQJ0QqMnD1NUQ2NJ2nz2VB1ge9ke9lYfJikkMOWbsWlLaajcxaHtvyE+40oiE+aM2vVeRd/q2hv4sGQH10+4HIvRTG7VAV49vB6r0cLsQZSCNlTtQdd8Y7r8/MRnwvk0hjkXg9FKctYKCnL/TH35dmLTBt+BfKS1t5Ti6Ww+5ZHeAwqBG17QP3PAE0sv6aBpoCiERk/ptbzScDlxw6CvypJOVzW2i6hkXpLAMcZ+vE65s6162JLA1oajADjCM1ib+wqbSrYRZLKzKHXgdx4DLb+hGKBnWYBAs5qsZ1w6QATehqItBJuDWD2t9wLswacsz6DpGhOjMvnpZd8ZtrgURWFKbBZbSj9B07QhuwGiGkyEx82gvmInPm8HRpP93DtdBE5c4J1tTqCruQSDyX5BfIF/Z8GX+Pob3+/5fAsEiy2ccTPXULDnaSoL3qWy4F2gex5r99posRhM9jHXHfJi8vzef7G1bPfxSok0fvnR7wG4ImNRv9bQPJWm+ag99hHW4FhCR2HH7/5SFRWzwTToJSL6KyQqC0f4OKqKPiAycd6IfTbrug66hqZ50fzenv963a00Vufi93ag6zq6rqH7vWi6D93vQ9N8+Lwd+DxtIxJ3X8y29SRNuH7Yb9ydqbLE73Pj7mggYhQ1Fhtq8mk/xljsEQC4OxuG5Xxedys1xRsJCk3BZA7u6ZT5woHXcfvdXJo6D/MILqpa0lyOwxxEhG1o1vGzSTnosMpvKGZ6bPZpv1PzEmdyJLOIdQUfMil6ZO5aT4mZwPuFH3Ow7mi/l6IYjMiEOdSVbaWx6lOik/s/6tjVXofJ4sTVVNx9AaD7+7WfyeIY9WvInbjAO9uFbkdLOUHOxFHXuGqwxkWkcTSASSB038ibdtnPaK0/Qlnea3i6mqiv2EF9xY6ebRTVSFjMFGzB8Wh+NygqimJAUVQUVcXuSMQRPrgFycX5OVER8dKBN6k4XtY7LiKNmyddM6DjdLXXknd8OZuUSbeM2Tm0J1gM5gEtFj9YieOv5fD2x9m78cdMXvQfWGxDU4XS6aqm01WDq6mI1oZ8QqOyUI0W0HWaavbR1V57xn2tQdEoqhFFUVFVE6pqRjXaUVQjqmrEaHEQHjcDVTnl8v+0j8zAfob2/kxWUBSVptr91BRvovDTvxAaPYnkrJuHrTLhTHPMu1w1gH7RNIUBSQLHHKPZgcFoG7a1a2pLt6JpPpInrgCg3dsJdJel/GnX3/jLpy8zLSYLk8FIZngqV41bgnGYvlCKGkvZfGwnk6InDNmFn81oHZYvFwFev5f6jiYWn7IMxAlGg5F7Zt3KZemXEGEbnhHwz5oVNwWbycqHJduHNAm0OxOwOeJpqNjZKwnUNT8+XydtjYU0Vu4Guu/m65oPv99NZ9vg549lzbsfuzPhvGMfKu5zjARqfg+d7TWERA/dvNDhNiEyg61lu9lQtJXLAliCrKpGQqMnERKVjbujHkU14mouxu/twu/rpKX+MI1VuUDuGY9hdyZhd8QRm345JotDRg+HgV/zU9FWA0BFWzVR9nDunLGSOYnTB3ysivy30fxeMqbfRUjU2H/PWIyWIWkM81l2ZwLOyIm01udRX76ThHHLA36OtqYijn7yh16P1Ryr50Q5pWqwEBE/G1twDIpqQjWYupM9gxFrUAzWoKiAxzQUYlOXEJkwh7K8V2msysXVXMr4nHuHbG6qruu0ezrwaF46j0/x+exIYIfreGfQ4LFfTdJf8sk9xiiKgiNiHK6moiE/l9/noa5sK6HRk3ouED0+DwuSc7gy41LW7voboTYnVW01lLVWsaV0F7sr9/OjpfcPeWwlTWX84INHMaoGVk2+bsjOYzVZZE7gMKltb0BHJzb4zF9iaWEj15jIbDQzO2EauZUH0HQNdQg76UUmzKYs7zVqS7cSHjedysL1NJTvRDu+vhGA3ZGAYjCiKkaMpiBAISxmKjZHLH6fh9Do7HNObtc1P/m711JZuJ7MGf82ZK/nfLn9HhRF6bObG0BHawXoGkHO0dW46nwsTV/Avw6/yx8/eZatpbv4wZJvBvT4iqL0XDBaTqmkiEtfhq750fweVKMVOF5apml4OhupKv4AT2cj9RU7qa/YiaIYiM+8ipjUJRfMKOxo1OJuQ9M11sy6jYXJs7GZrIP6eXe0VtBce5DYtGWERo/dMtBTWYzR3MZFAAAgAElEQVTmYUkCAcbNvIf8PU/TULmL+MwrA9JR1d3Z1HMzpaFiFwDOiAmkTl6F0RyEoqjHO0YrF9R7zGiykzbldsJiplO07znyd/+J7AX/jtFk67WdX/OzvyYPl6cDt8+N2+/Bp52sdFFQ8Pg9/OPAGwBEB0Vww8QrURWFFw+8icVgxqf7aeho6nXcz64729lWhWqwYB6hG80jQZLAMSjImURzzT68Hhcmc/CQnaeqcD1+XycxqYt7Huvyu7EazGRHj+M31/z45OO+/8/efYfHUV6LH//O9l2tVr33Xqzi3nC3semYDjYtNw6XJL8QEgIh7YYSuCTcAAECBHBCCJiOaTbGGPfeLcuSbNnqvXetts3vD9kCIcuWZEkr776f5/ED2pmdOeu1dufM+77ndPHsztc5VJlNXXvDiBfrWHtiEzaHjccX/opY36gRO49OpcPqsPHinjdYlrEUXzf6cBhtVW21AOdMAp0tIyiFrUV7KGosI9Y3csTO4xsykdK8TynNW01p3moANHpfAiNnodJ44OkTh0bnNSznCoqeS8XJddSU7CAw8pJhOeZw67JZ0Co1/V4AtbeUAWAwhY9mWCNKp9Jy94SbeG7XSrKqc6lpqyPQ6D8q55YUSpSKMxdi3dO3UHS3KorNWA5AS30+5vYaakp2UJ6/Fr1nCF7+IzdC7k4cDgfvHP2UkuaK7gQcqD79+eir98Kg0Z/7AOdQW7YbSaEiKHrstXsaKp1S2zPFbzT4h06mIOstWuvzMfknXdCxujobyd72JEq1gYDw6dRX7MM7MI248Xf12u9ibN8xUN6BqcSNv5OTB1dSlP0e8RPu7tl2uPIY/9j3NvWdjf0f4Htq2ut5/cA7vR4zqPUsy1iKVqlhXf5mPDQGfPS9v0M72yrRewa79N/19w1rEvjxxx/z29/+Fr3+2w+opKQk3n333Z6fS0tLeeSRRzh48CB6vZ6bbrqJ+++/v+fL3eFw8Nxzz/Hhhx/S2dnJxIkTeeyxxwgLG7tTlUbbmTYNHc1leAWMzJeuxdxMdck2TH6JvdYLddksfe6eQPcFyw2pl3OoMpvCptIRTQI7LJ3sKN3PorjZxI1gAgjdawIBthbt4VRDMU8v+f2oTXd1J2XNlXx0bC0AwWeZDjpWZAanIEkS+yuyRjQJVKkNJExcQf7B1/HwjsIvZBIBETNG5FyBkbNoqjlGad4naPW+eAUMf6uVC9Xa1YZR49Hv9o7mUtRa07AlxmPFzMjJGDUe/GnL8xypyiXQ6Me4wKQx8Rlk8kvA5JeAf/g0sjY/Rn35PpEEXgBZlqlpr0OlUPGXbS9T2FSKn96n50LVU+NBaEgQiRfQD9XcUUd9xX58gjJdquiURqWhaxSXbXgFjkOpNlBXse+CkkCbtYPje18EwG7toKpwIwCePiPT83Ys8/JPJjBqNjXF22htKMDTNxab3cZLe99EQmJ5xnVMCktHp9SiVWlQKVRI9K5mqpQUKCQFNoeNDpsZi82Cj96bNks7Hmo9OrUOgMsS5gG91yrKskxnayU+wYOvsnsxG/aRwNDQUDZu3HjWbXa7nXvvvZeJEyfy/PPPU11dzYoVKzCZTPzwhz8E4PXXX+eLL77grbfeIigoiKeeeop7772XTz/9dEy0JBgLuqdmSrS3lI5YElhXvgdkmYiU63o93mXr6rc4Q5R3d1GGgoaSIZWrHqjdZYew2q0siBm5Uv1nGL4zLaG8pYoH1/2Jq5IWsiD2EpealuEsde0N/PPQ++wv727C66P3wvMcF/vO5qUzEekVNqxVG/tj8k9i0uKnR/w8SpWWhEkryN72FEXH3id99m9ROLHYE0BdRwOf5q6ny26hy2Yhu+Y4kedoldDeUorBhaaCfldyQDwKScFrB1YBEOIZyB2Z1zN5BD9jB0OhUOEXOona0l3YLO2oxvDv71j2ztFP+ST3q56fr0+9nFvSrh6W7xlZlqku2kxlwTfIsoOgqFkXfMyxRKfS0mRuHrXzKRSq7irOZXuGXMW5seoIBVlvAzJR427GNziThqoj2CztI3bDb6wLjVtMfcUBSo9/Qsr0X7A2fyNN5hZ+M+enTAgZeAVRlVLVk/BB34JiZ/udsnY1Y7d19lTgdxejOh10//79FBcX88477+Dh4UFsbCwrVqxg5cqVPUngu+++y4oVK4iN7a4+9uCDDzJz5kwOHDjAlClTRjPcMUup0qHzCKBjhIrDyLKD+vL9ePrGozN8O/3IZrdhlx1nbbAJ3b9oCb4x7C0/zM1pV41YkrSjZB8hxsARHwUEmBE5iZzafG4YdwWbCnay7uRm/rH/bcpaqrhrwo0jfn5X5nA4eGbna5xsKGJq+HgWxl5CnE/UmE+uY30i2V+RhSzLYz7WgVKpDcRkLOPkwZVUFW4iNH6x02Jp7Wrjic0vUN5ahZ/eB41KjZ/em1mRZ//8t1k76eqowy908ihHOjo0SjVLU5ZwrPo4bdYOyluqWHngPTLOUkXXWfzCplBTsp3c3X8jedp9qLUjt0zBVW0r2tvz/48v/BVJFzDi910dLWWUHv+ctsYCtIYAosfd5FLTpgE8tR5k1xxnb9lhbA47ktS9TkwhKVBIEgpJSWpgAp/nfc0Xx79hSlgmKQHxZIak4m8Y2qwl/9Ap1JbsoKHy0KCn0Vu7Wig4ugqQiUy5Hv+w7s+2M/91V0qVjvCEKynO+YATpzbw3tHPSfKPIzN45IsXdbaeKQojksALUlNTw6xZ3XeZ0tLSuP/++0lO7h6tysvLIyoqCpPJ1LN/WloaZWVltLW1Icsy5eXlpKV9m/GbTCaioqLIzc3tkwS2tLTQ0tLS67GqqqrhfkljkodXBM11x0fkQrS6aAsWcyOh8Ut6PW4+3WDzXL265kZP57UDqyhoLBmRJM0hO8ivL2Ru9PRRuQDXqbT8dFr33PzbMq7lprSreHTTs2wq3Mmd429wmSRgtMmyzFtZqznZUMQPJtzM5YnznR3SgCX4xbCpcCdHqnIZH3LxV9Y7w+SXhJd/CjWlOwiOme+00cDnd/+L8tYqfjL1TubFnP+OeMfp9YAeLnZh+123pl8D6d3/f7DiKE9te4m1JzayNGXJuZ84SgyeoYQlXE55/pdkbXmU6LRb8Qud5OywLjoeGgMvX/1kvzdaz8fhsFFxcj2dreVIkhK7vauniFxAxEwikq656NtBnI1WqcFqt/J/O/4xoP23Fu9ha/EeAgy+/GXJ7/DQDH4kr7uKcxh15fsGlQTKDjsnD/4TgNQZv3SrdgQD4Rc2mbryPew+uQmrw8bdE24a0SJsZ3T0JIHBI36usWRASeDDDz/M6tWr+92+ZMkSnn/+eaZMmcJnn31GVFQUra2tvPrqq9x55518/vnnBAUF0dbWhqdn7z4gZxLCM0ngdx87w9PTk7a2vg27//3vf/Piiy8O5CW4HIMpgvqKA1jNTWiGsUdea8NJyvPXYjCF4xOU3mubxdZdmfBsawLPuCRyMm8c/oBNBTtHJAmsbK3BbOsi1mfk1mOdi0qhZEbERI7XnaK1qw2TbnT62riSQ5XZ/HXHq1jsVjKDU3rm518sZkRM5KOctTy59QXumbycRXGuMbVKkiQCo2bTfCCXshNriExZOuoxHKnK4UhVDktTlgwoAYTuJvEABi/XnA76fRND00nyj2Nr0Z4xkwQCBMcsQKP3ozTvE4pzPsLoE9ur6qjQP7vDTqO5maUpi4ecAJo76jix7xWsXc3oPIK623ZIEj5BmYTGL7loWgcMxZkb09ckX8q86Bk4ZAcyMg5ZprK1hud2vY63zsQ1yYuZGz2NDmsnByqO8sahD3hiyws8uuCXqIdw08s3OJPy/LWDmgZdW7qLjtZygmMWiATwLCRJQUjspZRv/zt6pWbUKoJ3tlag0fuiVA+96NLFaEBJ4B/+8AceeuihfrdrNN2/gBER375Z3t7ePPTQQ2zYsIHNmzdzyy23YDQa+yRzZ0byjEZjTxLY2traa5/W1laMxr7TS+666y6uu673mrWqqiqWL18+kJd1UTtTHKa9uWTYkkDZYaf0+GcolBoSJ93Tp7z8QEYCDRo908InsKNkH3dOuHHYpysdrc4DIME/ZliPOxgRp9cm5dWdGlKPJlfUZmnns7yvqWmro8ncQpfdcnoqjuL0RU33F3JbVzuFTaVISCyJn8ttGddedKOpHhoDf5x3P3/Z/grvHv2UudHThnQBMRZ5+sbjFZBKbekO/MOmjHrvwA+z1+Bn8OG6lIH332qsOoLRO8alCl2cz6TQdFZlfUJrVxueY2jqpW9wJhqdN8f3/p0T+14iedrPUGtN53+im2s2d7d/8B3id7ndZuHUoX9htbQSnXYLviGTLrrP1Qtx47grmRKeSUpAQp9tMT4RzIzsPSrtqTVyReIC6joa+eL4Bv5vxz94ePZPB/135uHdfaO7tmw3IbELB/ScproctAZ/QuOHv8fgxcpmt2GT7dgcNmwOO3aPIIrtEiEqCYetC8UoJGYdLWUYPN2vAOWAkkAPDw88PIa22FuSpJ7kLjk5meLiYlpbW3tGBI8dO0Z4eHhPkhcWFkZ2djbp6d2jUK2trZSUlJCS0rdinclk6jNq6C70nqFICjXVxVuHrZpRXcV+OlsriUi69qx3Q3oaNvdTGOaM+TEz2F68l33lh7mkn3U8Q7WlcDfR3uGEm5x3By0lIAGT1siaExvx1pkwaY1juqLlSGvsbObhr/+Xxs5mgo0B+Oi9MWmNOGQHdoeDFnMrKoUSSVKgVWmYHTWV2zOv71Oe+WIS7BnI7ZnX8dS2l8itPUlG8NirqDkUkiQRlXoTR7c9QXXxVmLSbxu1c3fZLJyoL+T61MvRf2dR/zmf09GAub2a8KRrRji6sSX19MXue0c/Z8Xk0XuPBsLoHUXchLs5degNsrY8jkrjSXjCFfiFueaazeHQ0NkEgJ9hYEmg3WamtaEAu7WDlvoTNFQdBmSix93ismtjz8Wg0Z81ATyfOzKvx2K3sP7kVp7a9nd+OPHWQbVhMXpHY/SOoeLkOszt1USNuxmFQkV7cykqjRGV2kBL/QlslrbT03AlOprL8AlKd6sk/ftau9o4XleAQpLYWXKArcV7zrrfJVotxcfe79MuY7jZrJ10ddbjFzZ1RM8zFg3rmsANGzaQnp5OYGAgbW1tvPbaazQ0NDBnTnc/msmTJxMZGcnTTz/Nww8/THV1Na+//jq33fbtl9itt97KypUrmT59OkFBQTz99NNER0czaZJYX/BdCoWKwIiZVBdvwdLZeMGjgRZzE2UnvkDvGUJAP/Pbz5RgPt90lXGBiQQYfNlUsGtYk8DS5gpONRZz94Sbhu2YQ6FSKJkZOZl1+Zv5/TdPo1VpefnqJ85Zwt5VORwOntu1kqbOFu6fsaLPHVdXlujXXbyquKncZZJAALXWiF/oZOrKdhMYOatn1sFIK22uQEYe1PSf1saTQHe7AneS4BfDpNB0NhftYlnG0gvqGzcSvANSSZryY1rqT1BbtoeiY+9htbQRFD3XrS9++3OmB9rZ+tDKsowsdzfGVihUWLtaOL73Jbo663v2MfklEhAxA+/AgVdQFLpvev3XhFtwOBxsKNjOz9f+kUcXPECif+wAn68gbsLdlOR8TEPlIZpr89DovXuKjEiSsue9+y6jj/NmMjlbU2cz/7Pxrz19gQHCPIOZFzMDtVKFUlKikBTo1VoiOiqoK91BS33+WT/jZdlBfcUBTH6JF9QeqLO1AmDUZ76MBcOaBG7bto1HHnmkZ/pmWloab7zxBqGh3dPnlEolr7zyCo888ggzZ85Er9dz880391QGBVixYgWtra0sW7aMzs5OJk2axMsvvyzaQ5yFX+gkqou30NpYgJ9+6BffssNO/sGVyA4bMenL+v2SNg9wJFAhKZgbM4MPj63hrSMfc3vm9UOO7bu2FO1GKSn6rRI4mi5LmMe6/M1Ad3K8vXjfRbe27UKYrWZ+t+EvlLVWIcsyt6Zf41YJIIBR64FR40FlW42zQxl2YQmXU19xgLryPaOWBBY3dRd4ifQe+BdxW2MhKrUHOo+gkQprTJIkiSsTF3Kg4ihvHv6QyxPn88Xxb1ArVPxocv+f4aPJ6BOD0ScGv7Cp5B94lfL8NXS0lBKTcfuYiM/Zcmvzaelqo6atnv8c+QilQkmwsfe6PZulnZxdz2Lt6m5/8N2kIjzxKrwD01GqdW41FXq4KRQK7pmynLkx03lyy4s8sfUF/nzpbwY8u6e7svJydKcCaK7NRVKokBQqZIcNvWcoAeHTMfknIcsOQEZCQq3rm+y7i49yvqSqrZZb068hIygFjVJNuCnkrNf4NmsHzTXZ5B94FZXGiMNuRaFUo1B0L7+wmJs40ykwOGYBYQmXDymmM8XFxHTQC/Too4/y6KOPnnOfiIgIVq5c2e92hULBAw88wAMPPDCcobkkrcEfkOjqqD/vvudSdmIN5rYqIpKvPWdlpHZrOwAeA/jCmX86Cfws72uWZSy94OpOdoedrUV7mBCaPiaKsYR6BvH+LS8D8Ov1T/L1qW0siXefu9wnG4opbem+23l5wnyWJo+dAhWjKcQYQFWr6yWBKrUB3+AMGioPE554Dcrz3PgZDsXN5ehUWgI9/Ab8nLamYjy8o93m9+67Yn27i2NtLNzJxsKdPY9H+4SzOH6us8LqQ6v3YdzMX1GY/S6NVYeRj7xJeNI1bls0pqatjie3vkhFa3XPY0aNB7dnXtdnGnRZ/lqsXc34h09Do/PBbutCqdJh9I7C09f9GoqPpCT/OH4x80c8ufUFntz6Is9d8ciAr1skSSI0fkmfiupCbxabhe3Fe5kVOYXrU8+fsKnUBpKm3Ett2V5s1jaUSh0Ou4XGmqPYrR1A9zp2h91KVeFGdB6BQ6pK3NJwEo3Oxy1b24xqn0BheCmUarQG/567GENh6WykpmQ73oFpBEaeu8phW1f3L51Re/5pjwEefizPuI63s1aTV3uS1MDEIccIcKQqlyZzC/Oip1/QcUbCpXFzeHX/2xQ2lhA7Cr0Lx4Ka9u4bD4Nt4upqgj0DyanNd3YYI8I/bBr1FQeoPPUV4UlXj+i5ZFnmSFUOcb5RA77wslna6eqoddveWga1npvTrqaspZKmzmYWx8/ls7z1vJf9BfNiZg5LUS6b3UZBYwlmWxcxPhFDLkIjKZTEpN+G3dpJU002TTXZ+IVOIjrt1gEfw26zIDusdLZV095SQkPlYRx2C5Ep12Pyix9SXKOtrqOBn3/5CHaHHaVCyeMLfoVKoSLMFNSnuFRbUxH15XvxD59OVOoNTorYvYwPSeXeKXfwyr7/8EH2Gm5JH9nPvQvVZG7BpDH2jKJ1WDtBptf08NzafP648RkyglLw0BgI9PBjbsx0NAo1vgYfKlqq6LJbWJe/mW3Fe/nBhJuZEJqGzWFDp9TSYe3k7azVdFrNLMtY2n0OJI7VHKfd2sll8fMIMvqjV+uoaa+n09pJhFcoHdbOXktksqvzeGLri9gddhbEDrylhtbgT3jiFb0ei0y5Dofdgt1uQa014bBbyd39LMXHPsBu7cQrIBmFUktHSxnl+WtxOGwkTr73rFNGLeYmWupPEBQ1dm6cjSaRBF7kPH3jaKg6jOywD6n/T2XhRoABFVZotXSPBBoHOPVkbvQ03s/+nL/t/idPLHwIf4+hNWWVZZk1J77BU+PBxDGYcJyJKa/ulNskgZ3WTqB7bZI7C/EMYlvxXrpslvNOk77YeHhHY/JLorZ8L6HxS/pUCx5Opc0VVLbWcHXSpQN+TktD93pAo3f0CEU19t04rvfFkUnrwWOb/8b6k1u4KmnRBR//49wv+fDYWgBivCP4/bz7hp4ISgoSJq2gpT6fmpId1FccwDdkIia/3jcIW+pP0tp4EmtXK8h2FCodnS0VtDUV9j7e6Sl3Jw/9Ey//ZHyCM/ANHtvVmveXZ2F32Lk6aRHXpVzW7w1VWXZQfOwDlGoDYaKK5KiaGz2Nbwq281HOWg5XHuNHk28b0e/1M4UTJUnC7rCzqXAnc6KmoTnL94nD4aC+s5Ev8zdT2lzOkapcvHUmkv3jMemMrD+5FYCZEZMwajyw2K1sLtoFQFZ1bs9xPs1b3288/zr0Pv869P5Zt/3Pxr/2eWxjwQ7UChVR3uGcbCjqtW1q2Hg0SjUn6gt6bhzfnHYV4y5wUEBSKFEq9D0FDJUqDQmTfkT+gdcpPf4ppcc/7fOcilPriR7Xt55EVcFGJCQCIsbeAMNoEEngRc7oHU1d2W7MHXXojYNbF9PV0UBd2W58Qyaec2pOdnUebx7+iKKmMvwMPqiUA/tn46334gcTb+bV/atYnbuOH01eNqj4ADosnazK+oSj1XncnHbVgM89mnwN3vjovTjZUOzsUEZNp80MgF41sCqOrir+9MXBkaocl2sXIkkSwTHzObH/FRoqD+EfPm3EzlXS3L0wP3EQNxVa6k+gVOl7yrQLMC4wiZSABD46tpZL4+Zc8I2JgxXZ+Oi8mBSazoaC7Ty19e88vvDBC1qjb/JLwMMrgmM7/o9Th/9NbMYdmPyTgO61OQVZ/+mZ6gWgVOlxOLp71Jr8k/ENHo/OGITOEIC5vZpTh9+kqeYoTTVHaaw6TFD0fGSHDaVKi1rrjd1uprZkBw1Vh/ENHk9o/BKUo/S5dbjyGDtLDvDjqXfgkB2szl1HkIc/d4w/+8iew27Fammlrnwv5vYaotNvG3D/OWF4KBVK/jD3Pv68/WWO1Zzg5X1v8d+Tl6NVaQg3hQzr1PP8+kJ+/83TyLLM9amXE24K5tX9q9hTdpgHZ92LRqmmpq2OveWHWXNiI/UdjT3PjfTqXr/WZG7hYOVRLHZrz7bCxlJaLe1oVRrCTMHE+ERS2lzBJZGTaTa3sqlwJ3q1DqWkINIrjMXx3Z8VSklJeUsV6/I3E+UTTm17PWGewVyROB8Z2Fq0hy67hYkhaaiVKiQUFDWVsqNkP02dzUR5h2PUGDhWcwKAspZK7A47aqWaqWHjuXviTfgbhjYYcD5avS8p0++jpf4EBUf+A4Ba5030uJupKd5GfflevPyTMHrH9kz77OpspK58L35hU9DqRyausW7sXVELg2Lw7C6609FaPugksK58DyCdczFtYWMpT2x5ARlYFDuL6RETB3WORXGzyas7xfaSfdwx/oZBNcItaSrnj5ueod3SQWZw6qB6h422eN9oTn3vLpgr67Ca0So1KIcw+uxK0oOS8daZ2Fy02+WSQACjTyw6j0CKcz+ms70GpUqHd0AKBlP4sJ6norUKSZIIGUSrlfamIoze0UgXuN7YlUiSxK3pV/PHjc/w/O5/8rNpd6MbYLuN7ytvqaKgsYRrkxezPPM6gj0DeOvIan7/zdPMiZ7G4rg5Q04GlSodMem3cWL/Pzh5aCUqjSd6j0BaG08B3UVPLOYmfIIzzznS6+EVSfqc39FYnUVj1eHTU02P9bt/Tcl2LOYmYjPvHJV1pE9ufRGAWzOuobCxlMbOZu6dcnuf/RqrjtBUe4ymmhwcp/vxGn1ix/zIpqvSqXX8cf4v2HBqG6/uX8VvN/wZgDifKH5xyY8GtW65Pw7ZwV93vNozEvhxzpc9245U5fD09pdp6+qgsKn0dA9Jb24adyV6tY4EvxiS/OMoa6kkqyqXJfFzkWWZXaUHmRE5CdV5vpfvmnBjv9sS/WOZHzvzrNtuy7i2z2PJAXFjpiieUqXDJyiDSYuf7vW4Ru9D+97S7uRQUhAQPg2foAzqKw8BDLjHoysSSeBFTucRiKRQ0dlSASEDT9AcDht15XvxCkhBc45KVR/nfIlDlnn28v8h1NR/0ZhzWRh7CVuL9vBxzpfcln7+5uANnU0Y1Hr+vufftFs6+MnUO5kdNXVMJxzxvtHsKz9Cm6XdLVpFdFg7MYxCA9exTqlQMid6GmuOf0OzuQUvnWv1LZUkiZiM5RRlv0dNcfdUo8pT6/EKGEdc5h1DmoJ+NiVNFQR5+PdZF9Ufm6Udc3vNkIoAuLpk/3gWxc1mw6lt/OjTX3NV0iKuTVnc5wZcm6UdBYp+20usyvoECYmFcd1rxa9OupSGzma2Fu3hnwff458H30OjVKNSqPDUGnlg5j1E+wz85oCnbxxps35N+ckvaaw6QqulFaNPHBFJVw+qVLskSfgGZ+IbnEl7Sxm2rlYUSjXNtXk0VB06fWGYSUjsQspPfkl10RYOfv0QYQlXovP4tidcdysGB8gOZNmOLDuwmptprMnGbjNj9I4mJHYRWoP/gBNICQkZmfz6Qt449AEmrZE531vX3lB5iMKjq5AUKvTGYKxdLdisHcRmLBc3OJxsYewsEv1i+TBnLfvLsyhqLuPX65/k/5b8fsA9Hc/GZrdxuOoYDZ1N3JZ+LRFeIfxl+ysAqBQqZGSOVHVP3wz08OP/TbubGJ/IPiP74aaQXj2TZ0e7X5+7gdAZ/Emd8Uua6/KoL99Hbeluaku7p8kGRF5yzmtgVyeSwIucpFCiNwbTcbrPyUBVF23GZmkjIHxGv/u0mFvZV36E2VFTh5wAQvdFSaxPJJ/kfoWE1OtuUltXOxsKthPo4cfEkDTeOPwhGwt29GxfMek25sX0H+NYcaZSX3FT+QXPd78YdFrNA27o7ermRk/ns7yv2V68jyuTXO+OosEzlNQZv8BqaaO6aAvtzSU01x6jsmDDsFTD67SaOVx1jLkDLPokyzLN9ceB7tESoTdJkrhn8jImhIzj7SOr+ShnLY3m3iNQDoeD3339F+o6GpgfO7O7GI/cPTrhQKbJ3ML+8iNck3xpT9sCSZK4e8JN3DX+RnaU7KOspRKbw05TZwtbi/fwx41/5dnL/4ivYeAXVFqDHzHpy/HyT8bkl3zB1fk8vjNC7ekbT3jSVb22h8ZfRkdLBa0N+ZTnrxnQMSWFCq3el4bKgzRUHiQgfAaRqedveyTLMgpJwi7L/JTmrlsAACAASURBVHXHqwDcM3l5zyiNzdpBUfb7NNceQ6PzIWXG/aLVwxgjSRKR3mH8cuaPgO4Ruie2vMA3BTu4Oe2q8zy7L4fs4E+bnye75njPY0sS5mJQ63n/lpfZWbKfON8o2iwdfHNqO146E9elXjYsRZ7cnVrriX/YFPzDpmCzdtBQeRDZYcfPTQuLnSGSQBdg8AylsSYbWZYHdIeyqSaHipNfYfJLxCsgud/9TjYU4ZAd/U4NGChJknhk/i94YuuLrM5dx/zYmfjovDjVUMSft79Mp7V7fZlCUuCQHT3PSw9KZk7UxXFnK+D0PPeGjiYnRzI6Oq2dIgk8LcIrlDjfKDYW7OCKxAUu265ArTESnnglACf2v0pNyQ4Co2b3e+Ha1dEAOFBrvVCc4yJmT9khLHYrc6L7X3NoMTfT0VpO+Yk1mNtrARmN3hcPr8gLeUkubUpYJhOCx/HMztfYXryXy+LnsbN0Pw0dTZi0xp7+ljuK93U/QZJQSIruP0iMD07lpnF9L3QlSWLW9z6Xp0VM4Jkdr/LBsTX895Tlg4pTkiT8QicP7UUOkkKhImHSj3DYzFjMTad7t/UEgiQpkSRFzx8kBSq1oXu2TWs5ZflrqS3bRVNtDia/eHyCxyMh9TxfZwhAqTagUGqo72jELjtQSAr8DT7MiZ7O/Kgp1JXvo7Z0Fx0tpQD4hkwgIvk6VGJmxZiXGZxKZnAqWwp3DSkJrOto7JUA3jjuil4zamZGdv8eBAFxblJkzhlUasN5q+G7C5EEugC9Zyh15XuxdjUPaFi7umgzSpWeyPOUnT7VUIyERKzPhV9o6dQ6lmVcyx83PsN9a/6n17bZUVNpt3RgUOvJCE65KEb+vs/7dOnhRnOzkyMZHZ1WMwaRBPa4LH4ef9/7bw5UZDE5LNPZ4Yy44JgF5B/4B8f3vkTK9J/3SvI6WiuoOPkVzbU5AGh0PiRMWoHO4+zr/bYW7SHIGECiX+9RPXN7LVWFm7DbOk+v85JBUuAXOhmVxgPvwHFiutx5qJQqFsTOZH9FFg+tf6LXtvSgJH43574LKvJyxpSwTGZHT2NjwQ7mx8wg0X/sjtBKkoRSrUc/yKTLYAonfsJ/UVO8jcbqo9RXHKC+4sBZ99XovClVdo9q/jh2MnGmQOy2TrK2/gmHzYxCocY/bCo+QRk9RXGEi8OEkHEcqcqhvKWKsEHOkGrr6q6wfn3q5dyafv6K7IIw0kQS6AK+LQ5Tcd4kUJYdtDeXDKga0v6KLGJ8IoZtxCclIIEnFj3Evw6+T7ulgw5rJ1ckLuC61LFb8GWg9Grd6T45dc4OZVR02Mwut/7tQsyKmsKHx9bwz4PvkxqQ2O86K1dh8osnInkppXmfUJL3Sa/S2xUn19Ncm4NG54N34Dhqy3ZTkPUWiZPv7TNq2NDRxLGaE9ww7oqeEVRLZyOlxz+jqSYbAElS4uWfTGDULPTGYNRa8e9uMKK8v50ied/0H+ClM2F3OEgNTBiWBPCMq5MWsblwFysPvMufl/x22I47nBo6m3h047N0WDtRKpRcnjCfaeHjMaj1mHSeffa32q38ev3/kugfy71TbkehUBEcM5/gmPl0tlVjs3ZQb27BbLNgt3fRWLYbS0c9ng6ZJnMDALaGE9Q0nUCp1KLV+xEYeQk+QemjVqFUGF6TQzP496EP2VK0m2UZSwf13LbTbbYyg1NGIjRBGDSRBLoAvWcIINHZUo53QGq/+zkcNoqy30WW7eftrVXSVE5hYyl3T+jbV+VCJPjF8OSlvx7WY44FkiQRYQqlrLnS2aGMCrPVPKhKr65OqVBy14Qb+cv2V3hu1+tMDssg0MOf8SHjnB3aiAmMvIT25hLqy/cRFDnr9OdQd5l/35CJRKfdiiRJGExhFGW/R9bmx/AJzkTnEYhPUAY6jwCyqnORkZl2urJqfeVBirLfA9mBwRSOV0AKIbGLxIjfBfA3+KJWqpGgzzTO4RThFcoNqVfwUc5a3jj0AbdnXDfmWvpsKdxNZVsNC2Jmcqw2n7ezVvN21mqg++Jeo1SjVqrRKjXIyJS1VFHWUklZSyXeOk8Maj1apZaUgHjssoOPc75mT9mhPudRKdqwOWwATJv3e7coFuYuAo3+xPpGcryuYNDPPZMEemoubO2rIAyXsfUJLQyJUqVDa/Cjuf44IXF9my07HDYqTq6jumgrIOPln4JvyIRzHnN/RRbw7Rx14fwivELZW3ZowGszL2Y2h33MXeA52+SwTOZGT2dL0W4OV3VPhYzzieK/Jt1CwiD6311MwhIup7k2h5xdz6DWmlAotVi7mjGYwnp+B/xCJ6PWelN5aj3Ntbk0VB6k4uQ6tIYAdjc1YFBpCdLoMXfUUZT9Hhqtiahxt2Dyi3fyq3MNkiTx1yW/H3KriMG4OnkRByuOsvbERpBl7p5484ifczCyqnOJ8Yng3ql3YLFbya4+Tl1HPZsKdlHcVIZKqcJqt9Fl66L19AU7gJfOxMc56856zAS/GK5KWoiEhM1hx2wzs7FgZ0/jbFFF2fUk+8ez5sQ32Oy2QX0Ptp6eDmrUiAJAwtggruJchN4zlKbqLLo66tAaepe9zj/wGm2N3XetIpKvHdCC2GM1J4j0CsNbTPkbsAivEL4p2E5zV6vL/73ZZDsqaey27HCWn067i5mRk9hbdgRJkthwahsf5XzJw7N/4uzQRoRG501s5p001RzF2tV6egqnhJd/74JTJr/4nqSuramY6qLNyLJMUWURIUoFx7Y/BYBCqSV52s/ElM9hFjyI/osXwqDW8+clv+W5na+zNn8TNoedG9OuHDOfhxUt1WQGd8+W0SjVTAxNA2Bx/Nw++5Y0lfOrr/6Eh1rPa9f+GVmW2Vq0hy/zNxHuFYKv3pvp4ROIPUsBj0Vxs9levJeqtrruyquCS0kNTGDNiW/47PjXXJ/af5/l7ztzY0GMDAtjhUgCXURo3KU0VWfRXJtHYNS3SV5d2S7aGgsIip5PaNyl56zSd4bNbuN43SkWxorqSYMR4dW9NrO0uWLMXPSMFLvDft6GtO5qQkgaE0K6Ly4bO5uo62h0ckQjy+SXgMkvAYC2xkIkpbrfIjAARu8ojOPvoqKlioYTe1gYMw1vlYPWxgLCE68UCaAL+O8pt9NkbmH9qa1sKtrF1UmLuCpxIUbt4C5+K1tr6LSaCTYGDGmdrcVuRaNU09bVTkFjCY3mZkJNQQN6bqgpGINazx3juxtrS5LE3JjpzI0ZWCuTkZx6KzjXpNB0Yn0i+SjnS+ZETcPfo//6CrIs02XrQiEpaOhsQq/SiVk0wpgh/iW6CL0xGI3el9bGU72SwPqKgyiUGkLjF6NQDOztPtlQjMVudYt+d8Ppu0lgelD/rTdcgc1hQymSwPPy1nlxsr7I2WGMGqPPwKe9nqgvBGBm4qWDrrInjG16tY5HFvySnJoTvLb/HT7O+ZJPc7/iwVn3MjE0fUDHWJ2zjnePfoaMjFap4Zb0q7kqadGAY8ipOcEjm57lksjJ7Cs/gsVuBb79nD4flULJG9c/M+DzCe5DISm4Z/IyHv76KZ7Z+RqPL/zVWb8Pa9rqeHr7KxQ3l/c8Jq6rhLFEJIEuxOSXSF3ZbhqrsvAJzqCtqZj25mKCoucNOAEE2F9xBKVCSWpAwghG63q8tJ54ajwodYPiMHaHXSSBA+CtM9HS1Sb+vs4iu/o4Hmo9IcbRmaoojL7UwESeuPQh1p/cyqqsT3hq20vMi5nB3eNvOuvInsPh4JX9b7Gn9BCdNjMBBl+mR0zk8+MbePPwR2w4tZ02SzstXW3MiZrGnRNuxHSWBvNmq5nX9r8DwI6S/UR4haJX6eiyW0gPFC0ZhAsX6xvFHZk38J8jH7HywLvc873+mBabhbeyVvckgGqFiiBjAPdOud0Z4QrCWYkk0IWExl1KS/0Jyk58gck/icKjq1CqdITELhzUcbKrj5PsHzfoqTvuTpIkIr3DOHW6IICrkmUZu+xANYgbC+7KR29CRqalqw0fvZezwxkzGjqa2FV2kLlR04a1TYEw9hjUepamLCHeN5rHNj/H5sJdKJC4d+odffbdX5HF5sJdjD/dlHtx/BzUSjUTQtJYeeBdQkxBtJhbaelqY2vxHrYW7+GeyctYFDe75xhtXe38Yt1jNJtb+H/T7mZO9LTRfLmCG7k6eRGFTaVsKNjOlPDMnmUAdoed/932d47VnGBW1FTum/4DJ0cqCGcnruJciFprIizhCgqz3uLwxt8DEJtx+6D6EZltXRQ1lbE0ZfFIhenSxgeP4+2s1TR1NuPtohf9docdQKwJHABvXfe/gSZzi0gCv+Pz4xuw2q1ckbTA2aEIoyQtKIn/mXc/H+d8yb7yI/y3fHuvKsqnGor5266V+Bl8+PXsn/QaOU8LSuLZK/7Y83On1cxXJ7ewKusTXt2/CrOti9SARE42FPJl/maRAAqj5sdTbmd78V7+d+vf8dV742fwod3SQUVrNVPDxrNi4q3ODlEQ+iWSQBfjE5hGe+QsrJY2PH3j8QnOHNTzCxqKccgOEv3iRihC15bgFw1AYVMpE1z0ot8mdyeBSlEd9LzOFAhq7GwmxifCydGMHbm1+YwLTCTcFOLsUIRRlBaUREVrNdk1xylvrep5/3Nr83ly699xIHPf9B+cd+q0Xq1jacoS5sfM4JGNz/Lm4Y96tnloDKyYdJtIAIVRoVaquS7lMlbnriPRP5Z2SwdGjYE50dO4LuUyl28XJVzcRBLoYiSFkojka4f8/MNVOSglBYn+rtnXbKSFeHZXnqtuq3NyJCPnTBNkMRJ4fmdGg5/a9ncmhqTx0Kwfu/30x06rmcKmUq5PGXhpdcF1TAxNgwOQVZVLuCmE0uYKHt/8PAa1jj8t/BVR3uEDPpaXzsTv591HdvVxDBo9Hmo9cb7RaFWaEXwFgtDbLWlXc1XSQjzPsj5VEMYykQQKvZysLyLGJ1L0sRkib50JrUpLmQsXhzkzHVQUOjm/77YKOViZzUc5a7kp7SonRuR8ubX5yLJMcoCYbeCO/PQ+mLRGsqrzOFKVw6HKY6gVKh5b8AChQ6gS62fwGXDbBkEYCQqFQiSAwkXJvW9JC31UtFYPuI+S0JckSYwLSCC75rizQxkxdocDECOBA6FRqpkclkmYKZg4nyg2FGzvSaLd1bbivXhoDKL6sJuSJAkZOFhxlEOVxwD406KHhpQACoIgCEMnRgLdnMPhYP2prVjtNubFTKehs4lQT5EEXoh4vxgOVmZjtprRqQdelOdi8e10UPHxMRAPzboXWZbZW36Yv+54lT1lh5gZOdnZYTlFp9XMvvIjzI2ejlqpdnY4gpOEeQaR19XGb+f8jPEhqc4ORxAEwS2Jqzg3tzZ/E28e/hCA/xzpXlwf7S0KWFyIM0l0TXs9kd5hTo5m+InCMIMnSRKTQjMIMPjydtYnTI+YiEJyv4kYHxxbg8VuZW60mL7nzh6cdS/t1k6CjQHODkUQBMFtud9ViNDDYreyOncdKQEJLMtYSrJ/HHOjp5MRlOzs0C5qQUZ/AKrbXbM4jFgTODQqhZLbMpZS215PdvXFN124w9LJvvIj1LU3DPq5doedVVmf8MXxDUwOyyTRP3YEIhQuFp5ao0gABUEQnEyMBLqxNce/obWrjRvHXUF6UDJLU5Y4OySXEORxOgl00QqhNtEncMimho/HQ61na/EeMoJTnB3OoPzz4HtsLd6DUqEkMyiFyWEZzI+ZiUJSYHfYscl2JCTMNjPN5laOVufhqTVS017H+9lfABDnE8VPztIkXBAEQRCE0SWSQDf2TcF2YrwjSAtMcnYoLsVDY8Cg1lPjokmgaBY/dBqlmrSgZE7UFTg7lEHJqz3F1uI9hHgGEuThz9HqPA5WZvPq/lUDPsbksEwevOS/Rd8sQRAEQRgDRBLopg5WZFPTXs8PJiwUF2XDTJIkgjz8XXY6qE1MB70gIZ6B7C8/gs1hvygSaZvDzt92rcSo8eB/Fz2MQaPHYrOws/QAte31QPe/BYWk4Kv8LaQEJjA1LBMAhaRAp9LiZ/AhTFR/FARBEIQxQySBbmrtiY0ATIuY4ORIXFOg0d9lewWK6qAXJtYnErvsIL++gJSLoE3Cu0c/pb6zkZvTrsag0QOgUWmYFzOjz75iSrkgCIIgXBxEYRg3I8syq7I+Ias6l5vGXYmv3tvZIbmkIKM/Ne11OGSHs0MZdnZRHfSCpAclo5AUHKnKcXYo55VVlctneV/jrTNxfcplzg5HEARBEIRhIpJAF9dmaefez37Dy3v/Q01bHV+f2sYnuV+R6BfLkoR5zg7PZQV6+GN12GjqbHF2KMNOrAm8MB4aAwm+0WRV5Tk7lPPacGo7GqWaZy//IwqF+LoQBEEQBFch5nO5uA+PraWhs4lNhTvZVLgTgGjvcB5b8IC4qBtB37aJqMXX4FqjraI66IVLDUzk07z1mK1mdGqds8Pp16mGIiaGpuOhMTg7FEEQBEEQhpHIAlzYiboC1p7YyJSwTO6fsYJ432givcJYnnmdSABHmCu3iRCFYS5cSkA8DtlBfkORs0PpV3b1cWo7Goj3jXZ2KIIgCIIgDLNhHQlcsWIFBw4c6PlZlmU6Ozv5zW9+w9133w3AggULqK2tRaX69tTPPPMM8+fP7/n5jTfe4I033qCxsZGUlBQeeeQRkpNFA/PBcMgO3s5ajYdaz4+n3oFR48HMyEnODstt+Bt8kSTJJZPAb6eDiokEQxXvFw3AqYZi0oNG97Ot3dLBO1mfclnCPMK9Qnptq26r5V8H38cu28mrK8BTa+TSuNmjGp8gCIIgCCNvWK/iXn/99V4/f/PNN/z85z/niiuu6PX4o48+yvXXX3/WY6xZs4aXXnqJV199lZSUFF577TVWrFjBunXrMBqNwxmuSztcmUNu7UluTrsao8bD2eG4HZVSRaCHP+UtVc4OZdidqQ4qRgKHzqjxINgYQF7dqVE/97r8zaw/tZXNRbt4ePZPGReYiCRJWO1Wntr6EuWtVcT5RhFhCmFpyhL0Y3i6qiAIgiAIQzOit/JXrVrFokWLCAwMHPBz3n33XW666SbGjx8PwE9+8hPeffddNmzYwNKlS0cqVJfR0NHEh8fWcKDiKF46E0uTFzs7JLcV6xNJbm0+dofdpRKmM9VBVaI66AVJDUxkU+FOmswteOtMo3LOtq52VueuA8Bit/LY5ueI9YkkyBhAUVMpla01/HTqXcyNmT4q8QiCIAiC4BwjtjCspKSEHTt2sGzZsj7bnn76aaZOncpVV13Fa6+9htVq7dmWl5dHWlratwEqFKSmppKbm9vnOC0tLZSVlfX6U1XleiMvg/FZ3no2FGxHrVRx1/gbUCnFlD1nuSRyMk3mFtac7snoKkRhmOGxKHYWsizzxfFvRvQ8sizz089/x83v/Zj/+uRXWOxW/rL4t1ybvJhwUwgWu5WSpnJ0Si23pF0tEkBBEARBcAMDyhAefvhhVq9e3e/2JUuW8Pzzz/d67J133iE+Pp6pU6f2evypp54iNTUVnU5HVlYWDz74IE1NTTz44IMAtLW1YTL1vivu6elJW1tbn/P++9//5sUXXxzIS3ALVruVrcV7mR4xkV/O/JGzw3F7E0PTUUgKPsn9iisSF7hM0iQKwwyPeL9oJoSM47O89ZS3VHJdymUk+scO+3m2FO2mtqOh5+fLE+YT7RNBtE8EyzOvG/bzCYIgCIIw9g0oCfzDH/7AQw891O92jUbT6+euri4+/vhj7rvvvj77fjcpnDhxIvfddx9PP/10TxJoNBppbW3t9ZzW1lb8/f37HOuuu+7iuut6X8RUVVWxfPny878oF7SteB9tlnYWxFzi7FAEukfKfjL1Tl7c8wZ3fnQ/f1r4ILG+kc4O64LZRRI4bO4afyMO2UF29XEOVBzl8YW/Isk/bkjHqmqrZeWBd5kRMYmp4ZnUtTcQ6RXGN6e2E+Thz/NXPobNYUOtVA/zqxAEQRAE4WIzoCTQw8MDD4+BFxdZs2YNFouFa6+99rz7fr9VQXJyMtnZ2Vx22WUAOBwOcnJy+hSXATCZTH1GDd1Vfn0hbxx6n2BjABmjXG1Q6N+syCk0djbzdtZqPj/+NT+f8UNnh3TBzhSGEdVBL1yoKZjfzb2PsuZKfr3+SZ7btZInFj2Er35wvSWLGkv53TdPY7VbOVKVwyv7/gNAgm80+Q1F3J55PZIkiQRQEARBEARghNYErlq1imuuuaZPNc+ioiL27dtHV1cXDoeDrKwsnn/+ea688sqefW699VY++OADsrKysFgsvPzyywAsWrRoJEK9aHyet4E7Pvw5H2R/cdbtr+57G41SzQOX3CN6AI4hCoWCa1MWsyD2EvZXHO0ZRbuYnSkMo5TEv7PhEu4Vwo+n3kF9RyNPb3sFs9Xc777lLVWcaiimsbMZh8PBU9te4qH1T2K1W3l49k+YHjGRJL9Y/A2+5DcU4aX1ZH7MjFF8NYIgCIIgjHXDfis/Ozubo0eP8sQTT/TZ1tLSwuOPP05paSmSJBEUFMQNN9zAD3/47ejIlVdeSW1tLT/72c9obGwkNTWV119/3a3bQ+wvP8J/jnwEwCe5X3FZwjw8td/+fVS11lDcXM6t6dcQ5R3urDCFcxgfnMrGgh1sK97LvIv8gtzmsKOUFEiS5OxQXMqsqKl0Wrt47cAq/rb7n/x69k/67NNkbuHX65/EYu8upuVv8KWuo4FxgYncOf5GYnwimBiaDkCn1cyGU9uZFJbe6/NCEARBEARh2JPAtLQ0jh8/ftZtGRkZfPbZZ+c9xt13393TXN6dtXa1UdxUzkc5X+Kn9+GBS+7htxv+zDtZn3LPlO51jwUNJTy6+VkApodPcGa4wjlMC5+Al87E4cpjLpEEiqmgI+PS+NnUtNfxad56ihrLiPbpfVMnpyYfi92KTqVFQkKjVLM4bg4rJt/W51h6tY6rk917BoUgCIIgCGcnruTGKIfs4K87XiWnNh+AO8ffQLxfNJdETmZT0S68dCbifKN4dtfr2Bw2HrjkHkJNwU6OWuiPJElkBqVwsDIbm912UbfucLW+h2PNtSmL+erkFj7J+4r7v7OGtLS5gud2vY4kSaxc+rRY3ycIgiAIwpBdvFeiLu5YzQlyavNJD0ri+tQrSAmIB+C29Gs51VDMRzlrAfDUGvndnAeI9Y1yZrjCAMyMnMzW4j28sOcNfjFzhbPDGTKRBI4so8aD+TEzWX9qK2ZbFzqVFoA3D38IwOK4OSIBFARBEAThgogkcIzaXLgLg1rPr2f/FM13LvgCjd2l3neWHKCwsYQlCXPxN/g6MVJhoMYHp6JSqNhVeoBrGi4l7iJN3G0Om8v0PByrJoWm82X+JnJqTjAxNJ2sqlyOVOVyfepl3Jp+/qrLgiAIgiAI5yKSwDGow9LJnrJDzI2e3isB/K6ZkZOYGTlplCMTLoRCoeDPi3/DQ+uf5B/73uIvS37n7JD6VdNeT1tXO9XttXx4bC0WmwVvvRcLYy8hpzYflSSSwJGU7B+HXqXjqW0vcVXSIvaXH8GkNXJdyuXODk0QBEEQBBcgksAxpsPayd92/xOL3cr8mJnODkcYZhFeodw07krePfoZNW11BBr9nR1SD7Oti40FOzhUeYwjVTm9to0LTCSnJp/jdacAmB011Rkhug2NSsP9M3/Ifw5/zBfHNwDw/6bdjValcXJkgiAIgiC4ApEEOkF9RyN7yg7xWd7XOGQHGqUaP4MPc6Km8UnuV1S31zEvegbxftHODlUYAVPDx/Pu0c/4LO/rs1Z1HC1VrTU8t2slpS2V2Bw2ZFkGQK/SMS96BlPCMzGo9YR4BuKr96bJ3EJrVxuyLBPsGei0uN3FhJA0xgUm8dzO15kYms6c6GnODkkQBEEQBBchksBRZHfY+fz4Bt47+hl22YGfwYeJIek0m1s4UHGU3NqTAPxo0jIujZ/t5GiFkRJuCmFO9DS+PrWNpSlL8PdwzprOj3PXUdBYwhWJC9CpNKgUKqK9w5kclnnW/b11Jrx1plGO0r1plGoemv1jZ4chCIIgCIKLEUngKPoyfxOrsj4BYMWkW5kdNQ29WgdAVVst7ZaOnpEXwbVdm7yYrUV7eGLLCzy+6FcYNR6jdm6r3Upu7Uk2F+7i0rjZ3D3hplE7tyAIgiAIguB8IgkcJW2Wdt7P/gKAR+b/ktTAhF7bg40BzghLcJIIr1DuyLyB/xz5iMc3/Y0nL/31qLVd+Mnnv6O5qxWT1shN464clXMKgiAIgiAIY4dIAkfJ/vIszLYuHlvwK5ID4pwdjjAGXJW0kLqOBr7M38T24n3MjZk+oOd9t3ccgCzLFDeVYbFbCfYMxKgx0GHtRK1QU9pcwamGYo5U5TAraio5NSdo7moF4E8LH8Rb7zUir00QBEEQBEEYu0QSOEqS/eO4Z/IykQAKPSRJ4u4JN3G0Oo+vTm7pkwTKssymwl10WDtIC0wm2iecjQU7+Mf+t5kfM5O7x9+ITq1jS9FuXtr7Zs/zgowBVLfV9jnf/oosAJQKJS9f/aRY3ycIgiAIguCmRBI4SoI9A0VFRaEPSZJYEDuTNw9/RFZVLhnBKTSbW/jVuj/RaTNjsVt79o3wCqW0uQKAjQU72FG8D38PX8pbqvDT+3BtymLeyfoUq91KkIc/HdZOFsReQmZwCp5aI9VtdWiUGpID4nqNJAqCIAiCIAjuRSSBguBkC2Nn8d7Rz9lctJtk/zie2PICzV2taFValmUsZXr4BDYW7uRUQzGhnkEsy1hKYWMJe8oOI8sycb5RXJu8mAivUC5LmNfveaK8w0fvRQmCIAiCIAhjlkgCBcHJ9GodE0LS2F68lyOVx2i1tHND6hXcOO6KnmIxyzKW9npOiGcgMyMnOyNcQRAEQRAE4SKncHYAgiDA9IiJAPgafPjRpGXckn71qFULFQRBEARBENyLGAkUhDFgZuQk4n2jW1kPJQAACdFJREFUCDT6OzsUQRAEQRAEwcWJkUBBGCNEAigIgiAIgiCMBpEECoIgCIIgCIIguBGRBAqCIAiCIAiCILgRkQQKgiAIgiAIgiC4EZEECoIgCIIgCIIguBGRBAqCIAiCIAiCILgRkQQKgiAIgiAIgiC4EZEECoIgCIIgCIIguBGRBAqCIAiCIAiCILgRkQQKgiAIgiAIgiC4EZEECoIgCIIgCIIguBGRBAqCIAiCIAiCILgRlbMDGG52ux2AqqoqJ0ciCIIgCIIgCIIw+s7kQmdyo+9zuSSwtrYWgOXLlzs5EkEQBEEQBEEQBOepra0lKiqqz+OSLMuyE+IZMWazmezsbAICAlAqlc4Op0dVVRXLly/n7bffJjg42NnhCKNIvPfuS7z37ku89+5LvPfuS7z37mmsvu92u53a2lrS0tLQ6XR9trvcSKBOp2Py5MnODqNfwcHBhIeHOzsMwQnEe+++xHvvvsR7777Ee+++xHvvnsbi+362EcAzRGEYQRAEQRAEQRAENyKSQEEQBEEQBEEQBDcikkBBEARBEARBEAQ3onzkkUcecXYQ7kKr1TJt2jS0Wq2zQxFGmXjv3Zd4792XeO/dl3jv3Zd4793Txfi+u1x1UEEQBEEQBEEQBKF/YjqoIAiCIAiCIAiCGxFJoCAIgiAIgiAIghsRSaAgCIIgCIIgCIIbEUngKHA4HDzzzDPMnDmTCRMm8MMf/pDy8nJnhyVcoBdeeIGUlBQmTJjQ8+eXv/xlz/acnBxuvfVWMjMzmTdvHm+++f/bu7+Qpt4wDuBfLcxgLuum0kChmJZupmYq/RVCIUM0JFRKEukPESgYVJLFLqRoSTAh60IMqRuX0R+1rpYGqUShlaZWZiaCaNlss3nU7f1dRKeWlv5o/tu+nxvZ+5xzfF+e8zzu3Q5Y7nD+yMgIzp49i82bNyMiIgK5ubkwmUyzvQyahurqamRkZCAiIgJBQUET4s7IdVVVFeLj46HRaJCUlISGhoYZXRNNbaq8BwUFQaPROPSAjo4OOT6d3l9fX4+kpCSEhYUhISEBNTU1M74umppOp0NiYiIiIiKwdetW5Ofn48uXLw7HsO5d03Ryz9p3TVeuXMGuXbsQGRmJ6OhoZGdno62tTY67XM0LmnHXrl0TcXFxorOzU1gsFnHmzBmxZ88eYbPZ5npq9A/0er3Yv3//pDGz2SxiY2NFcXGxGBkZEU1NTSIqKko8ePBAPqagoECkpKSIvr4+YTKZxKFDh8Thw4dna/r0Pzx+/Fjcv39fGAwGoVKpHGLOyPXz58+FWq0WRqNRSJIkKioqRFhYmOjt7Z21NdJEf8u7EEKoVCrR2Nj4x/On6v09PT1Co9GIiooKIUmSMBqNQqPRiObm5hlbE01PUVGRaG1tFaOjo+LTp08iKytLHDlyRI6z7l3XVLkXgrXvqt6/fy9MJpMQQghJkkRpaanYsmWLsNlsLlnz3ATOgri4OHHz5k359dDQkAgJCRFPnz6dw1nRv/rbJrCyslJuHD9cvHhRHDhwQAghhNVqFWq1WtTW1srxd+/eCZVKxTcA81hjY+OEzYAzcn3y5EmRk5PjcN3U1FRRXFw8U0uh/2GyvAsx9RvBqXq/Xq8XqampDufk5OSIU6dOOWnm5CxGo1GEh4fLr1n37uP33AvB2ncHkiSJsrIyoVKphMlkcsma5+OgM8xsNqO3txehoaHymFKpREBAgMNXzLQwtbS0ICYmBnFxccjLy0NPTw8AoL29HRs2bICn588SCw0NRXt7OwDgw4cPkCQJarVajq9duxZLly7lfbHAOCPX7e3tDj3i92vQ/JWXl4fo6GikpKSgoqJCHp9O72feF46GhgYEBwfLr1n37uP33P/A2ndNtbW12LRpE9RqNS5cuICsrCwsW7bMJWt+8Zz8VjdisVgAfG8Av/Lx8ZFjtDAlJCRg79698PPzQ39/P4qKipCVlYW7d+/CYrHAx8fH4XilUinn/MfP34/hfbHwOCPXFotlQo9QKpXo6uqaqWmTE1y/fh3h4eHw9PREY2MjTpw4gfHxcWRkZEyr91ssFqxbt84h/uu9Q/NDTU0NDAYDbty4IY+x7t3DZLkHWPuubOfOnXj27BlMJhPu3LmD1atXA3DNmuc3gTNMoVAA+P7J0K/MZrMco4VJpVLB398fHh4eWLlyJQoLCzEwMICmpiYoFIoJzfzr169yznlfuA5n5FqhUEyI/3oNmp9iY2Ph7e0NLy8vbN++HQcPHsS9e/cAMO+uorq6GufOnUNJSQlCQkLkcda96/tT7gHWvjvw9fVFZmYm8vPz8fbtW5eseW4CZ5iPjw/8/f3R0tIij5nNZnz8+BHr16+fw5mRs3l4eMDDwwNCCAQHB+P169ew2+1yvLW1VX6kJDAwEEuWLHG4Lzo7O2G1Wid97ITmL2fkOjg42CH++zVoYfD09IQQAsD0ej/zPr8ZDAZotVpcvXoVMTExDjHWvWv7W+4nw9p3TXa7HePj4+ju7nbJmucmcBakpaWhtLQUXV1d+PbtG3Q6HQIDAxEZGTnXU6N/UFNTg8HBQQDA58+fUVBQgBUrViA8PBzx8fGw2WwoKSnB6OgoXr58CYPBgPT0dACAt7c3kpOTodfr0d/fj6GhIeh0OuzYsQP+/v5zuSyahM1mgyRJGBsbAwBIkgRJkmC3252S63379sFoNKKurg5jY2OorKzEmzdvkJKSMmdrpr/nvbW1Fa9evcLo6CjGx8fx5MkTlJWVITExUT5/qt6fnJyMjo4OVFZWYmxsDHV1dXj06BHS0tLmZL30U3l5OS5duoTS0tJJ/1az7l3XVLln7buu8vJyDAwMAAAGBweh1Wrh5eWFjRs3umTNe4gfH13QjLHb7bh8+TJu3boFq9WKyMhIaLVarFmzZq6nRv/g6NGjaG5uhtVqhVKpRFRUFHJychAQEADg+/+T0Wq1aGtrw/Lly5GdnY3MzEz5/JGRERQWFuLhw4ew2WzYtm0btFotfH1952pJ9Ae3b9/G6dOnJ4yXl5cjOjraKbmuqqqCXq9HX18fAgICkJ+fj9jY2FlZH03ub3kfHh6GTqdDX18fFi1aBD8/P6Snp8tvCIDp9f76+nqcP38e3d3dWLVqFXJzc7F79+5ZWR/9WVBQEBYvXgwvLy+H8erqavj5+QFwTo9n3c8/U+XeaDSy9l3UsWPH8OLFCwwPD0OhUECtVuP48ePy48CuVvPcBBIREREREbkRPg5KRERERETkRrgJJCIiIiIiciPcBBIREREREbkRbgKJiIiIiIjcCDeBREREREREboSbQCIiIiIiIjfCTSAREREREZEb4SaQiIiIiIjIjXATSERERERE5Eb+A9giskZGf4EPAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"
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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure()\\n\",\n \"plt.plot(thorax_loc[:,0,0], 'y',label='fly-0')\\n\",\n \"plt.plot(thorax_loc[:,0,1], 'g',label='fly-1')\\n\",\n \"\\n\",\n \"plt.plot(-1*thorax_loc[:,1,0], 'y')\\n\",\n \"plt.plot(-1*thorax_loc[:,1,1], 'g')\\n\",\n \"\\n\",\n \"plt.legend(loc=\\\"center right\\\")\\n\",\n \"plt.title('Thorax locations')\\n\",\n \"\\n\",\n \"\\n\",\n \"plt.figure(figsize=(7,7))\\n\",\n \"plt.plot(thorax_loc[:,0,0],thorax_loc[:,1,0], 'y',label='fly-0')\\n\",\n \"plt.plot(thorax_loc[:,0,1],thorax_loc[:,1,1], 'g',label='fly-1')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.xlim(0,1024)\\n\",\n \"plt.xticks([])\\n\",\n \"\\n\",\n \"plt.ylim(0,1024)\\n\",\n \"plt.yticks([])\\n\",\n \"plt.title('Thorax tracks')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"jPe_mc-wM8Pd\"\n },\n \"source\": [\n \"## More advanced visualizations\\n\",\n \"\\n\",\n \"For some additional analysis, we'll first smooth and differentiate the data with a Savitzky-Golay filter to extract velocities of each joint.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"id\": \"IPCHkT1-M8Pd\"\n },\n \"outputs\": [],\n \"source\": [\n \"from scipy.signal import savgol_filter\\n\",\n \"\\n\",\n \"def smooth_diff(node_loc, win=25, poly=3):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" node_loc is a [frames, 2] array\\n\",\n \" \\n\",\n \" win defines the window to smooth over\\n\",\n \" \\n\",\n \" poly defines the order of the polynomial\\n\",\n \" to fit with\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" node_loc_vel = np.zeros_like(node_loc)\\n\",\n \" \\n\",\n \" for c in range(node_loc.shape[-1]):\\n\",\n \" node_loc_vel[:, c] = savgol_filter(node_loc[:, c], win, poly, deriv=1)\\n\",\n \" \\n\",\n \" node_vel = np.linalg.norm(node_loc_vel,axis=1)\\n\",\n \"\\n\",\n \" return node_vel\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Vpq6cw9pM8Pe\"\n },\n \"source\": [\n \"There are two flies. Let's get results for each separately.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"id\": \"jcRqRSUlM8Pf\"\n },\n \"outputs\": [],\n \"source\": [\n \"thx_vel_fly0 = smooth_diff(thorax_loc[:, :, 0])\\n\",\n \"thx_vel_fly1 = smooth_diff(thorax_loc[:, :, 1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"5CzJhQcPM8Pg\"\n },\n \"source\": [\n \"### Visualizing thorax x-y dynamics and velocity for fly 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 451\n },\n \"id\": \"L_CRmS1rM8Pg\",\n \"outputId\": \"8b598004-765a-4a02-a739-a98c604fd1a2\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Velocity')\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(15,7))\\n\",\n \"ax1 = fig.add_subplot(211)\\n\",\n \"ax1.plot(thorax_loc[:, 0, 0], 'k', label='x')\\n\",\n \"ax1.plot(-1*thorax_loc[:, 1, 0], 'k', label='y')\\n\",\n \"ax1.legend()\\n\",\n \"ax1.set_xticks([])\\n\",\n \"ax1.set_title('Thorax')\\n\",\n \"\\n\",\n \"ax2 = fig.add_subplot(212, sharex=ax1)\\n\",\n \"ax2.imshow(thx_vel_fly0[:,np.newaxis].T, aspect='auto', vmin=0, vmax=10)\\n\",\n \"ax2.set_yticks([])\\n\",\n \"ax2.set_title('Velocity')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"kXnxUssanZq3\"\n },\n \"source\": [\n \"### Visualize thorax colored by magnitude of fly speed\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 397\n },\n \"id\": \"jhmqJAFPM8Ph\",\n \"outputId\": \"e3a8d876-8efb-4694-ab15-f862fe2c114f\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Thorax tracks colored by magnitude of fly speed')\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(15,6))\\n\",\n \"ax1 = fig.add_subplot(121)\\n\",\n \"ax1.plot(thorax_loc[:, 0, 0], thorax_loc[:, 1, 0], 'k')\\n\",\n \"ax1.set_xlim(0,1024)\\n\",\n \"ax1.set_xticks([])\\n\",\n \"ax1.set_ylim(0,1024)\\n\",\n \"ax1.set_yticks([])\\n\",\n \"ax1.set_title('Thorax tracks')\\n\",\n \"\\n\",\n \"kp = thx_vel_fly0 # use thx_vel_fly1 for other fly\\n\",\n \"vmin = 0\\n\",\n \"vmax = 10\\n\",\n \"\\n\",\n \"ax2 = fig.add_subplot(122)\\n\",\n \"ax2.scatter(thorax_loc[:,0,0], thorax_loc[:,1,0], c=kp, s=4, vmin=vmin, vmax=vmax)\\n\",\n \"ax2.set_xlim(0,1024)\\n\",\n \"ax2.set_xticks([])\\n\",\n \"ax2.set_ylim(0,1024)\\n\",\n \"ax2.set_yticks([])\\n\",\n \"ax2.set_title('Thorax tracks colored by magnitude of fly speed')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"IHBEg-ysM8Pj\"\n },\n \"source\": [\n \"### Find covariance in thorax velocities between fly-0 and fly-1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"id\": \"x-cXNvlSM8Pj\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"def corr_roll(datax, datay, win):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" datax, datay are the two timeseries to find correlations between\\n\",\n \" \\n\",\n \" win sets the number of frames over which the covariance is computed\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" s1 = pd.Series(datax)\\n\",\n \" s2 = pd.Series(datay)\\n\",\n \" \\n\",\n \" return np.array(s2.rolling(win).corr(s1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 432\n },\n \"id\": \"cwUZXiqoM8Pk\",\n \"outputId\": \"9bfcdb44-f7fe-4666-a9e5-64e21a3c1149\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"win = 50\\n\",\n \"\\n\",\n \"cov_vel = corr_roll(thx_vel_fly0, thx_vel_fly1,win)\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(2, 1, sharex=True, figsize=(15,6))\\n\",\n \"ax[0].plot(thx_vel_fly0, 'y', label='fly-0')\\n\",\n \"ax[0].plot(thx_vel_fly1, 'g', label='fly-1')\\n\",\n \"ax[0].legend()\\n\",\n \"ax[0].set_title('Forward Velocity')\\n\",\n \"\\n\",\n \"ax[1].plot(cov_vel, 'c', markersize=1)\\n\",\n \"ax[1].set_ylim(-1.2, 1.2)\\n\",\n \"ax[1].set_title('Covariance')\\n\",\n \"\\n\",\n \"fig.tight_layout()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"wbJGtGNGM8Pm\"\n },\n \"source\": [\n \"## Clustering\\n\",\n \"\\n\",\n \"For an example of clustering the data, we'll\\n\",\n \"\\n\",\n \"1. extract joint velocities for each joint,\\n\",\n \"2. run simple k-means on the velocities from each frame.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"id\": \"4yf4CQDTM8Pm\"\n },\n \"outputs\": [],\n \"source\": [\n \"def instance_node_velocities(instance_idx):\\n\",\n \" fly_node_locations = locations[:, :, :, instance_idx]\\n\",\n \" fly_node_velocities = np.zeros((frame_count, node_count))\\n\",\n \"\\n\",\n \" for n in range(0, node_count):\\n\",\n \" fly_node_velocities[:, n] = smooth_diff(fly_node_locations[:, n, :])\\n\",\n \" \\n\",\n \" return fly_node_velocities\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"id\": \"wmq-D9fiqwwB\"\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_instance_node_velocities(instance_idx, node_velocities):\\n\",\n \" plt.figure(figsize=(20,8))\\n\",\n \" plt.imshow(node_velocities.T, aspect='auto', vmin=0, vmax=20, interpolation=\\\"nearest\\\")\\n\",\n \" plt.xlabel('frames')\\n\",\n \" plt.ylabel('nodes')\\n\",\n \" plt.yticks(np.arange(node_count), node_names, rotation=20);\\n\",\n \" plt.title(f'Fly {instance_idx} node velocities')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 523\n },\n \"id\": \"kn_LHczvqh5C\",\n \"outputId\": \"995ec261-9e92-4d58-c007-a61abe9a9330\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fly_ID = 0\\n\",\n \"fly_node_velocities = instance_node_velocities(fly_ID)\\n\",\n \"plot_instance_node_velocities(fly_ID, fly_node_velocities)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 523\n },\n \"id\": \"GWmQp_r0M8Pp\",\n \"outputId\": \"08587a35-0f8e-46b4-ff1c-ee80a6a407d3\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fly_ID = 1\\n\",\n \"fly_node_velocities = instance_node_velocities(fly_ID)\\n\",\n \"plot_instance_node_velocities(fly_ID, fly_node_velocities)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"id\": \"_BOOJFBtM8Pr\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.cluster import KMeans\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"id\": \"ZHY_uo_LM8Ps\"\n },\n \"outputs\": [],\n \"source\": [\n \"nstates = 10\\n\",\n \"\\n\",\n \"km = KMeans(n_clusters=nstates)\\n\",\n \"\\n\",\n \"labels = km.fit_predict(fly_node_velocities)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 741\n },\n \"id\": \"BO8udp-bM8Pt\",\n \"outputId\": \"d7dd2b61-9009-4c29-bab6-5f04a826c05c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(20, 12))\\n\",\n \"\\n\",\n \"ax1 = fig.add_subplot(211)\\n\",\n \"ax1.imshow(fly_node_velocities.T, aspect=\\\"auto\\\", vmin=0, vmax=20, interpolation=\\\"nearest\\\")\\n\",\n \"ax1.set_xlabel(\\\"Frames\\\")\\n\",\n \"ax1.set_ylabel(\\\"Nodes\\\")\\n\",\n \"ax1.set_yticks(np.arange(node_count))\\n\",\n \"ax1.set_yticklabels(node_names);\\n\",\n \"ax1.set_title(f\\\"Fly {fly_ID} node velocities\\\")\\n\",\n \"ax1.set_xlim(0,frame_count)\\n\",\n \"\\n\",\n \"ax2 = fig.add_subplot(212,sharex=ax1)\\n\",\n \"ax2.imshow(labels[None, :], aspect=\\\"auto\\\", cmap=\\\"tab10\\\", interpolation=\\\"nearest\\\")\\n\",\n \"ax2.set_xlabel(\\\"Frames\\\")\\n\",\n \"ax2.set_yticks([])\\n\",\n \"ax2.set_title(\\\"Ethogram (colors = clusters)\\\");\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"collapsed_sections\": [],\n \"name\": \"Analysis examples.ipynb\",\n \"provenance\": [],\n \"toc_visible\": true\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}" }, { "path": "docs/notebooks/Data_structures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n },\n \"source\": [\n \"\\\"Open\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"NqgGonrTRLg9\"\n },\n \"source\": [\n \"# Data structures\\n\",\n \"\\n\",\n \"In this notebook, we will explore some of the major data structures used in SLEAP, using [`sleap-io`](https://io.sleap.ai/) and how they can be manipulated when generating predictions from trained models. \\n\",\n \"\\n\",\n \"Note: `sleap-io` is a core dependency of SLEAP. If you have SLEAP installed, you already have `sleap-io` availableā€”no need for a separate installation! However, [`sleap-nn`](https://nn.sleap.ai/) is an optional dependency which handles training and inference. [`sleap-nn`](https://nn.sleap.ai/) is not installed by default. Ensure [`sleap-nn`](https://nn.sleap.ai/) is installed!\\n\",\n \"\\n\",\n \"A quick overview of the data structures before we start:\\n\",\n \"\\n\",\n \"- `PointsArray`/`PredictedPointsArray` ā†’ Contains array of `x, y` coordinates and other metadata, like visibility/ complete flag, (and `score` for predictions) of a landmark.\\n\",\n \"- `Instance`/`PredictedInstance` ā†’ Contains `PointsArray`/`PredictedPointsArray`. This represent a single individual within a frame and may also contain an associated `Track`.\\n\",\n \"- `Skeleton` ā†’ Defines the nodes and edges that define the set of unique landmark types that each point represents, e.g., \\\"head\\\", \\\"tail\\\", etc. This *does not contain positions* -- those are stored in individual `PointsArray`s.\\n\",\n \"- `LabeledFrame` ā†’ Contains a set of `Instance`/`PredictedInstance`s for a single frame.\\n\",\n \"- `Labels` ā†’ Contains a set of `LabeledFrame`s and the associated metadata for the videos and other information related to the project or predictions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"8BOjXv09U2iK\"\n },\n \"source\": [\n \"## 1. Setup dependencies and data\\n\",\n \"\\n\",\n \"We'll start by installing [`sleap-io`](https://io.sleap.ai/#installation) and downloading some data and models to play around with.\\n\",\n \"\\n\",\n \"If you get a dependency error in subsequent cells, just click **Runtime** ā†’ **Restart runtime** to reload the packages.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"3GTiapGASisF\",\n \"outputId\": \"c7ce8c05-a473-4995-8cab-0f20d04a52b1\"\n },\n \"outputs\": [],\n \"source\": [\n \"# This should take care of all the IO dependencies on colab:\\n\",\n \"!pip install -qqq \\\"sleap-io\\\"\\n\",\n \"\\n\",\n \"# This should take care of all the torch dependencies on colab:\\n\",\n \"!pip install -qqq \\\"sleap-nn[torch-cpu]\\\" \\n\",\n \"\\n\",\n \"# if you have GPU (in colab, enable GPU runtime)\\n\",\n \"# !pip install -qqq \\\"sleap-nn[torch-cuda128]\\\"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"0n8oqLWBU0v7\",\n \"outputId\": \"f9cdcfe1-d152-4a0a-b769-6f9f7d8c0cf0\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"--2025-09-22 11:16:31-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\\n\",\n \"Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.72.251, 142.251.40.59, 142.250.176.27, ...\\n\",\n \"Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.72.251|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 85343812 (81M) [video/mp4]\\n\",\n \"Saving to: ā€˜190719_090330_wt_18159206_rig1.2@15000-17560.mp4ā€™\\n\",\n \"\\n\",\n \"190719_090330_wt_18 100%[===================>] 81.39M 26.3MB/s in 3.1s \\n\",\n \"\\n\",\n \"2025-09-22 11:16:34 (26.3 MB/s) - ā€˜190719_090330_wt_18159206_rig1.2@15000-17560.mp4ā€™ saved [85343812/85343812]\\n\",\n \"\\n\",\n \"--2025-09-22 11:16:35-- https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp\\n\",\n \"Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.72.251, 142.251.40.59, 142.250.176.27, ...\\n\",\n \"Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.72.251|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 1581400 (1.5M) [application/octet-stream]\\n\",\n \"Saving to: ā€˜190719_090330_wt_18159206_rig1.2@15000-17560.slpā€™\\n\",\n \"\\n\",\n \"190719_090330_wt_18 100%[===================>] 1.51M --.-KB/s in 0.07s \\n\",\n \"\\n\",\n \"2025-09-22 11:16:35 (22.9 MB/s) - ā€˜190719_090330_wt_18159206_rig1.2@15000-17560.slpā€™ saved [1581400/1581400]\\n\",\n \"\\n\",\n \"--2025-09-22 11:16:35-- https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\\n\",\n \"Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.72.251, 142.251.40.59, 142.250.176.27, ...\\n\",\n \"Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.72.251|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 6372537 (6.1M) [application/zip]\\n\",\n \"Saving to: ā€˜centroid.fast.210504_182918.centroid.n=1800.zipā€™\\n\",\n \"\\n\",\n \"centroid.fast.21050 100%[===================>] 6.08M 39.1MB/s in 0.2s \\n\",\n \"\\n\",\n \"2025-09-22 11:16:35 (39.1 MB/s) - ā€˜centroid.fast.210504_182918.centroid.n=1800.zipā€™ saved [6372537/6372537]\\n\",\n \"\\n\",\n \"--2025-09-22 11:16:35-- https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip\\n\",\n \"Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.72.251, 142.251.40.59, 142.250.176.27, ...\\n\",\n \"Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.72.251|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 30775963 (29M) [application/zip]\\n\",\n \"Saving to: ā€˜td_fast.210505_012601.centered_instance.n=1800.zipā€™\\n\",\n \"\\n\",\n \"td_fast.210505_0126 100%[===================>] 29.35M 25.5MB/s in 1.1s \\n\",\n \"\\n\",\n \"2025-09-22 11:16:37 (25.5 MB/s) - ā€˜td_fast.210505_012601.centered_instance.n=1800.zipā€™ saved [30775963/30775963]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Test video:\\n\",\n \"!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.mp4\\n\",\n \"\\n\",\n \"# Test video labels (from predictions/not necessary for inference benchmarking):\\n\",\n \"!wget https://storage.googleapis.com/sleap-data/reference/flies13/190719_090330_wt_18159206_rig1.2%4015000-17560.slp\\n\",\n \"\\n\",\n \"# Bottom-up model:\\n\",\n \"# !wget https://storage.googleapis.com/sleap-data/reference/flies13/bu.210506_230852.multi_instance.n%3D1800.zip\\n\",\n \"\\n\",\n \"# Top-down model (two-stage):\\n\",\n \"!wget https://storage.googleapis.com/sleap-data/reference/flies13/centroid.fast.210504_182918.centroid.n%3D1800.zip\\n\",\n \"!wget https://storage.googleapis.com/sleap-data/reference/flies13/td_fast.210505_012601.centered_instance.n%3D1800.zip\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Archive: centroid.fast.210504_182918.centroid.n=1800.zip\\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/best_model.h5 \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/initial_config.json \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/labels_gt.test.slp \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/labels_gt.train.slp \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/labels_gt.val.slp \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/labels_pr.test.slp \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/labels_pr.train.slp \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/labels_pr.val.slp \\n\",\n \" extracting: centroid.fast.210504_182918.centroid.n=1800/metrics.test.npz \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/metrics.train.npz \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/metrics.val.npz \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/model_info.json \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/training_config.json \\n\",\n \" inflating: centroid.fast.210504_182918.centroid.n=1800/training_log.csv \\n\",\n \"Archive: td_fast.210505_012601.centered_instance.n=1800.zip\\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/best_model.h5 \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/initial_config.json \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/labels_gt.test.slp \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/labels_gt.train.slp \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/labels_gt.val.slp \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/labels_pr.test.slp \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/labels_pr.train.slp \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/labels_pr.val.slp \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/labels_pr.with_centroid.test.slp \\n\",\n \" extracting: td_fast.210505_012601.centered_instance.n=1800/metrics.test.npz \\n\",\n \" extracting: td_fast.210505_012601.centered_instance.n=1800/metrics.train.npz \\n\",\n \" extracting: td_fast.210505_012601.centered_instance.n=1800/metrics.val.npz \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/metrics.with_centroid.test.npz \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/model_info.json \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/training_config.json \\n\",\n \" inflating: td_fast.210505_012601.centered_instance.n=1800/training_log.csv \\n\"\n ]\n }\n ],\n \"source\": [\n \"# In Google Colab, unzip is installed by default, so we can just run:\\n\",\n \"import os\\n\",\n \"\\n\",\n \"# Unzip each file into a directory named after the zip file (without .zip)\\n\",\n \"for zip_file in [\\n\",\n \" \\\"centroid.fast.210504_182918.centroid.n=1800.zip\\\",\\n\",\n \" \\\"td_fast.210505_012601.centered_instance.n=1800.zip\\\"\\n\",\n \"]:\\n\",\n \" dir_name = os.path.splitext(zip_file)[0]\\n\",\n \" os.makedirs(dir_name, exist_ok=True)\\n\",\n \" # The -d flag tells unzip to extract into the given directory\\n\",\n \" !unzip -o \\\"{zip_file}\\\" -d \\\"{dir_name}\\\"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"F-zzLnAoWrC5\",\n \"outputId\": \"b0ae7571-3ac0-42c7-d50f-982e4d9a459f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total 278704\\n\",\n \"drwxr-xr-x@ 19 divyasesh staff 608B Sep 22 11:16 \\u001b[34m.\\u001b[m\\u001b[m\\n\",\n \"drwxr-xr-x@ 14 divyasesh staff 448B Sep 19 20:29 \\u001b[34m..\\u001b[m\\u001b[m\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 81M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.mp4\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 1.5M May 20 2021 190719_090330_wt_18159206_rig1.2@15000-17560.slp\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 713K Sep 22 10:30 Analysis_examples.ipynb\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 461K Sep 22 11:16 Data_structures.ipynb\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 179K Sep 19 19:36 Interactive_and_realtime_inference.ipynb\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 45K Sep 19 19:36 Interactive_and_resumable_training.ipynb\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 157K Sep 19 19:36 Model_evaluation.ipynb\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 132K Sep 19 19:36 Post_inference_tracking.ipynb\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 471K Sep 19 19:36 SLEAP_Tutorial_at_Cosyne_2024_Using_exported_data.ipynb\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 93K Sep 19 19:36 Training_and_inference_on_an_example_dataset.ipynb\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 12K Sep 19 19:36 Training_and_inference_using_Google_Drive.ipynb\\n\",\n \"drwxr-xr-x@ 16 divyasesh staff 512B Sep 22 11:16 \\u001b[34mcentroid.fast.210504_182918.centroid.n=1800\\u001b[m\\u001b[m\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 6.1M May 20 2021 centroid.fast.210504_182918.centroid.n=1800.zip\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 3.4K Sep 22 11:01 notebooks-overview.md\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 16K Sep 19 19:36 sleap_io_idtracker_IDs.ipynb\\n\",\n \"drwxr-xr-x@ 18 divyasesh staff 576B Sep 22 11:16 \\u001b[34mtd_fast.210505_012601.centered_instance.n=1800\\u001b[m\\u001b[m\\n\",\n \"-rw-r--r--@ 1 divyasesh staff 29M May 20 2021 td_fast.210505_012601.centered_instance.n=1800.zip\\n\"\n ]\n }\n ],\n \"source\": [\n \"!ls -lah\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"w6xCj73QXM0t\",\n \"outputId\": \"47d181ba-9272-4b9d-ab2a-0fcae34f38d1\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0.5.3'\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import sleap_io as sio\\n\",\n \"\\n\",\n \"sio.__version__\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0.0.1'\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import sleap_nn as snn\\n\",\n \"\\n\",\n \"snn.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"B-y49i6sWu45\"\n },\n \"source\": [\n \"## 2. Data structures and inference\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"0Fyey-smRjXx\"\n },\n \"source\": [\n \"sleap-io can read videos in a variety of different formats through the `sio.load_video` high level API. Once loaded, the `sio.Video` object allows you to access individual frames as if the it were a standard numpy array.\\n\",\n \"\\n\",\n \"**Note:** The actual frames are not loaded until you access them so we don't blow up our memory when using long videos.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"cH_qfme2We7k\",\n \"outputId\": \"cb6aaf9c-ab38-4b3b-ffac-8acd78bf13c1\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(2560, 1024, 1024, 1)\\n\",\n \"(4, 1024, 1024, 1) uint8\\n\"\n ]\n }\n ],\n \"source\": [\n \"video_path = \\\"190719_090330_wt_18159206_rig1.2@15000-17560.mp4\\\"\\n\",\n \"\\n\",\n \"# Videos can be represented agnostic to the backend format\\n\",\n \"video = sio.load_video(video_path)\\n\",\n \"\\n\",\n \"# sleap.Video objects have a numpy-like interface:\\n\",\n \"print(video.shape)\\n\",\n \"\\n\",\n \"# And we can load images in the video using array indexing:\\n\",\n \"imgs = video[:4]\\n\",\n \"print(imgs.shape, imgs.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"oPiNRLWlZKZS\"\n },\n \"source\": [\n \"The high level interface for running inference (`sleap_nn.predict.run_inference()`) takes model folders as input and returns a `sio.Labels` object with the predicted instances. These are outputs from our training procedure and need to contain a `\\\"best.ckpt\\\"` (or `best_model.h5` for SLEAP <= 1.4.1.) and `\\\"training_config.yaml\\\"` (or `training_config.json` for SLEAP <= 1.4.1).\\n\",\n \" \\n\",\n \"`best.ckpt` is a checkpoint file containing the Torch model weights that was saved during\\n\",\n \"training. It includes the model's parameters, so they're standalone and don't need SLEAP/ SLEAP-NN -- \\n\",\n \"BUT they do not contain the inference methods.\\n\",\n \"\\n\",\n \"`training_config.yaml` is a serialized `sleap_nn.config.TrainingJobConfig` that contains metadata\\n\",\n \"like what channels of the model correspond to what landmarks and etc.\\n\",\n \"\\n\",\n \"Top-down models have two stages: centroid and centered instance confidence maps, which we train and save out separately, so loading them together links them up into a single inference model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"id\": \"wnIgeeivXiln\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2025-09-22 11:16:42 | INFO | sleap_nn.predict:run_inference:291 | Started inference at: 2025-09-22 11:16:42.955460\\n\",\n \"2025-09-22 11:16:42 | INFO | sleap_nn.predict:run_inference:302 | Integral refinement is not supported with MPS device. Using CPU.\\n\",\n \"2025-09-22 11:16:42 | INFO | sleap_nn.predict:run_inference:307 | Using device: cpu\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:get_keras_first_layer_channels:57 | Found first layer 'model_weights/stack0_enc0_conv0/stack0_enc0_conv0/kernel:0' with 1 input channels\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:map_legacy_to_pytorch_layers:269 | Successfully mapped 34/34 legacy weights to PyTorch parameters\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:360 | Successfully loaded 34/34 weights from legacy model\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:371 | Verifying weight assignments...\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:426 | āœ“ All weight assignments verified successfully\\n\"\n ]\n },\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"18dc75a7200d45fe8bab21082317336f\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"Output()\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:get_keras_first_layer_channels:57 | Found first layer 'model_weights/stack0_enc0_conv0/stack0_enc0_conv0/kernel:0' with 1 input channels\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:map_legacy_to_pytorch_layers:269 | Successfully mapped 34/34 legacy weights to PyTorch parameters\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:360 | Successfully loaded 34/34 weights from legacy model\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:371 | Verifying weight assignments...\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:426 | āœ“ All weight assignments verified successfully\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:get_keras_first_layer_channels:57 | Found first layer 'model_weights/stack0_enc0_conv0/stack0_enc0_conv0/kernel:0' with 1 input channels\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:map_legacy_to_pytorch_layers:269 | Successfully mapped 34/34 legacy weights to PyTorch parameters\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:360 | Successfully loaded 34/34 weights from legacy model\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:371 | Verifying weight assignments...\\n\",\n \"2025-09-22 11:16:43 | INFO | sleap_nn.legacy_models:load_legacy_model_weights:426 | āœ“ All weight assignments verified successfully\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\"\n            ],\n            \"text/plain\": []\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        },\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"2025-09-22 11:22:14 | INFO | sleap_nn.predict:run_inference:421 | Finished inference at: 2025-09-22 11:22:14.085145\\n\",\n            \"2025-09-22 11:22:14 | INFO | sleap_nn.predict:run_inference:422 | Total runtime: 331.1298098564148 secs\\n\",\n            \"2025-09-22 11:22:14 | INFO | sleap_nn.predict:run_inference:431 | Predictions output path: 190719_090330_wt_18159206_rig1.2@15000-17560.predictions.slp\\n\",\n            \"2025-09-22 11:22:14 | INFO | sleap_nn.predict:run_inference:432 | Saved file at: 2025-09-22 11:22:14.194343\\n\"\n          ]\n        },\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"Labels(labeled_frames=2560, videos=1, skeletons=1, tracks=0, suggestions=0, sessions=0)\"\n            ]\n          },\n          \"execution_count\": 8,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"from sleap_nn.predict import run_inference\\n\",\n        \"\\n\",\n        \"# Top-down\\n\",\n        \"labels = run_inference(data_path=video_path, model_paths=[\\n\",\n        \"    \\\"centroid.fast.210504_182918.centroid.n=1800\\\",\\n\",\n        \"    \\\"td_fast.210505_012601.centered_instance.n=1800\\\"\\n\",\n        \"    ])\\n\",\n        \"\\n\",\n        \"# Predicted labels\\n\",\n        \"labels\\n\",\n        \"\\n\",\n        \"# Bottom-up\\n\",\n        \"# labels = run_inference(data_path=video_path, model_paths=[\\n\",\n        \"#     \\\"bu.210506_230852.multi_instance.n=1800.zip\\\"\\n\",\n        \"#     ])\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"3iiyg26z-rZt\"\n      },\n      \"source\": [\n        \"Labels contain not just the predicted data, but all the other associated data structures and metadata:\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 9,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"EgL-bqRj-l6R\",\n        \"outputId\": \"3fd8f355-92b1-4bbb-b7e9-d564b007d97b\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"[Video(filename=\\\"190719_090330_wt_18159206_rig1.2@15000-17560.mp4\\\", shape=(2560, 1024, 1024, 1), backend=MediaVideo)]\"\n            ]\n          },\n          \"execution_count\": 9,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"labels.videos\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 10,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"EOu9c9ly-nkN\",\n        \"outputId\": \"3e66210c-12f6-48e4-c829-41aa3768b140\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"[Skeleton(nodes=[\\\"head\\\", \\\"thorax\\\", \\\"abdomen\\\", \\\"wingL\\\", \\\"wingR\\\", \\\"forelegL4\\\", \\\"forelegR4\\\", \\\"midlegL4\\\", \\\"midlegR4\\\", \\\"hindlegL4\\\", \\\"hindlegR4\\\", \\\"eyeL\\\", \\\"eyeR\\\"], edges=[(0, 11), (0, 12), (1, 0), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10)])]\"\n            ]\n          },\n          \"execution_count\": 10,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"labels.skeletons\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"8bVug0aH8sOA\"\n      },\n      \"source\": [\n        \"Individual labeled frames are accessible through a list-like interface:\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 11,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"pGcyrjKf8hp4\",\n        \"outputId\": \"1ff0ab5a-5a67-4d35-c09f-21adbcec655e\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"LabeledFrame(video=Video(filename=\\\"190719_090330_wt_18159206_rig1.2@15000-17560.mp4\\\", shape=(2560, 1024, 1024, 1), backend=MediaVideo), frame_idx=0, instances=[PredictedInstance(points=[[234.6404266357422, 430.84320068359375], [271.8457336425781, 436.1230773925781], [308.34124755859375, 438.6607971191406], [321.9830627441406, 440.3363952636719], [322.0407409667969, 437.0755615234375], [246.48248291015625, 450.92755126953125], [242.76162719726562, 413.98126220703125], [286.1595764160156, 460.1742858886719], [272.72576904296875, 407.01220703125], [nan, nan], [318.0406494140625, 430.85784912109375], [242.54898071289062, 442.32696533203125], [245.7898712158203, 422.02001953125]], track=None, score=0.94, tracking_score=None), PredictedInstance(points=[[319.83282470703125, 436.218017578125], [354.3247375488281, 435.20550537109375], [369.22540283203125, 433.84979248046875], [394.0439453125, 481.4942321777344], [399.39190673828125, 430.14251708984375], [307.92706298828125, 446.9759826660156], [305.74822998046875, 421.2812194824219], [325.94049072265625, 475.3349609375], [332.0487365722656, 385.46990966796875], [364.0356750488281, 475.0806884765625], [375.80450439453125, 399.5433349609375], [329.64776611328125, 445.9298095703125], [328.3006896972656, 425.326904296875]], track=None, score=0.81, tracking_score=None)])\"\n            ]\n          },\n          \"execution_count\": 11,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"labeled_frame = labels[0]  # shortcut for labels.labeled_frames[0]\\n\",\n        \"labeled_frame\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"5DrFjiOk9Vbt\"\n      },\n      \"source\": [\n        \"Convenient methods allow for easy inspection:\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 12,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"import seaborn as sns\\n\",\n        \"from typing import Union, Tuple\\n\",\n        \"import matplotlib.pyplot as plt\\n\",\n        \"import numpy as np\\n\",\n        \"import matplotlib\\n\",\n        \"\\n\",\n        \"def imgfig(\\n\",\n        \"    size: Union[float, Tuple] = 6, dpi: int = 72, scale: float = 1.0\\n\",\n        \") -> matplotlib.figure.Figure:\\n\",\n        \"    \\\"\\\"\\\"Create a tight figure for image plotting.\\n\",\n        \"\\n\",\n        \"    Args:\\n\",\n        \"        size: Scalar or 2-tuple specifying the (width, height) of the figure in inches.\\n\",\n        \"            If scalar, will assume equal width and height.\\n\",\n        \"        dpi: Dots per inch, controlling the resolution of the image.\\n\",\n        \"        scale: Factor to scale the size of the figure by. This is a convenience for\\n\",\n        \"            increasing the size of the plot at the same DPI.\\n\",\n        \"\\n\",\n        \"    Returns:\\n\",\n        \"        A matplotlib.figure.Figure to use for plotting.\\n\",\n        \"    \\\"\\\"\\\"\\n\",\n        \"    if not isinstance(size, (tuple, list)):\\n\",\n        \"        size = (size, size)\\n\",\n        \"    fig = plt.figure(figsize=(scale * size[0], scale * size[1]), dpi=dpi)\\n\",\n        \"    ax = fig.add_axes([0, 0, 1, 1], frameon=False)\\n\",\n        \"    ax.get_xaxis().set_visible(False)\\n\",\n        \"    ax.get_yaxis().set_visible(False)\\n\",\n        \"    plt.autoscale(tight=True)\\n\",\n        \"    ax.set_xticks([])\\n\",\n        \"    ax.set_yticks([])\\n\",\n        \"    ax.grid(False)\\n\",\n        \"    return fig\\n\",\n        \"\\n\",\n        \"def plot_img(\\n\",\n        \"    img: np.ndarray, dpi: int = 72, scale: float = 1.0\\n\",\n        \") -> matplotlib.figure.Figure:\\n\",\n        \"    \\\"\\\"\\\"Plot an image in a tight figure.\\\"\\\"\\\"\\n\",\n        \"    if hasattr(img, \\\"numpy\\\"):\\n\",\n        \"        img = img.numpy()\\n\",\n        \"\\n\",\n        \"    if img.shape[0] == 1:\\n\",\n        \"        # Squeeze out batch singleton dimension.\\n\",\n        \"        img = img.squeeze(axis=0)\\n\",\n        \"\\n\",\n        \"    # Check if image is grayscale (single channel).\\n\",\n        \"    grayscale = img.shape[-1] == 1\\n\",\n        \"    if grayscale:\\n\",\n        \"        # Squeeze out singleton channel.\\n\",\n        \"        img = img.squeeze(axis=-1)\\n\",\n        \"\\n\",\n        \"    # Normalize the range of pixel values.\\n\",\n        \"    img_min = img.min()\\n\",\n        \"    img_max = img.max()\\n\",\n        \"    if img_min < 0.0 or img_max > 1.0:\\n\",\n        \"        img = (img - img_min) / (img_max - img_min)\\n\",\n        \"\\n\",\n        \"    fig = imgfig(\\n\",\n        \"        size=(float(img.shape[1]) / dpi, float(img.shape[0]) / dpi),\\n\",\n        \"        dpi=dpi,\\n\",\n        \"        scale=scale,\\n\",\n        \"    )\\n\",\n        \"\\n\",\n        \"    ax = fig.gca()\\n\",\n        \"    ax.imshow(\\n\",\n        \"        img,\\n\",\n        \"        cmap=\\\"gray\\\" if grayscale else None,\\n\",\n        \"        origin=\\\"upper\\\",\\n\",\n        \"        extent=[-0.5, img.shape[1] - 0.5, img.shape[0] - 0.5, -0.5],\\n\",\n        \"    )\\n\",\n        \"    return fig\\n\",\n        \"\\n\",\n        \"def plot_instance(\\n\",\n        \"    instance,\\n\",\n        \"    skeleton=None,\\n\",\n        \"    cmap=None,\\n\",\n        \"    color_by_node=False,\\n\",\n        \"    lw=2,\\n\",\n        \"    ms=10,\\n\",\n        \"    bbox=None,\\n\",\n        \"    scale=1.0,\\n\",\n        \"    **kwargs,\\n\",\n        \"):\\n\",\n        \"    \\\"\\\"\\\"Plot a single instance with edge coloring.\\\"\\\"\\\"\\n\",\n        \"    if cmap is None:\\n\",\n        \"        cmap = sns.color_palette(\\\"tab20\\\")\\n\",\n        \"\\n\",\n        \"    if skeleton is None and hasattr(instance, \\\"skeleton\\\"):\\n\",\n        \"        skeleton = instance.skeleton\\n\",\n        \"\\n\",\n        \"    if skeleton is None:\\n\",\n        \"        color_by_node = True\\n\",\n        \"    else:\\n\",\n        \"        if len(skeleton.edges) == 0:\\n\",\n        \"            color_by_node = True\\n\",\n        \"\\n\",\n        \"    if hasattr(instance, \\\"numpy\\\"):\\n\",\n        \"        inst_pts = instance.numpy()\\n\",\n        \"    else:\\n\",\n        \"        inst_pts = instance\\n\",\n        \"\\n\",\n        \"    h_lines = []\\n\",\n        \"    if color_by_node:\\n\",\n        \"        for k, (x, y) in enumerate(inst_pts):\\n\",\n        \"            if bbox is not None:\\n\",\n        \"                x -= bbox[1]\\n\",\n        \"                y -= bbox[0]\\n\",\n        \"\\n\",\n        \"            x *= scale\\n\",\n        \"            y *= scale\\n\",\n        \"\\n\",\n        \"            h_lines_k = plt.plot(x, y, \\\".\\\", ms=ms, c=cmap[k % len(cmap)], **kwargs)\\n\",\n        \"            h_lines.append(h_lines_k)\\n\",\n        \"\\n\",\n        \"    else:\\n\",\n        \"        for k, (src_node, dst_node) in enumerate(skeleton.edges):\\n\",\n        \"            pts = instance.numpy()\\n\",\n        \"            src_pt = pts[skeleton.node_names.index(src_node.name)]\\n\",\n        \"            dst_pt = pts[skeleton.node_names.index(dst_node.name)]\\n\",\n        \"\\n\",\n        \"            x = np.array([src_pt[0], dst_pt[0]])\\n\",\n        \"            y = np.array([src_pt[1], dst_pt[1]])\\n\",\n        \"\\n\",\n        \"            if bbox is not None:\\n\",\n        \"                x -= bbox[1]\\n\",\n        \"                y -= bbox[0]\\n\",\n        \"\\n\",\n        \"            x *= scale\\n\",\n        \"            y *= scale\\n\",\n        \"\\n\",\n        \"            h_lines_k = plt.plot(\\n\",\n        \"                x, y, \\\".-\\\", ms=ms, lw=lw, c=cmap[k % len(cmap)], **kwargs\\n\",\n        \"            )\\n\",\n        \"\\n\",\n        \"            h_lines.append(h_lines_k)\\n\",\n        \"\\n\",\n        \"    return h_lines\\n\",\n        \"\\n\",\n        \"def plot_instances(\\n\",\n        \"    instances, skeleton=None, cmap=None, color_by_track=False, tracks=None, **kwargs\\n\",\n        \"):\\n\",\n        \"    \\\"\\\"\\\"Plot a list of instances with identity coloring.\\\"\\\"\\\"\\n\",\n        \"\\n\",\n        \"    if cmap is None:\\n\",\n        \"        cmap = sns.color_palette(\\\"tab10\\\")\\n\",\n        \"\\n\",\n        \"    if color_by_track and tracks is None:\\n\",\n        \"        # Infer tracks for ordering if not provided.\\n\",\n        \"        tracks = set()\\n\",\n        \"        for instance in instances:\\n\",\n        \"            tracks.add(instance.track)\\n\",\n        \"\\n\",\n        \"        # Sort by spawned frame.\\n\",\n        \"        tracks = sorted(list(tracks), key=lambda track: track.name)\\n\",\n        \"\\n\",\n        \"    h_lines = []\\n\",\n        \"    for i, instance in enumerate(instances):\\n\",\n        \"        if color_by_track:\\n\",\n        \"            if instance.track is None:\\n\",\n        \"                raise ValueError(\\n\",\n        \"                    \\\"Instances must have a set track when coloring by track.\\\"\\n\",\n        \"                )\\n\",\n        \"\\n\",\n        \"            if instance.track not in tracks:\\n\",\n        \"                raise ValueError(\\\"Instance has a track not found in specified tracks.\\\")\\n\",\n        \"\\n\",\n        \"            color = cmap[tracks.index(instance.track) % len(cmap)]\\n\",\n        \"\\n\",\n        \"        else:\\n\",\n        \"            # Color by identity (order in list).\\n\",\n        \"            color = cmap[i % len(cmap)]\\n\",\n        \"\\n\",\n        \"        h_lines_i = plot_instance(instance, skeleton=skeleton, cmap=[color], **kwargs)\\n\",\n        \"        h_lines.append(h_lines_i)\\n\",\n        \"\\n\",\n        \"    return h_lines\\n\",\n        \"\\n\",\n        \"def plot(lf, scale=0.5):\\n\",\n        \"    plot_img(lf.image, scale=scale)\\n\",\n        \"    plot_instances(lf.instances)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 13,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 543\n        },\n        \"id\": \"s2YiRWSa7f6D\",\n        \"outputId\": \"3f76ae98-dd72-4c2e-ac06-9bfe3b2c2637\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"image/png\": 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\",\n            \"text/plain\": [\n              \"
\"\n            ]\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        }\n      ],\n      \"source\": [\n        \"plot(labels[0], scale=0.5)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"gU8q0OCb9b9m\"\n      },\n      \"source\": [\n        \"The labeled frame is itself a container for instances:\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 14,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"ZP9Z0etc9e0c\",\n        \"outputId\": \"00986c80-23d0-43fa-f4f9-c60482e5293e\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"[PredictedInstance(points=[[234.6404266357422, 430.84320068359375], [271.8457336425781, 436.1230773925781], [308.34124755859375, 438.6607971191406], [321.9830627441406, 440.3363952636719], [322.0407409667969, 437.0755615234375], [246.48248291015625, 450.92755126953125], [242.76162719726562, 413.98126220703125], [286.1595764160156, 460.1742858886719], [272.72576904296875, 407.01220703125], [nan, nan], [318.0406494140625, 430.85784912109375], [242.54898071289062, 442.32696533203125], [245.7898712158203, 422.02001953125]], track=None, score=0.94, tracking_score=None),\\n\",\n              \" PredictedInstance(points=[[319.83282470703125, 436.218017578125], [354.3247375488281, 435.20550537109375], [369.22540283203125, 433.84979248046875], [394.0439453125, 481.4942321777344], [399.39190673828125, 430.14251708984375], [307.92706298828125, 446.9759826660156], [305.74822998046875, 421.2812194824219], [325.94049072265625, 475.3349609375], [332.0487365722656, 385.46990966796875], [364.0356750488281, 475.0806884765625], [375.80450439453125, 399.5433349609375], [329.64776611328125, 445.9298095703125], [328.3006896972656, 425.326904296875]], track=None, score=0.81, tracking_score=None)]\"\n            ]\n          },\n          \"execution_count\": 14,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"labeled_frame.instances\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 15,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"Y-stVhiw9uIr\",\n        \"outputId\": \"4cd7dbdf-bd91-4037-b971-3a17c85193bd\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"PredictedInstance(points=[[234.6404266357422, 430.84320068359375], [271.8457336425781, 436.1230773925781], [308.34124755859375, 438.6607971191406], [321.9830627441406, 440.3363952636719], [322.0407409667969, 437.0755615234375], [246.48248291015625, 450.92755126953125], [242.76162719726562, 413.98126220703125], [286.1595764160156, 460.1742858886719], [272.72576904296875, 407.01220703125], [nan, nan], [318.0406494140625, 430.85784912109375], [242.54898071289062, 442.32696533203125], [245.7898712158203, 422.02001953125]], track=None, score=0.94, tracking_score=None)\"\n            ]\n          },\n          \"execution_count\": 15,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"instance = labeled_frame[0]  # shortcut for labeled_frame.instances[0]\\n\",\n        \"instance\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"MMOUcx6Z94TJ\"\n      },\n      \"source\": [\n        \"Finally, instances are containers for points:\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 16,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"7xK-uGJZ905J\",\n        \"outputId\": \"102accd0-ba45-44b0-b839-eff15a06245a\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"PredictedPointsArray([([234.64042664, 430.84320068], 0.93376952,  True, False, 'head'),\\n\",\n              \"                      ([271.84573364, 436.12307739], 0.93177146,  True, False, 'thorax'),\\n\",\n              \"                      ([308.34124756, 438.66079712], 0.59078413,  True, False, 'abdomen'),\\n\",\n              \"                      ([321.98306274, 440.33639526], 0.41335493,  True, False, 'wingL'),\\n\",\n              \"                      ([322.04074097, 437.07556152], 0.45038646,  True, False, 'wingR'),\\n\",\n              \"                      ([246.48248291, 450.92755127], 0.86102617,  True, False, 'forelegL4'),\\n\",\n              \"                      ([242.7616272 , 413.98126221], 0.75765818,  True, False, 'forelegR4'),\\n\",\n              \"                      ([286.15957642, 460.17428589], 0.58625907,  True, False, 'midlegL4'),\\n\",\n              \"                      ([272.72576904, 407.01220703], 0.75733137,  True, False, 'midlegR4'),\\n\",\n              \"                      ([         nan,          nan], 0.        , False, False, 'hindlegL4'),\\n\",\n              \"                      ([318.04064941, 430.85784912], 0.26891908,  True, False, 'hindlegR4'),\\n\",\n              \"                      ([242.54898071, 442.32696533], 0.93467677,  True, False, 'eyeL'),\\n\",\n              \"                      ([245.78987122, 422.02001953], 0.97268051,  True, False, 'eyeR')],\\n\",\n              \"                     dtype=[('xy', '\"\n            ]\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        }\n      ],\n      \"source\": [\n        \"plot(labels[0], scale=0.5)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": []\n    }\n  ],\n  \"metadata\": {\n    \"accelerator\": \"GPU\",\n    \"colab\": {\n      \"collapsed_sections\": [],\n      \"name\": \"SLEAP - Data structures.ipynb\",\n      \"provenance\": []\n    },\n    \"kernelspec\": {\n      \"display_name\": \".venv\",\n      \"language\": \"python\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.13.7\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "docs/notebooks/Interactive_and_resumable_training.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"colab_type\": \"text\",\n        \"id\": \"view-in-github\"\n      },\n      \"source\": [\n        \"\\\"Open\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"DpvQa3M3n7jC\"\n      },\n      \"source\": [\n        \"# Interactive and resumable training\\n\",\n        \"\\n\",\n        \"Most of the time, you will be training models through the GUI or using the [`sleap-nn train` CLI](https://nn.sleap.ai/latest/guides/training/).\\n\",\n        \"\\n\",\n        \"If you'd like to customize the training process, however, you can use sleap-nn's low-level training functionality interactively. This allows you to define scripts that train models according to your own workflow, for example, to **resume training** on an already trained model. Another possible application would be to train a model using **transfer learning**, where a pretrained model can be used to initialize the weights of the new model.\\n\",\n        \"\\n\",\n        \"In this notebook we will explore how to set up a training job and train a model for multiple rounds without the GUI or CLI.\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"BeeqrLbdupmE\"\n      },\n      \"source\": [\n        \"## 1. Setup\\n\",\n        \"\\n\",\n        \"Run this cell first to install `sleap-nn`. If you get a dependency error in subsequent cells, just click **Runtime** ā†’ **Restart runtime** to reload the packages.\\n\",\n        \"\\n\",\n        \"Don't forget to set **Runtime** ā†’ **Change runtime type** ā†’ **GPU** as the accelerator.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"BYxJ2rJOMW8B\",\n        \"outputId\": \"d2230650-4e45-46f3-ff8f-dbe271bb9eb9\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"zsh:1: command not found: pip\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"!pip install -qqq \\\"sleap-nn[torch-cpu]\\\"\\n\",\n        \"\\n\",\n        \"# if you have GPU (in colab, enable GPU runtime)\\n\",\n        \"# !pip install -qqq \\\"sleap-nn[torch-cuda128]\\\"\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"qjfoeOZvpV8o\"\n      },\n      \"source\": [\n        \"Import SLEAP to make sure it installed correctly and print out some information about the system:\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 1,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"jftAOyvvuQeh\",\n        \"outputId\": \"f62974d2-51e7-47d8-defb-ab6f970c995f\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"'0.0.1'\"\n            ]\n          },\n          \"execution_count\": 1,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"import sleap_nn\\n\",\n        \"\\n\",\n        \"sleap_nn.__version__\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"wSdTJYOdu4L6\"\n      },\n      \"source\": [\n        \"## 2. Setup training data\\n\",\n        \"\\n\",\n        \"Here we will download an existing training dataset package. This is an `.slp` file that contains both the labeled poses, as well as the image data for labeled frames.\\n\",\n        \"\\n\",\n        \"If running on Google Colab, you'll want to replace this with mounting your Google Drive folder containing your own data, or if running locally, simply change the path to your labels below in `TRAINING_SLP_FILE`.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 3,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"sDIF3RKdM86u\",\n        \"outputId\": \"9c267834-935c-4f90-bb77-c0f15814ba2a\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\\n\",\n            \"                                 Dload  Upload   Total   Spent    Left  Speed\\n\",\n            \"100  619M  100  619M    0     0  52.1M      0  0:00:11  0:00:11 --:--:-- 57.3M.9M\\n\",\n            \"total 1283584\\n\",\n            \"drwxr-xr-x@ 14 divyasesh  staff   448B Sep 24 19:47 \\u001b[34m.\\u001b[m\\u001b[m\\n\",\n            \"drwxr-xr-x@ 15 divyasesh  staff   480B Sep 24 17:10 \\u001b[34m..\\u001b[m\\u001b[m\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff   713K Sep 22 10:30 Analysis_examples.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff   462K Sep 22 12:05 Data_structures.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff   175K Sep 22 12:07 Interactive_and_realtime_inference.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff    62K Sep 24 19:47 Interactive_and_resumable_training.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff   159K Sep 23 16:54 Model_evaluation.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff   127K Sep 22 12:06 Post_inference_tracking.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff   471K Sep 19 19:36 SLEAP_Tutorial_at_Cosyne_2024_Using_exported_data.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff    95K Sep 24 00:13 Training_and_inference_on_an_example_dataset.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff    12K Sep 23 12:19 Training_and_inference_using_Google_Drive.ipynb\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff   619M Sep 24 19:47 labels.pkg.slp\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff   3.4K Sep 22 11:01 notebooks-overview.md\\n\",\n            \"-rw-r--r--@  1 divyasesh  staff    16K Sep 19 19:36 sleap_io_idtracker_IDs.ipynb\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"# !curl -L --output labels.pkg.slp https://www.dropbox.com/s/b990gxjt3d3j3jh/210205.sleap_wt_gold.13pt.pkg.slp?dl=1\\n\",\n        \"!curl -L --output labels.pkg.slp https://storage.googleapis.com/sleap-data/datasets/wt_gold.13pt/tracking_split2/train.pkg.slp\\n\",\n        \"!ls -lah\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 2,\n      \"metadata\": {\n        \"id\": \"vbpBugZRp_S7\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"TRAINING_SLP_FILE = \\\"labels.pkg.slp\\\"\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"-vYsPusvviiu\"\n      },\n      \"source\": [\n        \"## 3. Setup training job\\n\",\n        \"\\n\",\n        \"A sleap-nn `TrainingJobConfig` is a structure that contains all of the hyperparameters needed to train a SLEAP model. This is typically saved out to `initial_config.yaml` and `training_config.yaml` in the model folder so that training runs can be reproduced if needed, as well as to store metadata necessary for inference.\\n\",\n        \"\\n\",\n        \"Normally, these are generated interactively by the GUI, or manually by editing an existing YAML file in a text editor. Here, we will define a configuration interactively entirely in Python.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 3,\n      \"metadata\": {\n        \"id\": \"Cqt1Bhp-OIsi\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"{'data_config': {'train_labels_path': ['labels.pkg.slp'], 'val_labels_path': None, 'validation_fraction': 0.1, 'test_file_path': None, 'provider': 'LabelsReader', 'user_instances_only': True, 'data_pipeline_fw': 'torch_dataset', 'cache_img_path': None, 'use_existing_imgs': False, 'delete_cache_imgs_after_training': True, 'preprocessing': {'ensure_rgb': False, 'ensure_grayscale': False, 'max_height': None, 'max_width': None, 'scale': 1.0, 'crop_size': None, 'min_crop_size': 100}, 'use_augmentations_train': False, 'augmentation_config': {'intensity': None, 'geometric': {'rotation_min': -15.0, 'rotation_max': 15.0, 'scale_min': 0.9, 'scale_max': 1.1, 'translate_width': 0.0, 'translate_height': 0.0, 'affine_p': 1.0, 'erase_scale_min': 0.0001, 'erase_scale_max': 0.01, 'erase_ratio_min': 1.0, 'erase_ratio_max': 1.0, 'erase_p': 0.0, 'mixup_lambda_min': 0.01, 'mixup_lambda_max': 0.05, 'mixup_p': 0.0}}, 'skeletons': None}, 'model_config': {'init_weights': 'default', 'pretrained_backbone_weights': None, 'pretrained_head_weights': None, 'backbone_config': {'unet': {'in_channels': 1, 'kernel_size': 3, 'filters': 16, 'filters_rate': 1.5, 'max_stride': 16, 'stem_stride': None, 'middle_block': True, 'up_interpolate': True, 'stacks': 1, 'convs_per_block': 2, 'output_stride': 4}, 'convnext': None, 'swint': None}, 'head_configs': {'single_instance': None, 'centroid': None, 'centered_instance': {'confmaps': {'part_names': None, 'anchor_part': 'thorax', 'sigma': 1.5, 'output_stride': 4, 'loss_weight': 1.0}}, 'bottomup': None, 'multi_class_bottomup': None, 'multi_class_topdown': None}, 'total_params': None}, 'trainer_config': {'train_data_loader': {'batch_size': 4, 'shuffle': False, 'num_workers': 0}, 'val_data_loader': {'batch_size': 4, 'shuffle': False, 'num_workers': 0}, 'model_ckpt': {'save_top_k': 1, 'save_last': None}, 'trainer_devices': None, 'trainer_device_indices': None, 'trainer_accelerator': 'auto', 'profiler': None, 'trainer_strategy': 'auto', 'enable_progress_bar': True, 'min_train_steps_per_epoch': 200, 'train_steps_per_epoch': None, 'visualize_preds_during_training': False, 'keep_viz': False, 'max_epochs': 5, 'seed': None, 'use_wandb': False, 'save_ckpt': True, 'ckpt_dir': '.', 'run_name': 'baseline_model.topdown_centered_instance', 'resume_ckpt_path': None, 'wandb': {'entity': None, 'project': None, 'name': None, 'save_viz_imgs_wandb': False, 'api_key': None, 'wandb_mode': None, 'prv_runid': None, 'group': None, 'current_run_id': None}, 'optimizer_name': 'Adam', 'optimizer': {'lr': 0.001, 'amsgrad': False}, 'lr_scheduler': None, 'early_stopping': {'min_delta': 0.0, 'patience': 1, 'stop_training_on_plateau': False}, 'online_hard_keypoint_mining': {'online_mining': False, 'hard_to_easy_ratio': 2.0, 'min_hard_keypoints': 2, 'max_hard_keypoints': None, 'loss_scale': 5.0}, 'zmq': {'controller_port': None, 'controller_polling_timeout': 10, 'publish_port': None}}, 'name': '', 'description': '', 'sleap_nn_version': '0.0.1', 'filename': ''}\"\n            ]\n          },\n          \"execution_count\": 3,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"from sleap_nn.config.training_job_config import TrainingJobConfig, verify_training_cfg\\n\",\n        \"from sleap_nn.config.model_config import UNetConfig, CenteredInstanceConfMapsConfig, CenteredInstanceConfig\\n\",\n        \"from sleap_nn.config.data_config import AugmentationConfig, GeometricConfig\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"# Initialize the default training job configuration.\\n\",\n        \"cfg = TrainingJobConfig()\\n\",\n        \"\\n\",\n        \"# Update path to training data we just downloaded.\\n\",\n        \"cfg.data_config.train_labels_path = [TRAINING_SLP_FILE]\\n\",\n        \"cfg.data_config.validation_fraction = 0.1\\n\",\n        \"\\n\",\n        \"# These configures the actual neural network and the model type:\\n\",\n        \"cfg.model_config.backbone_config.unet = UNetConfig(\\n\",\n        \"    filters=16,\\n\",\n        \"    output_stride=4\\n\",\n        \")\\n\",\n        \"centered_head = CenteredInstanceConfig(confmaps=CenteredInstanceConfMapsConfig(anchor_part=\\\"thorax\\\", sigma=1.5, output_stride=4))\\n\",\n        \"cfg.model_config.head_configs.centered_instance = centered_head\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"# Preprocesssing and training parameters.\\n\",\n        \"cfg.data_config.augmentation_config = AugmentationConfig(geometric=GeometricConfig(affine_p=1.0))\\n\",\n        \"cfg.trainer_config.max_epochs = 5  # This is the maximum number of training rounds.\\n\",\n        \"cfg.trainer_config.train_data_loader.batch_size = 4\\n\",\n        \"cfg.trainer_config.val_data_loader.batch_size = 4\\n\",\n        \"\\n\",\n        \"# Setup how we want to save the trained model.\\n\",\n        \"cfg.trainer_config.save_ckpt = True\\n\",\n        \"cfg.trainer_config.run_name = \\\"baseline_model.topdown_centered_instance\\\"\\n\",\n        \"\\n\",\n        \"# verify config structure\\n\",\n        \"cfg = verify_training_cfg(cfg)\\n\",\n        \"cfg\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"9qSU7BcKv4Gw\"\n      },\n      \"source\": [\n        \"Existing configs can also be loaded from a `.yaml` file with:\\n\",\n        \"\\n\",\n        \"```python\\n\",\n        \"from omegaconf import OmegaConf\\n\",\n        \"cfg = OmegaConf.load(\\\"training_config.yaml\\\")\\n\",\n        \"```\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"Noq-XINMv8nz\"\n      },\n      \"source\": [\n        \"## 4. Training\\n\",\n        \"Next we will create a SLEAP `Trainer` from the configuration we just specified. This handles all the nitty gritty mechanics necessary to setup training in the backend.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 4,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"enbK9O5Dv8Pd\",\n        \"outputId\": \"0e36a6e2-a7e8-4d0f-e1d3-0d1b7abaf490\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"2025-09-24 20:02:09 | INFO | sleap_nn.training.model_trainer:_setup_train_val_labels:216 | Creating train-val split...\\n\",\n            \"2025-09-24 20:02:09 | INFO | sleap_nn.training.model_trainer:_setup_train_val_labels:261 | # Train Labeled frames: 1440\\n\",\n            \"2025-09-24 20:02:09 | INFO | sleap_nn.training.model_trainer:_setup_train_val_labels:262 | # Val Labeled frames: 160\\n\",\n            \"2025-09-24 20:02:09 | INFO | sleap_nn.training.model_trainer:setup_config:512 | Setting up config...\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"from sleap_nn.training.model_trainer import ModelTrainer\\n\",\n        \"\\n\",\n        \"trainer = ModelTrainer.get_model_trainer_from_config(cfg)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"JwMTtOrmwUM9\"\n      },\n      \"source\": [\n        \"Great, now we're ready to do the first round of training. This is when the model will actually start to improve over time:\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 5,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 1000,\n          \"referenced_widgets\": [\n            \"6b2a262ed72e4c659969f996ac889aa7\",\n            \"b30cb8d1b5bc4e12b554794098bd2c46\",\n            \"973660ab9cb2472786b368a18db11c63\",\n            \"cdb03dbf1b804f0b8b9bfd738c5eb2ad\"\n          ]\n        },\n        \"id\": \"L8jNydTEwNA1\",\n        \"outputId\": \"51828b8c-6d8b-4743-e9d2-9153f5b571c3\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stderr\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"GPU available: True (mps), used: True\\n\",\n            \"TPU available: False, using: 0 TPU cores\\n\",\n            \"HPU available: False, using: 0 HPUs\\n\",\n            \"/Users/divyasesh/Desktop/talmolab/sleap-core-docs/.venv/lib/python3.13/site-packages/torch/utils/data/dataloader.py:684: UserWarning: 'pin_memory' argument is set as true but not supported on MPS now, then device pinned memory won't be used.\\n\",\n            \"  warnings.warn(warn_msg)\\n\",\n            \"/Users/divyasesh/Desktop/talmolab/sleap-core-docs/.venv/lib/python3.13/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:751: Checkpoint directory /Users/divyasesh/Desktop/talmolab/sleap-core-docs/docs/notebooks/baseline_model.topdown_centered_instance exists and is not empty.\\n\",\n            \"\\n\",\n            \"  | Name                     | Type              | Params | Mode \\n\",\n            \"-----------------------------------------------------------------------\\n\",\n            \"0 | model                    | Model             | 306 K  | train\\n\",\n            \"1 | instance_peaks_inf_layer | FindInstancePeaks | 0      | train\\n\",\n            \"-----------------------------------------------------------------------\\n\",\n            \"306 K     Trainable params\\n\",\n            \"0         Non-trainable params\\n\",\n            \"306 K     Total params\\n\",\n            \"1.226     Total estimated model params size (MB)\\n\",\n            \"66        Modules in train mode\\n\",\n            \"0         Modules in eval mode\\n\"\n          ]\n        },\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:849 | Setting up for training...\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:_setup_model_ckpt_dir:575 | Setting up model ckpt dir: `baseline_model.topdown_centered_instance`...\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:868 | Setting up Trainer...\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:_setup_loggers_callbacks:647 | Setting up callbacks and loggers...\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:897 | Trainer devices: auto\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:950 | Training on 1 device(s)\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:951 | Training on mps:0 accelerator\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:955 | Setting up lightning module for centered_instance model...\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:959 | Backbone model: UNet(\\n\",\n            \"  (encoders): ModuleList(\\n\",\n            \"    (0): Encoder(\\n\",\n            \"      (encoder_stack): ModuleList(\\n\",\n            \"        (0): SimpleConvBlock(\\n\",\n            \"          (blocks): Sequential(\\n\",\n            \"            (stack0_enc0_conv0): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_enc0_act0_relu): ReLU()\\n\",\n            \"            (stack0_enc0_conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_enc0_act1_relu): ReLU()\\n\",\n            \"          )\\n\",\n            \"        )\\n\",\n            \"        (1): SimpleConvBlock(\\n\",\n            \"          (blocks): Sequential(\\n\",\n            \"            (stack0_enc1_pool): MaxPool2dWithSamePadding(kernel_size=2, stride=2, padding=same, dilation=1, ceil_mode=False)\\n\",\n            \"            (stack0_enc1_conv0): Conv2d(16, 24, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_enc1_act0_relu): ReLU()\\n\",\n            \"            (stack0_enc1_conv1): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_enc1_act1_relu): ReLU()\\n\",\n            \"          )\\n\",\n            \"        )\\n\",\n            \"        (2): SimpleConvBlock(\\n\",\n            \"          (blocks): Sequential(\\n\",\n            \"            (stack0_enc2_pool): MaxPool2dWithSamePadding(kernel_size=2, stride=2, padding=same, dilation=1, ceil_mode=False)\\n\",\n            \"            (stack0_enc2_conv0): Conv2d(24, 36, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_enc2_act0_relu): ReLU()\\n\",\n            \"            (stack0_enc2_conv1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_enc2_act1_relu): ReLU()\\n\",\n            \"          )\\n\",\n            \"        )\\n\",\n            \"        (3): SimpleConvBlock(\\n\",\n            \"          (blocks): Sequential(\\n\",\n            \"            (stack0_enc3_pool): MaxPool2dWithSamePadding(kernel_size=2, stride=2, padding=same, dilation=1, ceil_mode=False)\\n\",\n            \"            (stack0_enc3_conv0): Conv2d(36, 54, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_enc3_act0_relu): ReLU()\\n\",\n            \"            (stack0_enc3_conv1): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_enc3_act1_relu): ReLU()\\n\",\n            \"          )\\n\",\n            \"        )\\n\",\n            \"        (4): Sequential(\\n\",\n            \"          (stack0_enc4_last_pool): MaxPool2dWithSamePadding(kernel_size=2, stride=2, padding=same, dilation=1, ceil_mode=False)\\n\",\n            \"        )\\n\",\n            \"      )\\n\",\n            \"    )\\n\",\n            \"  )\\n\",\n            \"  (decoders): ModuleList(\\n\",\n            \"    (0): Decoder(\\n\",\n            \"      (decoder_stack): ModuleList(\\n\",\n            \"        (0): SimpleUpsamplingBlock(\\n\",\n            \"          (blocks): Sequential(\\n\",\n            \"            (stack0_dec0_s16_to_s8_interp_bilinear): Upsample(scale_factor=2.0, mode='bilinear')\\n\",\n            \"            (stack0_dec0_s16_to_s8_refine_conv0): Conv2d(135, 54, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_dec0_s16_to_s8_refine_conv0_act_relu): ReLU()\\n\",\n            \"            (stack0_dec0_s16_to_s8_refine_conv1): Conv2d(54, 54, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_dec0_s16_to_s8_refine_conv1_act_relu): ReLU()\\n\",\n            \"          )\\n\",\n            \"        )\\n\",\n            \"        (1): SimpleUpsamplingBlock(\\n\",\n            \"          (blocks): Sequential(\\n\",\n            \"            (stack0_dec1_s8_to_s4_interp_bilinear): Upsample(scale_factor=2.0, mode='bilinear')\\n\",\n            \"            (stack0_dec1_s8_to_s4_refine_conv0): Conv2d(90, 36, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_dec1_s8_to_s4_refine_conv0_act_relu): ReLU()\\n\",\n            \"            (stack0_dec1_s8_to_s4_refine_conv1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"            (stack0_dec1_s8_to_s4_refine_conv1_act_relu): ReLU()\\n\",\n            \"          )\\n\",\n            \"        )\\n\",\n            \"      )\\n\",\n            \"    )\\n\",\n            \"  )\\n\",\n            \"  (middle_blocks): ModuleList(\\n\",\n            \"    (0): SimpleConvBlock(\\n\",\n            \"      (blocks): Sequential(\\n\",\n            \"        (stack0_enc5_middle_expand_conv0): Conv2d(54, 81, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"        (stack0_enc5_middle_expand_act0_relu): ReLU()\\n\",\n            \"      )\\n\",\n            \"    )\\n\",\n            \"    (1): SimpleConvBlock(\\n\",\n            \"      (blocks): Sequential(\\n\",\n            \"        (stack0_enc6_middle_contract_conv0): Conv2d(81, 81, kernel_size=(3, 3), stride=(1, 1), padding=same)\\n\",\n            \"        (stack0_enc6_middle_contract_act0_relu): ReLU()\\n\",\n            \"      )\\n\",\n            \"    )\\n\",\n            \"  )\\n\",\n            \")\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:960 | Head model: ModuleList(\\n\",\n            \"  (0): Sequential(\\n\",\n            \"    (CenteredInstanceConfmapsHead): Sequential(\\n\",\n            \"      (0): Conv2d(36, 13, kernel_size=(1, 1), stride=(1, 1), padding=same)\\n\",\n            \"      (1): Identity()\\n\",\n            \"    )\\n\",\n            \"  )\\n\",\n            \")\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:962 | Total model parameters: 306444\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:967 | Input image shape: torch.Size([1, 1, 144, 144])\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:1021 | Finished trainer set up. [0.1s]\\n\",\n            \"2025-09-24 20:02:18 | INFO | sleap_nn.training.model_trainer:train:1024 | Starting training loop...\\n\"\n          ]\n        },\n        {\n          \"data\": {\n            \"application/vnd.jupyter.widget-view+json\": {\n              \"model_id\": \"71c589237aeb4f058fce156c92881ac1\",\n              \"version_major\": 2,\n              \"version_minor\": 0\n            },\n            \"text/plain\": [\n              \"Sanity Checking: |          | 0/? [00:00\\\"Open\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"WxEsSjtzA_Af\"\n   },\n   \"source\": [\n    \"# Model evaluation\\n\",\n    \"\\n\",\n    \"After you've trained several models, you may want to generate some accuracy metrics to compare between them. This notebook demonstrates how you'll do that given a trained model.\\n\",\n    \"\\n\",\n    \"Let's start by installing `sleap-nn` and downloading the trained model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"id\": \"5bNDjxe1BZXV\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"zsh:1: command not found: pip\\n\",\n      \"zsh:1: command not found: pip\\n\",\n      \"zsh:1: command not found: apt\\n\",\n      \"Downloading...\\n\",\n      \"From: https://drive.google.com/uc?id=1hk0KzF3jJNPESXo82tGmOUGvpWofQJcQ\\n\",\n      \"To: /Users/divyasesh/Desktop/talmolab/sleap/docs/notebooks/td_fast.centered_instance.n=1800.zip\\n\",\n      \"100%|ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ| 23.0M/23.0M [00:00<00:00, 37.2MB/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# This should take care of all the torch dependencies on colab:\\n\",\n    \"!pip install -qqq \\\"sleap-nn[torch-cpu]\\\"\\n\",\n    \"!pip install gdown\\n\",\n    \"\\n\",\n    \"!apt -qq install tree\\n\",\n    \"!gdown --fuzzy \\\"https://drive.google.com/file/d/1hk0KzF3jJNPESXo82tGmOUGvpWofQJcQ/view?usp=drive_link\\\"\\n\",\n    \"!unzip -qq -o -d \\\".\\\" \\\"td_fast.centered_instance.n=1800.zip\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"g6KIMZjwTEmu\"\n   },\n   \"source\": [\n    \"A trained SLEAP model will be a folder containing files that specify metadata that is useful for evaluation and analysis. The exact set of files may depend on the configuration, but all models will come with:\\n\",\n    \"\\n\",\n    \"- `train_0_pred_metrics.npz`: Metrics for the training split (since we allow multiple slp files for training, `0` indicates the index of the labels file).\\n\",\n    \"- `val_0_pred_metrics.npz`: Metrics for the validation split. This is what you'll want to use most of the time since it wasn't directly used for optimizing the model.\\n\",\n    \"\\n\",\n    \"**Note:** A test split will also be evaluated if it was provided during training and saved to `test_pred_metrics.npz`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"dTgeUbf8AT0P\",\n    \"outputId\": \"4f03ee9a-5297-4d53-cb8e-20a78e7c7285\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[01;34mtd_fast.centered_instance.n=1800\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mbest.ckpt\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32minitial_config.yaml\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mlabels_train_gt_0.slp\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mlabels_val_gt_0.slp\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mpred_test.slp\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mpred_train_0.slp\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mpred_val_0.slp\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mtest_pred_metrics.npz\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mtrain_0_pred_metrics.npz\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mtraining_config.yaml\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;32mtraining_log.csv\\u001b[0m\\n\",\n      \"ā””ā”€ā”€ \\u001b[01;32mval_0_pred_metrics.npz\\u001b[0m\\n\",\n      \"\\n\",\n      \"1 directory, 12 files\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!tree td_fast.centered_instance.n=1800\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"ywbriY3nUnHk\"\n   },\n   \"source\": [\n    \"Additionally, the following files are included and may also be useful:\\n\",\n    \"- `best.ckpt`: The actual saved model and weights. This can be loaded with `torch.load()` but it is recommended to use `run_inference()` function directly as it takes care of adding some additional inference-only procedures.\\n\",\n    \"- `training_config.yaml`: The configuration for the model training job, including metadata inferred during the training procedure. It can be loaded with `OmegaConf.load()`.\\n\",\n    \"- `labels_train_gt_0.slp` and `pred_train_0.slp`: These are SLEAP labels files containing the ground truth and predicted points for the training split. They do not contain the images, but can be used to retrieve the poses used.\\n\",\n    \"- `labels_val_gt_0.slp` and `pred_val_0.slp`: These are SLEAP labels files containing the ground truth and predicted points for the validation split. They do not contain the images, but can be used to retrieve the poses used.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"_FLiavFrBhCK\",\n    \"outputId\": \"fedb9d7b-6dcc-4048-d030-eba38a006086\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib as mpl\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sns\\n\",\n    \"from sleap_nn.evaluation import load_metrics, Evaluator\\n\",\n    \"from pathlib import Path\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"r0TLPw8aU6Rh\"\n   },\n   \"source\": [\n    \"SLEAP metrics can be loaded using the `sleap.load_metrics()` API:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"2o8ncdDYUy94\",\n    \"outputId\": \"6a8a49f0-1252-438b-a2c3-99160fa4bbc4\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Help on function load_metrics in module sleap_nn.evaluation:\\n\",\n      \"\\n\",\n      \"load_metrics(model_path: str, split='val')\\n\",\n      \"    Load the metrics for a given model and split.\\n\",\n      \"\\n\",\n      \"    Args:\\n\",\n      \"        model_path: Path to a model folder or metrics file (.npz).\\n\",\n      \"        split: Name of the split to load the metrics for. Must be `\\\"train\\\"`, `\\\"val\\\"` or\\n\",\n      \"            `\\\"test\\\"` (default: `\\\"val\\\"`). Ignored if a path to a metrics NPZ file is\\n\",\n      \"            provided.\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"help(load_metrics)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"wG1YbA1-SxGN\"\n   },\n   \"source\": \"To know more about the metrics and how they are computed, check out the documentation on [Evaluation Metrics](https://nn.sleap.ai/latest/reference/evaluation_metrics/).\\n\\nLoading the metrics for the validation split of the model we can see all of the available keys:\"\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"hQHHjfUHMerr\",\n    \"outputId\": \"4f1b2c2a-f86d-47c7-979c-ad70f7a14f61\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"voc_metrics\\n\",\n      \"mOKS\\n\",\n      \"distance_metrics\\n\",\n      \"pck_metrics\\n\",\n      \"visibility_metrics\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import sleap_nn\\n\",\n    \"\\n\",\n    \"metrics = sleap_nn.evaluation.load_metrics(\\\"td_fast.centered_instance.n=1800/train_0_pred_metrics.npz\\\")\\n\",\n    \"print(\\\"\\\\n\\\".join(metrics.keys()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"ROcOrWX2VB1E\"\n   },\n   \"source\": [\n    \"To start, let's look at the summary of the **localization errors**:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"EchwGMNcNBoN\",\n    \"outputId\": \"dae62b47-cddb-4605-91c2-c22c899839b6\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Error distance (50%): 0.7267759160979462\\n\",\n      \"Error distance (90%): 1.7100045784176336\\n\",\n      \"Error distance (95%): 2.273841080126975\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Error distance (50%):\\\", metrics[\\\"distance_metrics\\\"][\\\"p50\\\"])\\n\",\n    \"print(\\\"Error distance (90%):\\\", metrics[\\\"distance_metrics\\\"][\\\"p90\\\"])\\n\",\n    \"print(\\\"Error distance (95%):\\\", metrics[\\\"distance_metrics\\\"][\\\"p95\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"o3c91HExVIdx\"\n   },\n   \"source\": [\n    \"These are the percentiles of the distribution of how far off the model was from the ground truth location.\\n\",\n    \"\\n\",\n    \"We can visualize the entire distribution like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 467\n    },\n    \"id\": \"vo3qyz5nNfgf\",\n    \"outputId\": \"59d0c939-53a3-4580-cf0b-be85b58ad067\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(6, 3), dpi=150, facecolor=\\\"w\\\")\\n\",\n    \"sns.histplot(metrics[\\\"distance_metrics\\\"][\\\"dists\\\"].flatten(), binrange=(0, 20), kde=True, kde_kws={\\\"clip\\\": (0, 20)}, stat=\\\"probability\\\")\\n\",\n    \"plt.xlabel(\\\"Localization error (px)\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"LbrwS1iAVStb\"\n   },\n   \"source\": [\n    \"This metric is intuitive, but it does not incorporate other sources of error like those stemming from poor instance detection and grouping, or missing points.\\n\",\n    \"\\n\",\n    \"The Object Keypoint Similarity (OKS) is a more holistic metric that takes factors such as landmark visibility, animal size, and the difficulty in locating keypoints (all are assumed to be \\\"easy\\\" for our calculations). You can read more about this and other pose estimation metrics in: https://arxiv.org/abs/1707.05388\\n\",\n    \"\\n\",\n    \"First let's plot the distribution of OKS scores:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(6, 3), dpi=150, facecolor=\\\"w\\\")\\n\",\n    \"sns.histplot(metrics[\\\"voc_metrics\\\"][\\\"oks_voc.match_scores\\\"].flatten(), binrange=(0, 1), kde=True, kde_kws={\\\"clip\\\": (0, 1)}, stat=\\\"probability\\\")\\n\",\n    \"plt.xlabel(\\\"Object Keypoint Similarity\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"yj84sWSLWGTl\"\n   },\n   \"source\": [\n    \"Since these range from 0 to 1, it seems like we're doing pretty well!\\n\",\n    \"\\n\",\n    \"Another way to summarize this is through precision-recall curves, which evaluate how well the model does at different thresholds of OKS scores. The higher the threshold, the more stringent our criteria for classifying a prediction as correct.\\n\",\n    \"\\n\",\n    \"Here we plot this at different thresholds:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 562\n    },\n    \"id\": \"4WEhNB93O7Ns\",\n    \"outputId\": \"56001b09-2f5e-4e4f-cea5-2f66d6d9a2fd\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"
\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(4, 4), dpi=150, facecolor=\\\"w\\\")\\n\",\n    \"for precision, thresh in zip(metrics[\\\"voc_metrics\\\"]['oks_voc.precisions'][::2], metrics[\\\"voc_metrics\\\"][\\\"oks_voc.match_score_thresholds\\\"][::2]):\\n\",\n    \"    plt.plot(metrics[\\\"voc_metrics\\\"][\\\"oks_voc.recall_thresholds\\\"], precision, \\\"-\\\", label=f\\\"OKS @ {thresh:.2f}\\\")\\n\",\n    \"plt.xlabel(\\\"Recall\\\")\\n\",\n    \"plt.ylabel(\\\"Precision\\\")\\n\",\n    \"plt.legend(loc=\\\"lower left\\\");\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"DNgkSGy2WkVW\"\n   },\n   \"source\": [\n    \"An easy way to summarize this analysis is to take the average over all of these thresholds to compute the **mean Average Precision (mAP)** and **mean Average Recall (mAR)** which are widely used in the pose estimation literature.\\n\",\n    \"\\n\",\n    \"Here are those values saved out:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"G3_YhQiFNdu6\",\n    \"outputId\": \"fa4f34ad-00df-48d5-d734-a8cc501e6996\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"mAP: 0.8763579063082116\\n\",\n      \"mAR: 0.9309687499999999\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"mAP:\\\", metrics[\\\"voc_metrics\\\"][\\\"oks_voc.mAP\\\"])\\n\",\n    \"print(\\\"mAR:\\\", metrics[\\\"voc_metrics\\\"][\\\"oks_voc.mAR\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"bfJ5R2HHXENK\"\n   },\n   \"source\": [\n    \"Great, but what if we have some new labels or want to evaluate the model with an updated set of labels for better comparisons with newer models?\\n\",\n    \"\\n\",\n    \"For this, we'll need to generate new predictions.\\n\",\n    \"\\n\",\n    \"First, let's download a new SLEAP labels package file (`.pkg.slp`). This is important since it denotes that the labels contain the images as well which we need for predicting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"id\": \"YHCLd3pkRhGT\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wget -q https://storage.googleapis.com/sleap-data/datasets/wt_gold.13pt/tracking_split2/test.pkg.slp\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"9GIPBudQXsDZ\"\n   },\n   \"source\": [\n    \"Next we can simply load the model, the ground truth (GT) labels, and generate the predictions (~1 min on CPU):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"id\": \"OMXHY-7YRyTB\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from sleap_nn.predict import run_inference\\n\",\n    \"import sleap_io as sio\\n\",\n    \"\\n\",\n    \"labels_gt = sio.load_slp(\\\"test.pkg.slp\\\")\\n\",\n    \"labels_pr = run_inference(data_path=\\\"test.pkg.slp\\\", model_paths=[\\\"td_fast.centered_instance.n=1800\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"vUVPXmZSXx-i\"\n   },\n   \"source\": [\n    \"Generating another set of metrics can then be calculated with the pair of GT and predicted labels:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"0sn0R8tGS4LU\",\n    \"outputId\": \"1de841a5-b885-4b90-f901-ad2913437185\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Error distance (50%): 0.7671373993864394\\n\",\n      \"Error distance (90%): 1.9559974547904944\\n\",\n      \"Error distance (95%): 2.99784457113545\\n\",\n      \"mAP: 0.8297769329605552\\n\",\n      \"mAR: 0.90175\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sleap_nn.evaluation import Evaluator\\n\",\n    \"\\n\",\n    \"evals = Evaluator(labels_gt, labels_pr)\\n\",\n    \"\\n\",\n    \"metrics = evals.evaluate()\\n\",\n    \"\\n\",\n    \"print(\\\"Error distance (50%):\\\", metrics[\\\"distance_metrics\\\"][\\\"p50\\\"])\\n\",\n    \"print(\\\"Error distance (90%):\\\", metrics[\\\"distance_metrics\\\"][\\\"p90\\\"])\\n\",\n    \"print(\\\"Error distance (95%):\\\", metrics[\\\"distance_metrics\\\"][\\\"p95\\\"])\\n\",\n    \"print(\\\"mAP:\\\", metrics[\\\"voc_metrics\\\"][\\\"oks_voc.mAP\\\"])\\n\",\n    \"print(\\\"mAR:\\\", metrics[\\\"voc_metrics\\\"][\\\"oks_voc.mAR\\\"])\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"Model_evaluation.ipynb\",\n   \"provenance\": []\n  },\n  \"kernelspec\": {\n   \"display_name\": \".venv\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.13.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "docs/notebooks/Post_inference_tracking.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"colab_type\": \"text\",\n        \"id\": \"view-in-github\"\n      },\n      \"source\": [\n        \"\\\"Open\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"gQXmUCj9ljP3\"\n      },\n      \"source\": [\n        \"# Post-inference tracking\\n\",\n        \"\\n\",\n        \"In tracking, we associate the poses (that were detected within individual frames) across time such that they belong to the same individual.\\n\",\n        \"\\n\",\n        \"Since we typically do this separately, it is sometimes desirable to tweak the tracking parameters to optimize accuracy without having to re-run inference (e.g., `sleap-track` or `sleap-nn track`).\\n\",\n        \"\\n\",\n        \"In this notebook, we will explore how to re-run the tracking given an existing predictions SLP file.\\n\",\n        \"\\n\",\n        \"**Note:** Tracking does not run on the GPU, so this notebook can be run locally on your computer without the hassle of uploading your data if desired.\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"WL67LNf10hev\"\n      },\n      \"source\": [\n        \"## 1. Setup\\n\",\n        \"\\n\",\n        \"Run this cell first to install required libraries. If you get a dependency error in subsequent cells, just click **Runtime** ā†’ **Restart runtime** to reload the packages.\\n\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"UtfcHSZCDnvS\"\n      },\n      \"source\": [\n        \"### Install\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 7,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"HH0weH9f-T1N\",\n        \"outputId\": \"d6f69d8d-9aed-4793-c346-2ab60f110316\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"zsh:1: command not found: pip\\n\",\n            \"zsh:1: command not found: pip\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"# This should take care of all the dependencies on colab:\\n\",\n        \"!pip install -qqq \\\"sleap-io\\\"\\n\",\n        \"!pip install -qqq \\\"sleap-nn[torch-cpu]\\\"\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"d10pcIu70oLb\"\n      },\n      \"source\": [\n        \"### Test\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 8,\n      \"metadata\": {},\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"'0.5.3'\"\n            ]\n          },\n          \"execution_count\": 8,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"import sleap_io as sio\\n\",\n        \"\\n\",\n        \"sio.__version__\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 9,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"WBGKYmLj9Zc2\",\n        \"outputId\": \"8f044c67-3abe-4b8b-8552-db2b5c756c7c\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"'0.0.1'\"\n            ]\n          },\n          \"execution_count\": 9,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"import sleap_nn\\n\",\n        \"\\n\",\n        \"sleap_nn.__version__\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"hYBojEjY9qyr\"\n      },\n      \"source\": [\n        \"## 2. Setup data\\n\",\n        \"Here we're downloading an existing `.slp` file with predictions and the corresponding `.mp4` video.\\n\",\n        \"\\n\",\n        \"You should replace this with Google Drive mounting if running this on Google Colab, or simply skip it altogether and just set the paths below if running locally.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 5,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"akfAyAo-9cAd\",\n        \"outputId\": \"456bd33c-c1f6-4d57-dc37-a58ef8717472\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"!wget -q -O fly_clip.mp4 \\\"https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/fly_clip.mp4?raw=true\\\"\\n\",\n        \"!wget -q -O predictions.slp \\\"https://github.com/talmolab/sleap-tutorial-uo/blob/main/data/predictions.slp?raw=true\\\"\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 6,\n      \"metadata\": {\n        \"id\": \"gQSc_ZjFnHl9\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"PREDICTIONS_FILE = \\\"predictions.slp\\\"\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"9z5rbej_-_Ea\"\n      },\n      \"source\": [\n        \"## 3. Track\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 11,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"MhHCTkdr-wTz\",\n        \"outputId\": \"2e286994-eb4c-4648-c6b9-ab3e7d0cc605\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"Labels(labeled_frames=1350, videos=1, skeletons=1, tracks=0, suggestions=0, sessions=0)\"\n            ]\n          },\n          \"execution_count\": 11,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"# Load predictions\\n\",\n        \"labels = sio.load_slp(PREDICTIONS_FILE)\\n\",\n        \"\\n\",\n        \"# Here I'm removing the tracks so we just have instances without any tracking applied.\\n\",\n        \"for instance in labels.instances:\\n\",\n        \"    instance.track = None\\n\",\n        \"labels.tracks = []\\n\",\n        \"labels\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"hwFC2WYWBQXe\"\n      },\n      \"source\": [\n        \"Here we create a tracker with the options we want to experiment with. You can [read more about tracking in the documentation](../../guides/tracking-and-proofreading/#tracking-methods) or the parameters in the [`sleap-track` CLI help](../../reference/command-line-interfaces/#inference-and-tracking) or [`sleap-nn track CLI`](https://nn.sleap.ai/latest/inference/#tracking).\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 12,\n      \"metadata\": {\n        \"id\": \"AgDVuL-u9_iv\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"# Create tracker\\n\",\n        \"from sleap_nn.tracking.tracker import Tracker\\n\",\n        \"\\n\",\n        \"tracker = Tracker.from_config(\\n\",\n        \"    #general tracking options\\n\",\n        \"    window_size=3,\\n\",\n        \"    use_flow=True,\\n\",\n        \"\\n\",\n        \"    # matching options\\n\",\n        \"    min_new_track_points=1,\\n\",\n        \"    min_match_points=1,\\n\",\n        \"    features=\\\"keypoints\\\",\\n\",\n        \"    scoring_method=\\\"oks\\\",\\n\",\n        \"    track_matching_method=\\\"greedy\\\",\\n\",\n        \"\\n\",\n        \"    # optical flow options\\n\",\n        \"    of_img_scale=0.5,\\n\",\n        \"    of_window_size=21,\\n\",\n        \"    of_max_levels=3,\\n\",\n        \")\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"EfMhLxWcBqBg\"\n      },\n      \"source\": [\n        \"Next we'll actually run the tracking on each frame. This might take a bit longer when using the `\\\"flow\\\"` method.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 13,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"q-EE7r0pBpfD\",\n        \"outputId\": \"eabfe089-b122-494d-c41e-996b0243ab71\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"Labels(labeled_frames=1350, videos=1, skeletons=1, tracks=2, suggestions=0, sessions=0)\"\n            ]\n          },\n          \"execution_count\": 13,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"tracked_lfs = []\\n\",\n        \"for lf in labels:\\n\",\n        \"    lf.instances = tracker.track(lf.instances, frame_idx=lf.frame_idx, image=lf.image)\\n\",\n        \"    tracked_lfs.append(lf)\\n\",\n        \"tracked_labels = sio.Labels(tracked_lfs, videos=labels.videos, skeletons=labels.skeletons)\\n\",\n        \"tracked_labels\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"OjUvwRzWCJ_G\"\n      },\n      \"source\": [\n        \"## 4. Inspect and save\\n\",\n        \"\\n\",\n        \"Let's see the results and save out the tracked predictions.\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 14,\n      \"metadata\": {},\n      \"outputs\": [],\n      \"source\": [\n        \"import seaborn as sns\\n\",\n        \"from typing import Union, Tuple\\n\",\n        \"import matplotlib.pyplot as plt\\n\",\n        \"import numpy as np\\n\",\n        \"import matplotlib\\n\",\n        \"\\n\",\n        \"def imgfig(\\n\",\n        \"    size: Union[float, Tuple] = 6, dpi: int = 72, scale: float = 1.0\\n\",\n        \") -> matplotlib.figure.Figure:\\n\",\n        \"    \\\"\\\"\\\"Create a tight figure for image plotting.\\n\",\n        \"\\n\",\n        \"    Args:\\n\",\n        \"        size: Scalar or 2-tuple specifying the (width, height) of the figure in inches.\\n\",\n        \"            If scalar, will assume equal width and height.\\n\",\n        \"        dpi: Dots per inch, controlling the resolution of the image.\\n\",\n        \"        scale: Factor to scale the size of the figure by. This is a convenience for\\n\",\n        \"            increasing the size of the plot at the same DPI.\\n\",\n        \"\\n\",\n        \"    Returns:\\n\",\n        \"        A matplotlib.figure.Figure to use for plotting.\\n\",\n        \"    \\\"\\\"\\\"\\n\",\n        \"    if not isinstance(size, (tuple, list)):\\n\",\n        \"        size = (size, size)\\n\",\n        \"    fig = plt.figure(figsize=(scale * size[0], scale * size[1]), dpi=dpi)\\n\",\n        \"    ax = fig.add_axes([0, 0, 1, 1], frameon=False)\\n\",\n        \"    ax.get_xaxis().set_visible(False)\\n\",\n        \"    ax.get_yaxis().set_visible(False)\\n\",\n        \"    plt.autoscale(tight=True)\\n\",\n        \"    ax.set_xticks([])\\n\",\n        \"    ax.set_yticks([])\\n\",\n        \"    ax.grid(False)\\n\",\n        \"    return fig\\n\",\n        \"\\n\",\n        \"def plot_img(\\n\",\n        \"    img: np.ndarray, dpi: int = 72, scale: float = 1.0\\n\",\n        \") -> matplotlib.figure.Figure:\\n\",\n        \"    \\\"\\\"\\\"Plot an image in a tight figure.\\\"\\\"\\\"\\n\",\n        \"    if hasattr(img, \\\"numpy\\\"):\\n\",\n        \"        img = img.numpy()\\n\",\n        \"\\n\",\n        \"    if img.shape[0] == 1:\\n\",\n        \"        # Squeeze out batch singleton dimension.\\n\",\n        \"        img = img.squeeze(axis=0)\\n\",\n        \"\\n\",\n        \"    # Check if image is grayscale (single channel).\\n\",\n        \"    grayscale = img.shape[-1] == 1\\n\",\n        \"    if grayscale:\\n\",\n        \"        # Squeeze out singleton channel.\\n\",\n        \"        img = img.squeeze(axis=-1)\\n\",\n        \"\\n\",\n        \"    # Normalize the range of pixel values.\\n\",\n        \"    img_min = img.min()\\n\",\n        \"    img_max = img.max()\\n\",\n        \"    if img_min < 0.0 or img_max > 1.0:\\n\",\n        \"        img = (img - img_min) / (img_max - img_min)\\n\",\n        \"\\n\",\n        \"    fig = imgfig(\\n\",\n        \"        size=(float(img.shape[1]) / dpi, float(img.shape[0]) / dpi),\\n\",\n        \"        dpi=dpi,\\n\",\n        \"        scale=scale,\\n\",\n        \"    )\\n\",\n        \"\\n\",\n        \"    ax = fig.gca()\\n\",\n        \"    ax.imshow(\\n\",\n        \"        img,\\n\",\n        \"        cmap=\\\"gray\\\" if grayscale else None,\\n\",\n        \"        origin=\\\"upper\\\",\\n\",\n        \"        extent=[-0.5, img.shape[1] - 0.5, img.shape[0] - 0.5, -0.5],\\n\",\n        \"    )\\n\",\n        \"    return fig\\n\",\n        \"\\n\",\n        \"def plot_instance(\\n\",\n        \"    instance,\\n\",\n        \"    skeleton=None,\\n\",\n        \"    cmap=None,\\n\",\n        \"    color_by_node=False,\\n\",\n        \"    lw=2,\\n\",\n        \"    ms=10,\\n\",\n        \"    bbox=None,\\n\",\n        \"    scale=1.0,\\n\",\n        \"    **kwargs,\\n\",\n        \"):\\n\",\n        \"    \\\"\\\"\\\"Plot a single instance with edge coloring.\\\"\\\"\\\"\\n\",\n        \"    if cmap is None:\\n\",\n        \"        cmap = sns.color_palette(\\\"tab20\\\")\\n\",\n        \"\\n\",\n        \"    if skeleton is None and hasattr(instance, \\\"skeleton\\\"):\\n\",\n        \"        skeleton = instance.skeleton\\n\",\n        \"\\n\",\n        \"    if skeleton is None:\\n\",\n        \"        color_by_node = True\\n\",\n        \"    else:\\n\",\n        \"        if len(skeleton.edges) == 0:\\n\",\n        \"            color_by_node = True\\n\",\n        \"\\n\",\n        \"    if hasattr(instance, \\\"numpy\\\"):\\n\",\n        \"        inst_pts = instance.numpy()\\n\",\n        \"    else:\\n\",\n        \"        inst_pts = instance\\n\",\n        \"\\n\",\n        \"    h_lines = []\\n\",\n        \"    if color_by_node:\\n\",\n        \"        for k, (x, y) in enumerate(inst_pts):\\n\",\n        \"            if bbox is not None:\\n\",\n        \"                x -= bbox[1]\\n\",\n        \"                y -= bbox[0]\\n\",\n        \"\\n\",\n        \"            x *= scale\\n\",\n        \"            y *= scale\\n\",\n        \"\\n\",\n        \"            h_lines_k = plt.plot(x, y, \\\".\\\", ms=ms, c=cmap[k % len(cmap)], **kwargs)\\n\",\n        \"            h_lines.append(h_lines_k)\\n\",\n        \"\\n\",\n        \"    else:\\n\",\n        \"        for k, (src_node, dst_node) in enumerate(skeleton.edges):\\n\",\n        \"            pts = instance.numpy()\\n\",\n        \"            src_pt = pts[skeleton.node_names.index(src_node.name)]\\n\",\n        \"            dst_pt = pts[skeleton.node_names.index(dst_node.name)]\\n\",\n        \"\\n\",\n        \"            x = np.array([src_pt[0], dst_pt[0]])\\n\",\n        \"            y = np.array([src_pt[1], dst_pt[1]])\\n\",\n        \"\\n\",\n        \"            if bbox is not None:\\n\",\n        \"                x -= bbox[1]\\n\",\n        \"                y -= bbox[0]\\n\",\n        \"\\n\",\n        \"            x *= scale\\n\",\n        \"            y *= scale\\n\",\n        \"\\n\",\n        \"            h_lines_k = plt.plot(\\n\",\n        \"                x, y, \\\".-\\\", ms=ms, lw=lw, c=cmap[k % len(cmap)], **kwargs\\n\",\n        \"            )\\n\",\n        \"\\n\",\n        \"            h_lines.append(h_lines_k)\\n\",\n        \"\\n\",\n        \"    return h_lines\\n\",\n        \"\\n\",\n        \"def plot_instances(\\n\",\n        \"    instances, skeleton=None, cmap=None, color_by_track=False, tracks=None, **kwargs\\n\",\n        \"):\\n\",\n        \"    \\\"\\\"\\\"Plot a list of instances with identity coloring.\\\"\\\"\\\"\\n\",\n        \"\\n\",\n        \"    if cmap is None:\\n\",\n        \"        cmap = sns.color_palette(\\\"tab10\\\")\\n\",\n        \"\\n\",\n        \"    if color_by_track and tracks is None:\\n\",\n        \"        # Infer tracks for ordering if not provided.\\n\",\n        \"        tracks = set()\\n\",\n        \"        for instance in instances:\\n\",\n        \"            tracks.add(instance.track)\\n\",\n        \"\\n\",\n        \"        # Sort by spawned frame.\\n\",\n        \"        tracks = sorted(list(tracks), key=lambda track: track.name)\\n\",\n        \"\\n\",\n        \"    h_lines = []\\n\",\n        \"    for i, instance in enumerate(instances):\\n\",\n        \"        if color_by_track:\\n\",\n        \"            if instance.track is None:\\n\",\n        \"                raise ValueError(\\n\",\n        \"                    \\\"Instances must have a set track when coloring by track.\\\"\\n\",\n        \"                )\\n\",\n        \"\\n\",\n        \"            if instance.track not in tracks:\\n\",\n        \"                raise ValueError(\\\"Instance has a track not found in specified tracks.\\\")\\n\",\n        \"\\n\",\n        \"            color = cmap[tracks.index(instance.track) % len(cmap)]\\n\",\n        \"\\n\",\n        \"        else:\\n\",\n        \"            # Color by identity (order in list).\\n\",\n        \"            color = cmap[i % len(cmap)]\\n\",\n        \"\\n\",\n        \"        h_lines_i = plot_instance(instance, skeleton=skeleton, cmap=[color], **kwargs)\\n\",\n        \"        h_lines.append(h_lines_i)\\n\",\n        \"\\n\",\n        \"    return h_lines\\n\",\n        \"\\n\",\n        \"def plot(lf, scale=0.5):\\n\",\n        \"    plot_img(lf.image, scale=scale)\\n\",\n        \"    plot_instances(lf.instances)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 15,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 287\n        },\n        \"id\": \"g-ia6hYGCXZX\",\n        \"outputId\": \"2652a6e2-6f63-4b81-dd54-d8a01c6c25a4\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"image/png\": 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\",\n            \"text/plain\": [\n              \"
\"\n            ]\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        }\n      ],\n      \"source\": [\n        \"plot(tracked_labels[0], scale=0.25)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 16,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 287\n        },\n        \"id\": \"nDMnJFmFCszY\",\n        \"outputId\": \"90b984e6-b6bb-468b-eb66-2b0537758c44\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"image/png\": 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\",\n            \"text/plain\": [\n              \"
\"\n            ]\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        }\n      ],\n      \"source\": [\n        \"plot(tracked_labels[100], scale=0.25)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": 17,\n      \"metadata\": {\n        \"id\": \"D3YMi3C0C0uh\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"tracked_labels.save(\\\"retracked.slp\\\")\"\n      ]\n    }\n  ],\n  \"metadata\": {\n    \"colab\": {\n      \"collapsed_sections\": [],\n      \"name\": \"SLEAP - Post-inference tracking.ipynb\",\n      \"provenance\": []\n    },\n    \"kernelspec\": {\n      \"display_name\": \".venv\",\n      \"language\": \"python\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"codemirror_mode\": {\n        \"name\": \"ipython\",\n        \"version\": 3\n      },\n      \"file_extension\": \".py\",\n      \"mimetype\": \"text/x-python\",\n      \"name\": \"python\",\n      \"nbconvert_exporter\": \"python\",\n      \"pygments_lexer\": \"ipython3\",\n      \"version\": \"3.13.7\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 0\n}\n"
  },
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    "path": "docs/notebooks/SLEAP_Tutorial_at_Cosyne_2024_Using_exported_data.ipynb",
    "content": "{\n  \"cells\": [\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"2h863aPAeqip\"\n      },\n      \"source\": [\n        \"# SLEAP Tutorial at Cosyne 2024 - Using exported data\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"BEvDiRR-e0hv\"\n      },\n      \"source\": [\n        \"## Setup\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"XSmZl0fTZ2q6\",\n        \"outputId\": \"fab3b34a-116d-40a4-a5ab-060b33306261\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"Collecting sleap-io\\n\",\n            \"  Downloading sleap_io-0.0.13-py3-none-any.whl (46 kB)\\n\",\n            \"\\u001b[2K     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already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->hdmf<4,>=2.5.6->ndx-pose->sleap-io) (0.18.0)\\n\",\n            \"Collecting ruamel.yaml.clib>=0.2.7 (from ruamel-yaml>=0.16->hdmf<4,>=2.5.6->ndx-pose->sleap-io)\\n\",\n            \"  Downloading ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (526 kB)\\n\",\n            \"\\u001b[2K     \\u001b[90mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\\u001b[0m \\u001b[32m526.7/526.7 kB\\u001b[0m \\u001b[31m29.2 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n            \"\\u001b[?25hInstalling collected packages: simplejson, ruamel.yaml.clib, av, ruamel-yaml, hdmf, pynwb, ndx-pose, sleap-io\\n\",\n            \"Successfully installed av-11.0.0 hdmf-3.12.2 ndx-pose-0.1.1 pynwb-2.6.0 ruamel-yaml-0.18.6 ruamel.yaml.clib-0.2.8 simplejson-3.19.2 sleap-io-0.0.13\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"!pip install sleap-io\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"IC9q0g4aZ8IU\",\n        \"outputId\": \"3edaa0b1-88b3-4b3f-8d8f-85da752fec2c\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"--2024-02-28 12:14:40--  https://github.com/talmolab/cosyne-tutorial-data/raw/main/new_data/results.zip\\n\",\n            \"Resolving github.com (github.com)... 140.82.121.4\\n\",\n            \"Connecting to github.com (github.com)|140.82.121.4|:443... connected.\\n\",\n            \"HTTP request sent, awaiting response... 302 Found\\n\",\n            \"Location: https://raw.githubusercontent.com/talmolab/cosyne-tutorial-data/main/new_data/results.zip [following]\\n\",\n            \"--2024-02-28 12:14:40--  https://raw.githubusercontent.com/talmolab/cosyne-tutorial-data/main/new_data/results.zip\\n\",\n            \"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\\n\",\n            \"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\\n\",\n            \"HTTP request sent, awaiting response... 200 OK\\n\",\n            \"Length: 6024596 (5.7M) [application/zip]\\n\",\n            \"Saving to: ā€˜results.zipā€™\\n\",\n            \"\\n\",\n            \"results.zip         100%[===================>]   5.75M  --.-KB/s    in 0.04s   \\n\",\n            \"\\n\",\n            \"2024-02-28 12:14:40 (151 MB/s) - ā€˜results.zipā€™ saved [6024596/6024596]\\n\",\n            \"\\n\",\n            \"Archive:  results.zip\\n\",\n            \"  inflating: mice_new.mp4            \\n\",\n            \"  inflating: new_video.v002.000_mice_new.analysis.csv  \\n\",\n            \"  inflating: new_video.v002.000_mice_new.analysis.h5  \\n\",\n            \"  inflating: new_video.v002.slp      \\n\",\n            \"  inflating: new_video.v002.slp.nwb  \\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"!wget https://github.com/talmolab/cosyne-tutorial-data/raw/main/new_data/results.zip\\n\",\n        \"!unzip results.zip\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"HdAC_y02bTXc\"\n      },\n      \"source\": [\n        \"## Using CSV\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"421AOcu5bWGz\",\n        \"outputId\": \"c158cdef-d50b-4ae7-a713-920c6a512938\"\n      },\n   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trackframe_idxinstance.scorehead.xhead.yhead.scoretorso.xtorso.ytorso.scoretail_base.xtail_base.ytail_base.score
0track_000.452409566.795776248.9264220.775533477.049164276.1953120.840341416.618591289.0795290.641716
1track_010.423293579.212158252.0988770.524509491.916687268.1333010.864011428.593292252.5682680.502443
2track_110.230167475.953583427.9694211.023815480.214294364.2354130.990409NaNNaN0.000000
3track_020.404776595.261230255.9982600.438605508.075378260.3524780.902620444.067688272.2767640.773855
4track_120.292742476.059387428.0794680.965847480.260010368.2130431.030145492.502716320.7703250.225987
.......................................
5555track_128170.549521604.961792452.1396480.537645556.229675436.3714290.955967520.295044376.6586910.806471
5556track_028180.441821412.508972596.6763310.742932360.331940556.8076780.841093292.633972564.3538210.851277
5557track_128180.565165609.180847451.9963380.756140556.315918436.3302920.970995516.091919376.8085020.851646
5558track_028190.446245411.902161600.4783940.762012360.342468556.9849850.854819292.555084564.5647580.847509
5559track_128190.569532616.554871440.2179570.625704560.256348436.2749630.936956513.046875376.7800290.842597
\\n\",\n              \"
5560 rows Ɨ 12 columns
\\n\",\n              \"
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\\n\",\n              \"    \\n\",\n              \"\\n\",\n              \"  \\n\",\n              \"\\n\",\n              \"    \\n\",\n              \"  
\\n\",\n              \"\\n\",\n              \"\\n\",\n              \"
\\n\",\n              \"  \\n\",\n              \"\\n\",\n              \"\\n\",\n              \"\\n\",\n              \"  \\n\",\n              \"
\\n\",\n              \"    
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\\n\"\n            ],\n            \"text/plain\": [\n              \"        track  frame_idx  instance.score      head.x      head.y  head.score  \\\\\\n\",\n              \"0     track_0          0        0.452409  566.795776  248.926422    0.775533   \\n\",\n              \"1     track_0          1        0.423293  579.212158  252.098877    0.524509   \\n\",\n              \"2     track_1          1        0.230167  475.953583  427.969421    1.023815   \\n\",\n              \"3     track_0          2        0.404776  595.261230  255.998260    0.438605   \\n\",\n              \"4     track_1          2        0.292742  476.059387  428.079468    0.965847   \\n\",\n              \"...       ...        ...             ...         ...         ...         ...   \\n\",\n              \"5555  track_1       2817        0.549521  604.961792  452.139648    0.537645   \\n\",\n              \"5556  track_0       2818        0.441821  412.508972  596.676331    0.742932   \\n\",\n              \"5557  track_1       2818        0.565165  609.180847  451.996338    0.756140   \\n\",\n              \"5558  track_0       2819        0.446245  411.902161  600.478394    0.762012   \\n\",\n              \"5559  track_1       2819        0.569532  616.554871  440.217957    0.625704   \\n\",\n              \"\\n\",\n              \"         torso.x     torso.y  torso.score  tail_base.x  tail_base.y  \\\\\\n\",\n              \"0     477.049164  276.195312     0.840341   416.618591   289.079529   \\n\",\n              \"1     491.916687  268.133301     0.864011   428.593292   252.568268   \\n\",\n              \"2     480.214294  364.235413     0.990409          NaN          NaN   \\n\",\n              \"3     508.075378  260.352478     0.902620   444.067688   272.276764   \\n\",\n              \"4     480.260010  368.213043     1.030145   492.502716   320.770325   \\n\",\n              \"...          ...         ...          ...          ...          ...   \\n\",\n              \"5555  556.229675  436.371429     0.955967   520.295044   376.658691   \\n\",\n              \"5556  360.331940  556.807678     0.841093   292.633972   564.353821   \\n\",\n              \"5557  556.315918  436.330292     0.970995   516.091919   376.808502   \\n\",\n              \"5558  360.342468  556.984985     0.854819   292.555084   564.564758   \\n\",\n              \"5559  560.256348  436.274963     0.936956   513.046875   376.780029   \\n\",\n              \"\\n\",\n              \"      tail_base.score  \\n\",\n              \"0            0.641716  \\n\",\n              \"1            0.502443  \\n\",\n              \"2            0.000000  \\n\",\n              \"3            0.773855  \\n\",\n              \"4            0.225987  \\n\",\n              \"...               ...  \\n\",\n              \"5555         0.806471  \\n\",\n              \"5556         0.851277  \\n\",\n              \"5557         0.851646  \\n\",\n              \"5558         0.847509  \\n\",\n              \"5559         0.842597  \\n\",\n              \"\\n\",\n              \"[5560 rows x 12 columns]\"\n            ]\n          },\n          \"execution_count\": 3,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"import pandas as pd\\n\",\n        \"\\n\",\n        \"df = pd.read_csv(\\\"new_video.v002.000_mice_new.analysis.csv\\\")\\n\",\n        \"df\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"fR1RvQNnfnDK\",\n        \"outputId\": \"439cecd1-9eb8-425f-debc-fcb010f29d8c\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"\"\n            ]\n          },\n          \"execution_count\": 4,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        },\n        {\n          \"data\": {\n            \"image/png\": 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\",\n            \"text/plain\": [\n              \"
\"\n            ]\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        }\n      ],\n      \"source\": [\n        \"import matplotlib.pyplot as plt\\n\",\n        \"import seaborn as sns\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"sns.scatterplot(data=df, x=\\\"head.x\\\", y=\\\"head.y\\\", hue=\\\"track\\\")\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"C3AL5DOIcODk\"\n      },\n      \"source\": [\n        \"## Using HDF5\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"VNitI8jXcPs5\",\n        \"outputId\": \"92a432d9-d76c-4ac8-f4ef-2d6499fbadfa\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"(2820, 2, 3, 2)\"\n            ]\n          },\n          \"execution_count\": 5,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"import h5py\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"with h5py.File(\\\"new_video.v002.000_mice_new.analysis.h5\\\", \\\"r\\\") as f:\\n\",\n        \"    tracks = f[\\\"tracks\\\"][:].transpose(3, 1, 2, 0)\\n\",\n        \"\\n\",\n        \"tracks.shape  # (frames, tracks, nodes, xy)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"5iPq6zJngBBj\",\n        \"outputId\": \"836dfd96-0f30-4701-dead-9c682490e7b7\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stderr\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \":3: RuntimeWarning: Mean of empty slice\\n\",\n            \"  vel = np.nanmean(np.linalg.norm(np.diff(tracks, axis=0), axis=-1), axis=-1)\\n\"\n          ]\n        },\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"vel.shape = (2819, 2)\\n\"\n          ]\n        },\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"Text(0.5, 0, 'Velocity (px/frame)')\"\n            ]\n          },\n          \"execution_count\": 6,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        },\n        {\n          \"data\": {\n            \"image/png\": 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\",\n            \"text/plain\": [\n              \"
\"\n            ]\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        }\n      ],\n      \"source\": [\n        \"import numpy as np\\n\",\n        \"\\n\",\n        \"vel = np.nanmean(np.linalg.norm(np.diff(tracks, axis=0), axis=-1), axis=-1)\\n\",\n        \"print(\\\"vel.shape =\\\", vel.shape)\\n\",\n        \"\\n\",\n        \"sns.histplot(vel, kde=True, log_scale=True)\\n\",\n        \"plt.xlabel(\\\"Velocity (px/frame)\\\")\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"QYkmZN6YdxlE\",\n        \"outputId\": \"b3879391-be3b-4174-eb4b-96a657f22700\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stdout\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"/edge_inds:\\n\",\n            \"    /edge_inds (Dataset)\\n\",\n            \"        Shape: (2, 2)\\n\",\n            \"        Dtype: int64\\n\",\n            \"/edge_names:\\n\",\n            \"    /edge_names (Dataset)\\n\",\n            \"        Shape: (2, 2)\\n\",\n            \"        Dtype: |S9\\n\",\n            \"/instance_scores:\\n\",\n            \"    /instance_scores (Dataset)\\n\",\n            \"        Shape: (2, 2820)\\n\",\n            \"        Dtype: float64\\n\",\n            \"/labels_path:\\n\",\n            \"    /labels_path (Dataset)\\n\",\n            \"        Shape: ()\\n\",\n            \"        Dtype: object\\n\",\n            \"/node_names:\\n\",\n            \"    /node_names (Dataset)\\n\",\n            \"        Shape: (3,)\\n\",\n            \"        Dtype: |S9\\n\",\n            \"/point_scores:\\n\",\n            \"    /point_scores (Dataset)\\n\",\n            \"        Shape: (2, 3, 2820)\\n\",\n            \"        Dtype: float64\\n\",\n            \"/provenance:\\n\",\n            \"    /provenance (Dataset)\\n\",\n            \"        Shape: ()\\n\",\n            \"        Dtype: object\\n\",\n            \"/track_names:\\n\",\n            \"    /track_names (Dataset)\\n\",\n            \"        Shape: (2,)\\n\",\n            \"        Dtype: |S7\\n\",\n            \"/track_occupancy:\\n\",\n            \"    /track_occupancy (Dataset)\\n\",\n            \"        Shape: (2820, 2)\\n\",\n            \"        Dtype: uint8\\n\",\n            \"/tracking_scores:\\n\",\n            \"    /tracking_scores (Dataset)\\n\",\n            \"        Shape: (2, 2820)\\n\",\n            \"        Dtype: float64\\n\",\n            \"/tracks:\\n\",\n            \"    /tracks (Dataset)\\n\",\n            \"        Shape: (2, 2, 3, 2820)\\n\",\n            \"        Dtype: float64\\n\",\n            \"/video_ind:\\n\",\n            \"    /video_ind (Dataset)\\n\",\n            \"        Shape: ()\\n\",\n            \"        Dtype: int64\\n\",\n            \"/video_path:\\n\",\n            \"    /video_path (Dataset)\\n\",\n            \"        Shape: ()\\n\",\n            \"        Dtype: object\\n\"\n          ]\n        }\n      ],\n      \"source\": [\n        \"def print_hdf5_info(obj, indent=0):\\n\",\n        \"    \\\"\\\"\\\"Recursively prints information about datasets and groups in an HDF5 file.\\\"\\\"\\\"\\n\",\n        \"    if isinstance(obj, h5py.File) or isinstance(obj, h5py.Group):\\n\",\n        \"        for key in obj.keys():\\n\",\n        \"            print(' ' * indent + f\\\"{'/' if indent == 0 else ''}{key}:\\\")\\n\",\n        \"            print_hdf5_info(obj[key], indent + 4)\\n\",\n        \"    elif isinstance(obj, h5py.Dataset):\\n\",\n        \"        print(' ' * indent + f\\\"{'/' if indent == 0 else ''}{obj.name} (Dataset)\\\")\\n\",\n        \"        print(' ' * (indent + 4) + f\\\"Shape: {obj.shape}\\\")\\n\",\n        \"        print(' ' * (indent + 4) + f\\\"Dtype: {obj.dtype}\\\")\\n\",\n        \"        if obj.attrs:\\n\",\n        \"            print(' ' * (indent + 4) + \\\"Attributes:\\\")\\n\",\n        \"            for attr_name, attr_value in obj.attrs.items():\\n\",\n        \"                print(' ' * (indent + 8) + f\\\"{attr_name}: {attr_value}\\\")\\n\",\n        \"    else:\\n\",\n        \"        print(' ' * indent + f\\\"Unknown object: {obj}\\\")\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"with h5py.File(\\\"new_video.v002.000_mice_new.analysis.h5\\\", \\\"r\\\") as f:\\n\",\n        \"    print_hdf5_info(f)\"\n      ]\n    },\n    {\n      \"cell_type\": \"markdown\",\n      \"metadata\": {\n        \"id\": \"-6LGyLpmeFuW\"\n      },\n      \"source\": [\n        \"## Using sleap-io\\n\",\n        \"\\n\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"id\": \"yfXjN9QdbKu_\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"import sleap_io as sio\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"1PAwsAsse-IN\",\n        \"outputId\": \"c1aa75d7-9cf3-4d46-bbb8-5ee6e8cff677\"\n      },\n      \"outputs\": [\n        {\n          \"name\": \"stderr\",\n          \"output_type\": \"stream\",\n          \"text\": [\n            \"/usr/local/lib/python3.10/dist-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.6.0 because version 1.8.0 is already loaded.\\n\",\n            \"  return func(args[0], **pargs)\\n\",\n            \"/usr/local/lib/python3.10/dist-packages/hdmf/utils.py:668: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.3.0 because version 0.5.0 is already loaded.\\n\",\n            \"  return func(args[0], **pargs)\\n\"\n          ]\n        },\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"Labels(labeled_frames=2820, videos=1, skeletons=1, tracks=2)\"\n            ]\n          },\n          \"execution_count\": 9,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"predictions = sio.load_nwb(\\\"new_video.v002.slp.nwb\\\")\\n\",\n        \"predictions\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"SyxdIok4fGS3\",\n        \"outputId\": \"36548f63-88a3-4921-929d-05d1a26e403c\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"Labels(labeled_frames=2820, videos=1, skeletons=1, tracks=2)\"\n            ]\n          },\n          \"execution_count\": 10,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"predictions = sio.load_slp(\\\"new_video.v002.slp\\\")\\n\",\n        \"predictions\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"r71l_uz0fLqz\",\n        \"outputId\": \"780c9ed9-6006-4f1b-cc32-df36c32cc8a5\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"(2820, 2, 3, 2)\"\n            ]\n          },\n          \"execution_count\": 11,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"tracks = predictions.numpy()\\n\",\n        \"tracks.shape  # (frames, tracks, nodes, xy)\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\"\n        },\n        \"id\": \"8paby7igfS3s\",\n        \"outputId\": \"5c4c0e82-21fa-4623-fc46-71424a0833c8\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"text/plain\": [\n              \"LabeledFrame(video=Video(filename=\\\"mice_new.mp4\\\", shape=(2820, 768, 1024, 1), backend=MediaVideo), frame_idx=0, instances=[PredictedInstance(points={Node(name='head'): PredictedPoint(x=566.7957763671875, y=248.92642211914062, visible=True, complete=False, score=0.7755330204963684), Node(name='torso'): PredictedPoint(x=477.0491638183594, y=276.1953125, visible=True, complete=False, score=0.8403405547142029), Node(name='tail_base'): PredictedPoint(x=416.61859130859375, y=289.07952880859375, visible=True, complete=False, score=0.6417162418365479)}, skeleton=Skeleton(nodes=[Node(name='head'), Node(name='torso'), Node(name='tail_base')], edges=[Edge(source=Node(name='torso'), destination=Node(name='head')), Edge(source=Node(name='torso'), destination=Node(name='tail_base'))], symmetries=[], name='Skeleton-1'), track=Track(name='track_0'), from_predicted=None, score=0.45240876, tracking_score=0.0)])\"\n            ]\n          },\n          \"execution_count\": 12,\n          \"metadata\": {},\n          \"output_type\": \"execute_result\"\n        }\n      ],\n      \"source\": [\n        \"idx = 0\\n\",\n        \"\\n\",\n        \"lf = predictions[idx]\\n\",\n        \"lf\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"id\": \"FsN4x0y8ffXx\"\n      },\n      \"outputs\": [],\n      \"source\": [\n        \"def imgfig(size = 6, dpi = 72, scale = 1.0):\\n\",\n        \"    \\\"\\\"\\\"Create a tight figure for image plotting.\\n\",\n        \"\\n\",\n        \"    Args:\\n\",\n        \"        size: Scalar or 2-tuple specifying the (width, height) of the figure in inches.\\n\",\n        \"            If scalar, will assume equal width and height.\\n\",\n        \"        dpi: Dots per inch, controlling the resolution of the image.\\n\",\n        \"        scale: Factor to scale the size of the figure by. This is a convenience for\\n\",\n        \"            increasing the size of the plot at the same DPI.\\n\",\n        \"\\n\",\n        \"    Returns:\\n\",\n        \"        A matplotlib.figure.Figure to use for plotting.\\n\",\n        \"    \\\"\\\"\\\"\\n\",\n        \"    if not isinstance(size, (tuple, list)):\\n\",\n        \"        size = (size, size)\\n\",\n        \"    fig = plt.figure(figsize=(scale * size[0], scale * size[1]), dpi=dpi)\\n\",\n        \"    ax = fig.add_axes([0, 0, 1, 1], frameon=False)\\n\",\n        \"    ax.get_xaxis().set_visible(False)\\n\",\n        \"    ax.get_yaxis().set_visible(False)\\n\",\n        \"    plt.autoscale(tight=True)\\n\",\n        \"    ax.set_xticks([])\\n\",\n        \"    ax.set_yticks([])\\n\",\n        \"    ax.grid(False)\\n\",\n        \"    return fig\\n\",\n        \"\\n\",\n        \"\\n\",\n        \"def plot_instance(instance):\\n\",\n        \"    pts = instance.numpy()\\n\",\n        \"\\n\",\n        \"    cmap = sns.color_palette(\\\"tab10\\\")\\n\",\n        \"    for k, (src, dst) in enumerate(instance.skeleton.edge_inds):\\n\",\n        \"        plt.plot(pts[(src, dst), 0], pts[(src, dst), 1], \\\".-\\\", ms=15, lw=3, c=cmap[k])\"\n      ]\n    },\n    {\n      \"cell_type\": \"code\",\n      \"execution_count\": null,\n      \"metadata\": {\n        \"colab\": {\n          \"base_uri\": \"https://localhost:8080/\",\n          \"height\": 463\n        },\n        \"id\": \"JoBYgKLtkn6x\",\n        \"outputId\": \"149acce5-e42d-4f58-ad71-09a5a98d627b\"\n      },\n      \"outputs\": [\n        {\n          \"data\": {\n            \"image/png\": 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\",\n            \"text/plain\": [\n              \"
\"\n            ]\n          },\n          \"metadata\": {},\n          \"output_type\": \"display_data\"\n        }\n      ],\n      \"source\": [\n        \"# @title  { run: \\\"auto\\\" }\\n\",\n        \"\\n\",\n        \"idx = 752 # @param {type:\\\"slider\\\", min:0, max:2819, step:1}\\n\",\n        \"\\n\",\n        \"lf = predictions[idx]\\n\",\n        \"\\n\",\n        \"fig = imgfig(size=8)\\n\",\n        \"plt.imshow(lf.image.squeeze(), cmap=\\\"gray\\\")\\n\",\n        \"for instance in lf:\\n\",\n        \"    plot_instance(instance)\"\n      ]\n    }\n  ],\n  \"metadata\": {\n    \"colab\": {\n      \"collapsed_sections\": [\n        \"BEvDiRR-e0hv\"\n      ],\n      \"provenance\": []\n    },\n    \"kernelspec\": {\n      \"display_name\": \"Python 3\",\n      \"name\": \"python3\"\n    },\n    \"language_info\": {\n      \"name\": \"python\"\n    }\n  },\n  \"nbformat\": 4,\n  \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "docs/notebooks/Training_and_inference_on_an_example_dataset.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"view-in-github\"\n   },\n   \"source\": [\n    \"\\\"Open\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Training and inference on an example dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"LlV70jDuWzea\"\n   },\n   \"source\": [\n    \"In this notebook we'll install [`sleap-nn`](https://nn.sleap.ai/latest), download a sample dataset, run training and inference on that dataset using the `sleap-nn`, and then download the predictions.\\n\",\n    \"\\n\",\n    \"**Note:** If you only need to perform training and inference (and not use the full SLEAP GUI or labeling tools), you don't need to install the entire `sleap` package. You can just install [`sleap-nn`](https://nn.sleap.ai/latest), which is a lighter-weight package focused on model training and inference.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"yX9noEb8m8re\"\n   },\n   \"source\": [\n    \"## Install sleap-nn\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 1000\n    },\n    \"id\": \"DUfnkxMtLcK3\",\n    \"outputId\": \"a6340ef1-eaac-42ef-f8d4-bcc499feb57b\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"zsh:1: command not found: pip\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install -qqq \\\"sleap-nn[torch-cpu]\\\"\\n\",\n    \"\\n\",\n    \"# if you have GPU (in colab, enable GPU runtime)\\n\",\n    \"# !pip install -qqq \\\"sleap-nn[torch-cuda128]\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"iq7jrgUksLtR\"\n   },\n   \"source\": [\n    \"## Download sample training data into Colab\\n\",\n    \"Let's download a sample dataset from the SLEAP [sample datasets repository](https://github.com/talmolab/sleap-datasets) into Colab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\"\n    },\n    \"id\": \"fm3cU1Bc0tWc\",\n    \"outputId\": \"c0ac5677-e3c5-477c-a2f7-44d619208b22\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"zsh:1: command not found: apt-get\\n\",\n      \"--2025-09-23 22:49:59--  https://github.com/talmolab/sleap-datasets/releases/download/dm-courtship-v1/drosophila-melanogaster-courtship.zip\\n\",\n      \"Resolving github.com (github.com)... 140.82.116.4\\n\",\n      \"Connecting to github.com (github.com)|140.82.116.4|:443... connected.\\n\",\n      \"HTTP request sent, awaiting response... 302 Found\\n\",\n      \"Location: https://release-assets.githubusercontent.com/github-production-release-asset/263375180/16df8d00-94f1-11ea-98d1-6c03a2f89e1c?sp=r&sv=2018-11-09&sr=b&spr=https&se=2025-09-24T06%3A32%3A23Z&rscd=attachment%3B+filename%3Ddrosophila-melanogaster-courtship.zip&rsct=application%2Foctet-stream&skoid=96c2d410-5711-43a1-aedd-ab1947aa7ab0&sktid=398a6654-997b-47e9-b12b-9515b896b4de&skt=2025-09-24T05%3A32%3A15Z&ske=2025-09-24T06%3A32%3A23Z&sks=b&skv=2018-11-09&sig=Ij3ERUXEA5fEIAbcmukQghtno0Fl4j0%2BrI9epJ%2FH4Jw%3D&jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmVsZWFzZS1hc3NldHMuZ2l0aHVidXNlcmNvbnRlbnQuY29tIiwia2V5Ijoia2V5MSIsImV4cCI6MTc1ODY5MzI5OSwibmJmIjoxNzU4NjkyOTk5LCJwYXRoIjoicmVsZWFzZWFzc2V0cHJvZHVjdGlvbi5ibG9iLmNvcmUud2luZG93cy5uZXQifQ.Z7KygNVvJl9-7hx2zZxvJ2Nk11iVAFbZHyRtvwuRNOA&response-content-disposition=attachment%3B%20filename%3Ddrosophila-melanogaster-courtship.zip&response-content-type=application%2Foctet-stream [following]\\n\",\n      \"--2025-09-23 22:49:59--  https://release-assets.githubusercontent.com/github-production-release-asset/263375180/16df8d00-94f1-11ea-98d1-6c03a2f89e1c?sp=r&sv=2018-11-09&sr=b&spr=https&se=2025-09-24T06%3A32%3A23Z&rscd=attachment%3B+filename%3Ddrosophila-melanogaster-courtship.zip&rsct=application%2Foctet-stream&skoid=96c2d410-5711-43a1-aedd-ab1947aa7ab0&sktid=398a6654-997b-47e9-b12b-9515b896b4de&skt=2025-09-24T05%3A32%3A15Z&ske=2025-09-24T06%3A32%3A23Z&sks=b&skv=2018-11-09&sig=Ij3ERUXEA5fEIAbcmukQghtno0Fl4j0%2BrI9epJ%2FH4Jw%3D&jwt=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmVsZWFzZS1hc3NldHMuZ2l0aHVidXNlcmNvbnRlbnQuY29tIiwia2V5Ijoia2V5MSIsImV4cCI6MTc1ODY5MzI5OSwibmJmIjoxNzU4NjkyOTk5LCJwYXRoIjoicmVsZWFzZWFzc2V0cHJvZHVjdGlvbi5ibG9iLmNvcmUud2luZG93cy5uZXQifQ.Z7KygNVvJl9-7hx2zZxvJ2Nk11iVAFbZHyRtvwuRNOA&response-content-disposition=attachment%3B%20filename%3Ddrosophila-melanogaster-courtship.zip&response-content-type=application%2Foctet-stream\\n\",\n      \"Resolving release-assets.githubusercontent.com (release-assets.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\\n\",\n      \"Connecting to release-assets.githubusercontent.com (release-assets.githubusercontent.com)|185.199.109.133|:443... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 111973079 (107M) [application/octet-stream]\\n\",\n      \"Saving to: ā€˜dataset.zipā€™\\n\",\n      \"\\n\",\n      \"dataset.zip         100%[===================>] 106.79M  39.0MB/s    in 2.7s    \\n\",\n      \"\\n\",\n      \"2025-09-23 22:50:02 (39.0 MB/s) - ā€˜dataset.zipā€™ saved [111973079/111973079]\\n\",\n      \"\\n\",\n      \"Archive:  dataset.zip\\n\",\n      \"   creating: dataset/drosophila-melanogaster-courtship\\n\",\n      \"  inflating: dataset/drosophila-melanogaster-courtship/.DS_Store  \\n\",\n      \"   creating: dataset/__MACOSX\\n\",\n      \"   creating: dataset/__MACOSX/drosophila-melanogaster-courtship\\n\",\n      \"  inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._.DS_Store  \\n\",\n      \"  inflating: dataset/drosophila-melanogaster-courtship/20190128_113421.mp4  \\n\",\n      \"  inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._20190128_113421.mp4  \\n\",\n      \"  inflating: dataset/drosophila-melanogaster-courtship/courtship_labels.slp  \\n\",\n      \"  inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._courtship_labels.slp  \\n\",\n      \"  inflating: dataset/drosophila-melanogaster-courtship/example.jpg  \\n\",\n      \"  inflating: dataset/__MACOSX/drosophila-melanogaster-courtship/._example.jpg  \\n\",\n      \"zsh:1: command not found: tree\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!apt-get install tree\\n\",\n    \"!wget -O dataset.zip https://github.com/talmolab/sleap-datasets/releases/download/dm-courtship-v1/drosophila-melanogaster-courtship.zip\\n\",\n    \"!mkdir dataset\\n\",\n    \"!unzip dataset.zip -d dataset\\n\",\n    \"!rm dataset.zip\\n\",\n    \"!tree dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"xZ-sr67av5uu\"\n   },\n   \"source\": [\n    \"## Train models\\n\",\n    \"For the top-down pipeline, we'll need train two models: a centroid model and a centered-instance model.\\n\",\n    \"\\n\",\n    \"We'll first train a model for centroids using the default **training profile**. The training profile determines the model architecture, the learning rate, and other parameters.\\n\",\n    \"\\n\",\n    \"When you start training, you'll first see the training parameters and then the training and validation loss for each training epoch. \\n\",\n    \"\\n\",\n    \"As soon as you're satisfied with the validation loss you see for an epoch during training, you're welcome to stop training by clicking the stop button. The version of the model with the lowest validation loss is saved during training, and that's what will be used for inference.\\n\",\n    \"\\n\",\n    \"If you don't stop training, it will run for 200 epochs or until validation loss fails to improve for some number of epochs (controlled by the `early_stopping` fields in the training profile).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--2025-09-23 22:50:13--  https://raw.githubusercontent.com/talmolab/sleap-nn/refs/heads/main/docs/sample_configs/config_centroid_unet.yaml\\n\",\n      \"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8003::154, 2606:50c0:8000::154, 2606:50c0:8001::154, ...\\n\",\n      \"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8003::154|:443... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 3286 (3.2K) [text/plain]\\n\",\n      \"Saving to: ā€˜baseline.centroid.yamlā€™\\n\",\n      \"\\n\",\n      \"baseline.centroid.y 100%[===================>]   3.21K  --.-KB/s    in 0s      \\n\",\n      \"\\n\",\n      \"2025-09-23 22:50:13 (33.0 MB/s) - ā€˜baseline.centroid.yamlā€™ saved [3286/3286]\\n\",\n      \"\\n\",\n      \"--2025-09-23 22:50:14--  https://raw.githubusercontent.com/talmolab/sleap-nn/refs/heads/main/docs/sample_configs/config_topdown_centered_instance_unet.yaml\\n\",\n      \"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8003::154, 2606:50c0:8000::154, 2606:50c0:8001::154, ...\\n\",\n      \"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8003::154|:443... connected.\\n\",\n      \"HTTP request sent, awaiting response... 200 OK\\n\",\n      \"Length: 3114 (3.0K) [text/plain]\\n\",\n      \"Saving to: ā€˜baseline.centered_instance.yamlā€™\\n\",\n      \"\\n\",\n      \"baseline.centered_i 100%[===================>]   3.04K  --.-KB/s    in 0s      \\n\",\n      \"\\n\",\n      \"2025-09-23 22:50:14 (32.3 MB/s) - ā€˜baseline.centered_instance.yamlā€™ saved [3114/3114]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Let's get the default config files\\n\",\n    \"!wget -O baseline.centroid.yaml https://raw.githubusercontent.com/talmolab/sleap-nn/refs/heads/main/docs/sample_configs/config_centroid_unet.yaml\\n\",\n    \"!wget -O baseline.centered_instance.yaml https://raw.githubusercontent.com/talmolab/sleap-nn/refs/heads/main/docs/sample_configs/config_topdown_centered_instance_unet_medium_rf.yaml\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"id\": \"QKf6qzMqNBUi\"\n   },\n   \"outputs\": [],\n   \"source\": \"!sleap-nn train --config baseline.centroid.yaml \\\"data_config.train_labels_path=[dataset/drosophila-melanogaster-courtship/courtship_labels.slp]\\\" trainer_config.ckpt_dir=\\\"models\\\" trainer_config.run_name=\\\"courtship.centroid\\\" trainer_config.max_epochs=200\"\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"Vm3i0ry04IMx\"\n   },\n   \"source\": [\n    \"Let's now train a centered-instance model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": \"!sleap-nn train --config baseline.centered_instance.yaml \\\"data_config.train_labels_path=[dataset/drosophila-melanogaster-courtship/courtship_labels.slp]\\\" trainer_config.ckpt_dir=\\\"models\\\" trainer_config.run_name=\\\"courtship.topdown_confmaps\\\" trainer_config.max_epochs=200\"\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"whOf8PaFxYbt\"\n   },\n   \"source\": [\n    \"The models (along with the profiles and ground truth data used to train and validate the model) are saved in the `models/` directory:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"colab\": {\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 306\n    },\n    \"id\": \"GBUTQ2Cm44En\",\n    \"outputId\": \"ca298981-af65-43b3-f0f6-573f423acba8\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[01;34mmodels/\\u001b[0m\\n\",\n      \"ā”œā”€ā”€ \\u001b[01;34mcourtship.centroid\\u001b[0m\\n\",\n      \"ā”‚Ā Ā  ā”œā”€ā”€ \\u001b[00mbest.ckpt\\u001b[0m\\n\",\n      \"ā”‚Ā Ā  ā”œā”€ā”€ \\u001b[00minitial_config.yaml\\u001b[0m\\n\",\n      \"ā”‚Ā Ā  ā”œā”€ā”€ \\u001b[00mlabels_train_gt_0.slp\\u001b[0m\\n\",\n      \"ā”‚Ā Ā  ā”œā”€ā”€ \\u001b[00mlabels_val_gt_0.slp\\u001b[0m\\n\",\n      \"ā”‚Ā Ā  ā”œā”€ā”€ \\u001b[00mtraining_config.yaml\\u001b[0m\\n\",\n      \"ā”‚Ā Ā  ā””ā”€ā”€ \\u001b[00mtraining_log.csv\\u001b[0m\\n\",\n      \"ā””ā”€ā”€ \\u001b[01;34mcourtship.topdown_confmaps\\u001b[0m\\n\",\n      \"    ā”œā”€ā”€ \\u001b[00mbest.ckpt\\u001b[0m\\n\",\n      \"    ā”œā”€ā”€ \\u001b[00minitial_config.yaml\\u001b[0m\\n\",\n      \"    ā”œā”€ā”€ \\u001b[00mlabels_train_gt_0.slp\\u001b[0m\\n\",\n      \"    ā”œā”€ā”€ \\u001b[00mlabels_val_gt_0.slp\\u001b[0m\\n\",\n      \"    ā”œā”€ā”€ \\u001b[00mtraining_config.yaml\\u001b[0m\\n\",\n      \"    ā””ā”€ā”€ \\u001b[00mtraining_log.csv\\u001b[0m\\n\",\n      \"\\n\",\n      \"3 directories, 12 files\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!tree models/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"nIsKUX661xFK\"\n   },\n   \"source\": [\n    \"## Inference\\n\",\n    \"Let's run inference with our trained models for centroids and centered instances.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"id\": \"CLtjtq9E1Znr\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2025-09-24 00:12:47 | INFO | sleap_nn.predict:run_inference:319 | Started inference at: 2025-09-24 00:12:47.871203\\n\",\n      \"2025-09-24 00:12:47 | INFO | sleap_nn.predict:run_inference:330 | Integral refinement is not supported with MPS device. Using CPU.\\n\",\n      \"2025-09-24 00:12:47 | INFO | sleap_nn.predict:run_inference:335 | Using device: cpu\\n\",\n      \"\\u001b[2KPredicting... \\u001b[90mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\\u001b[0m \\u001b[35m100%\\u001b[0m \\u001b[32m101/101\\u001b[0m ETA: \\u001b[36m0:00:00\\u001b[0m Elapsed: \\u001b[33m0:00:17\\u001b[0m \\u001b[31m6.0 FPS\\u001b[0m0 FPS\\u001b[0m7 FPS\\u001b[0m\\n\",\n      \"\\u001b[?25h2025-09-24 00:13:05 | INFO | sleap_nn.predict:run_inference:453 | Finished inference at: 2025-09-24 00:13:05.531684\\n\",\n      \"2025-09-24 00:13:05 | INFO | sleap_nn.predict:run_inference:454 | Total runtime: 17.660489082336426 secs\\n\",\n      \"2025-09-24 00:13:05 | INFO | sleap_nn.predict:run_inference:465 | Predictions output path: dataset/drosophila-melanogaster-courtship/20190128_113421.predictions.slp\\n\",\n      \"2025-09-24 00:13:05 | INFO | sleap_nn.predict:run_inference:466 | Saved file at: 2025-09-24 00:13:05.594857\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!sleap-nn track -i \\\"dataset/drosophila-melanogaster-courtship/20190128_113421.mp4\\\" --frames 0-100 -m \\\"models/courtship.centroid\\\" -m \\\"models/courtship.topdown_confmaps\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"nzObCUToEqwA\"\n   },\n   \"source\": [\n    \"When inference is finished, predictions are saved in a file. Since we didn't specify a path, it will be saved as `