diff --git a/.gitignore b/.gitignore index 73d56d3..13fda86 100644 --- a/.gitignore +++ b/.gitignore @@ -82,3 +82,8 @@ venv/ # written by setuptools_scm **/_version.py + +# dev testing folders to keep clean commit history +# will use examples/ if want to distribute +notebooks/ +scripts/ diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..7293436 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,727 @@ +# AGENTS.md - ndev-morphology + +**Single-cell morphological analysis for napari** + +This document provides guidance for AI assistants and developers working on the `ndev-morphology` package. For workspace-level guidance, see the root `AGENTS.md`. + +--- + +## Quick Start for Development + +```powershell +# Navigate to ndev-morphology directory +cd C:\Users\timmo\ndev-kit\ndev-morphology + +# Create and activate virtual environment (using uv) +uv venv +.venv\Scripts\activate + +# Install in editable mode with dev dependencies +uv pip install -e . --group dev + +# Run tests to verify setup +pytest -v +``` + +**IMPORTANT**: Always activate the virtual environment before running tests or development commands. + +--- + +## Development Philosophy + +### No Backwards Compatibility Required + +**This code is not yet public.** We can make whatever breaking changes are needed at the moment. Do not hesitate to refactor, rename, or restructure when it improves the architecture. There is no need to maintain deprecated APIs or provide migration paths. + +### napari-workflows & napari-assistant Integration + +**Core Design Principle**: All ndev-morphology functions should be compatible with [napari-workflows](https://github.com/haesleinhuepf/napari-workflows) and discoverable in [napari-assistant](https://github.com/haesleinhuepf/napari-assistant). + +This is **more important than building our own workflow management system**. By designing functions that work with napari-workflows, we get: + +1. **Undo/Redo** — napari-workflows provides `UndoRedoController` that tracks workflow states +2. **Workflow saving/loading** — YAML-based workflow persistence +3. **Code generation** — Auto-generate Python scripts and Jupyter notebooks from workflows +4. **Composability** — Our functions become building blocks in larger workflows with other tools (pyclesperanto, devbio-napari, etc.) +5. **Interoperability** — Works with napari-workflow-optimizer, napari-workflow-inspector, napari-script-editor + +**How napari-workflows works**: +```python +from napari_workflows import Workflow + +# Define a workflow using function references and layer names +w = Workflow() +w.set("skeleton", skeletonize_labels, "labels_input") +w.set("pruned", prune_short_branches, "skeleton", min_length=5) +w.set("branches", summarize_branches, "pruned") + +# Execute any step (dependencies auto-resolve) +result = w.get("branches") +``` + +**Verified Compatible Functions** (tested in `tests/test_napari_workflows_compat.py`): +- `skeletonize_labels`, `separate_touching_skeleton_labels`, `exclude_region_from_skeleton`, `fill_skeleton_gaps` +- `filter_labels_by_size`, `exclude_labels_on_edges`, `connect_breaks_between_labels` + +These functions accept numpy arrays and return numpy arrays, making them fully compatible with napari-workflows' `Workflow.set()` pattern. Workflows can be saved/loaded as YAML files. + +**How napari-assistant discovers functions**: +Functions are discovered via the `napari.yaml` manifest. Use proper `display_name` prefixes: + +```yaml +widgets: + - command: ndev-morphology.skeletonize_labels_widget + display_name: Segmentation post-processing > Skeletonize Labels (ndev) +``` + +**Valid prefixes for napari-assistant**: +- `Filtering / noise removal >` +- `Filtering / background removal >` +- `Filtering >` +- `Image math >` +- `Transform >` +- `Projection >` +- `Segmentation / binarization >` +- `Segmentation / labeling >` +- `Segmentation post-processing >` +- `Measurement >` +- `Label neighbor filters >` +- `Label filters >` +- `Visualization >` + +--- + +## Package Purpose + +`ndev-morphology` provides single-cell morphological analysis tools, primarily for neuronal structures (dendrites, axons). The package bridges **skan** (skeleton analysis) and **napari** (visualization) with a focus on: + +1. **Label preparation** — Filtering, cleaning, and preparing segmentation masks +2. **Skeleton creation** — Label-aware skeletonization preserving cell identity +3. **Branch analysis** — Quantifying branch properties using skan +4. **Sholl analysis** — Counting skeleton crossings at concentric shells +5. **Directed tree analysis** — Understanding branch hierarchy from soma to tips (via networkx) +6. **napari visualization** — Interactive widgets for analysis and exploration +7. **SWC interoperability** — Import/export standard neuron morphology format + +### Target Users + +- **Neuroscientists** analyzing dendritic/axonal morphology in vitro +- **Image analysts** quantifying branching structures in biology +- **Developers** needing programmatic access to morphology metrics + +--- + +## Architecture Overview + +### Core Principle: Separation of Concerns + +Following the ndev-kit pattern, **business logic is separate from UI**: + +``` +src/ndev_morphology/ +├── model.py # MorphologyModel - unified data wrapper +├── labels.py # Label preprocessing (pure Python) +├── skeleton.py # Skeleton creation/modification (pure Python) +├── sholl.py # Sholl analysis with ShollResult dataclass +├── branch.py # Branch-level measurements and classification +├── soma.py # Soma detection and centroid extraction +├── tree.py # Directed tree analysis (networkx) +├── analysis.py # Per-cell analysis pipelines, batch processing +├── swc.py # SWC format import/export (future) +├── _geometry.py # Visualization helpers (skeleton_to_paths, etc.) +├── widgets/ # napari/magicgui widgets (UI layer) +│ ├── _utils.py # Shared widget utilities +│ ├── labels_widget.py +│ ├── skeletonize_widget.py +│ ├── skeleton_widget.py +│ ├── sholl_widget.py +│ ├── tree_widget.py +│ └── ... +└── napari.yaml # Plugin manifest +``` + +**Rule**: Any function in `widgets/` should be a thin wrapper around core functionality. + +--- + +## MorphologyModel Design + +### Purpose + +`MorphologyModel` is a **convenience wrapper** for working with skan.Skeleton objects: +- Bundles `skan.Skeleton` with metadata (spacing, source name) +- Provides lazy-computed properties (`summary`, `n_branches`, `total_length`) +- Can attach to napari layers for visualization/export (e.g., SWC) + +### When to Use + +| Use Case | Pattern | +|----------|--------| +| Creating from array | `MorphologyModel.from_array(skeleton_image, spacing=(0.2, 0.2))` | +| Creating from layer | `MorphologyModel.from_layer(labels_layer)` | +| Accessing branch data | `model.summary`, `model.compute_branches()` | +| SWC export (future) | `model.to_swc()` | + +### Layer Metadata Pattern + +```python +# Attach model to output layer for later retrieval +shapes_layer.metadata['morphology_model'] = model + +# Retrieve for export or inspection +model = layer.metadata.get('morphology_model') +``` + +**Note**: Workflow state and undo/redo should be handled by **napari-workflows**, not MorphologyModel. The model is for data representation, not pipeline management. + +--- + +## SWC Format Specification + +SWC is the standard format for neuron morphology, used by neuromorpho.org. + +### Format + +ASCII text, 7 columns per line: + +| Column | Name | Description | +|--------|------|-------------| +| 1 | Sample ID | Integer, typically starting from 1 | +| 2 | Type | 0=undefined, 1=soma, 2=axon, 3=dendrite, 4=apical dendrite | +| 3 | X | X coordinate in micrometers | +| 4 | Y | Y coordinate in micrometers | +| 5 | Z | Z coordinate in micrometers | +| 6 | Radius | Half the thickness in micrometers | +| 7 | Parent | Parent sample ID (-1 for root) | + +### Example + +``` +# SWC file for simple neuron +1 1 0.0 0.0 0.0 5.0 -1 # Soma at origin +2 3 10.0 0.0 0.0 1.0 1 # Dendrite from soma +3 3 20.0 5.0 0.0 0.8 2 # Branch continues +4 3 20.0 -5.0 0.0 0.8 2 # Fork +``` + +### Mapping to ndev-morphology + +| SWC Concept | ndev-morphology Equivalent | +|-------------|---------------------------| +| Sample ID | Node index in directed tree | +| Type | Need classification (soma labels, branch type heuristics) | +| X, Y, Z | `skeleton.coordinates[node]` | +| Radius | Could derive from skeleton width or set constant | +| Parent | Edge source in DirectedTree.graph | + +### Integration Strategy + +Prefer **napari-swc-reader** as optional dependency for I/O, with our own conversion functions: + +```python +# Future: ndev_morphology/swc.py +def tree_to_swc(tree: DirectedTree, soma_radius: float = 5.0) -> pd.DataFrame: + """Convert DirectedTree to SWC-format DataFrame.""" + ... + +def swc_to_tree(swc_df: pd.DataFrame, spacing: tuple) -> DirectedTree: + """Convert SWC DataFrame to DirectedTree.""" + ... +``` + +--- + +## skan vs networkx Design + +| Library | Purpose | When to Use | +|---------|---------|-------------| +| **skan** | Efficient skeleton representation (sparse CSR matrices), branch summarization, Sholl analysis | Default for skeleton analysis, branch measurements | +| **networkx** | Directed graph algorithms, tree traversal, branch ordering | When you need directed trees (soma→tips), branch order computation | + +**Key insight**: skan already provides `skeleton_to_nx()` for conversion to networkx. Don't reinvent this. + +**Workflow**: +1. Create skeleton with `skan.Skeleton(image, spacing=...)` +2. Use `skan.summarize(skeleton)` for branch properties +3. Convert to networkx only when you need directed tree operations: + ```python + from skan.csr import skeleton_to_nx + G = skeleton_to_nx(skeleton) + # Add direction from soma node + ``` + +--- + +## Module Specifications + +### `model.py` — MorphologyModel + +**Purpose**: Unified data wrapper for skeleton analysis and visualization. + +**Classes**: +- `MorphologyModel` — Wraps skan.Skeleton with metadata and lazy-computed properties + +**Functions**: +- `get_model_from_layer(layer)` — Retrieve model from layer metadata +- `attach_model_to_layer(layer, model)` — Store model in layer metadata + +**Usage Pattern**: +```python +from ndev_morphology import MorphologyModel + +# From numpy array (explicit creation) +model = MorphologyModel.from_array(skeleton_image, spacing=(0.2, 0.2)) + +# From napari layer (widget convenience) +model = MorphologyModel.from_layer(skeleton_layer) + +# Access properties +print(model.n_branches, model.total_length) +branches_df = model.compute_branches() +``` + +**Status**: ✅ Implemented and tested + +### `labels.py` — Label Preprocessing + +**Purpose**: Prepare segmentation masks for skeletonization. + +**Functions**: +- `filter_labels_by_size(labels, min_size, max_size)` — Remove labels outside area range +- `exclude_labels_on_edges(labels)` — Remove border-touching labels +- `connect_breaks_between_labels(labels, connect_distance)` — Merge nearby fragments + +**Status**: ✅ Implemented and tested + +### `skeleton.py` — Skeleton Operations + +**Purpose**: Create and modify skeletons while preserving label identity. + +**Functions**: +- `skeletonize_labels(labels)` — Skeletonize preserving label IDs (unique vs scikit-image) +- `separate_touching_skeleton_labels(skeleton_labels)` — Remove touching points between labels +- `exclude_region_from_skeleton(skeleton, mask, dilation_iterations)` — Exclude soma region + +**Status**: ✅ Implemented and tested + +### `sholl.py` — Sholl Analysis + +**Purpose**: Quantify branching complexity via shell crossing counts. + +**Classes**: +- `ShollResult` — Dataclass with radii, counts, and computed statistics: + - `max_crossings`, `critical_radius`, `enclosing_radius`, `mean_crossings`, `total_crossings` + +**Functions**: +- `compute_sholl_profile(skeleton, center, radii, ...)` — Wrapper around `skan.sholl_analysis` + +**Status**: ✅ Implemented and tested + +### `branch.py` — Branch Analysis + +**Purpose**: Branch-level measurements and classification using skan. + +**Classes**: +- `BranchType` — Enum for branch types (ENDPOINT_TO_ENDPOINT, JUNCTION_TO_ENDPOINT, etc.) + +**Functions**: +- `summarize_branches(skeleton, *, intensity_image=None)` — Extended skan.summarize with tortuosity +- `compute_tortuosity(branches_df)` — Path length / euclidean distance for each branch +- `filter_branches_by_type(branches_df, types)` — Filter DataFrame by branch types + +**Status**: ✅ Implemented and tested + +### `soma.py` — Soma Detection + +**Purpose**: Identify cell body for directed tree analysis. + +**Functions**: +- `detect_soma_centroid(labels, *, method, intensity_image)` — Find soma center via multiple methods +- `find_soma_node(skeleton, soma_centroid)` — Find nearest skeleton node to soma +- `get_label_centroid(labels, label_id)` — Get centroid of a specific label + +**Status**: ✅ Implemented and tested + +### `tree.py` — Directed Tree Analysis + +**Purpose**: Analyze skeletons as directed trees rooted at soma. + +**Classes**: +- `DirectedTree` — Wraps networkx DiGraph with morphology-specific methods +- `BranchOrder` — Constants for semantic branch order values + +**Functions**: +- `create_directed_tree(skeleton, root_coords)` — Create single directed tree +- `create_directed_trees_from_soma(skeleton_image, soma_mask, soma_centroid)` — Create multiple trees from radiating branches +- `compute_branch_order(tree)` — Centrifugal ordering (1=primary, 2=secondary, etc.) +- `compute_strahler_order(tree)` — Strahler ordering (tips=1, merging increases) +- `find_longest_path(tree)` — Find longest path from root to tip +- `summarize_directed_tree(tree)` — DataFrame with per-branch order information + +**Status**: ✅ Implemented and tested + +### `analysis.py` — Per-Cell Analysis + +**Purpose**: Batch processing pipeline for multi-cell images. + +**Classes**: +- `CellAnalysisResult` — Dataclass containing skeleton, branches, Sholl, summary + +**Functions**: +- `analyze_single_cell(skeleton_labels, label_id, ...)` — Comprehensive analysis of one cell +- `analyze_all_cells(skeleton_labels, ...)` — Analyze all labeled cells, returns DataFrame +- `analyze_all_cells_generator(...)` — Memory-efficient generator version +- `aggregate_branch_stats(results)` — Combine branch DataFrames from multiple cells + +**Status**: ✅ Implemented and tested + +### `pruning.py` — Skeleton Pruning + +**Purpose**: Remove unwanted branches from skeletons. + +**Functions**: +- `prune_short_branches(skeleton, min_length)` — Remove branches below length threshold +- `remove_isolated_cycles(skeleton)` — Remove isolated cycle branches +- `prune_skeleton_to_image(skeleton, prune_indices)` — Convert pruned skeleton back to image + +**Status**: ✅ Implemented and tested + +### `_geometry.py` — Visualization Utilities + +**Purpose**: Convert analysis results to napari layer formats. + +**Functions**: +- `skeleton_to_paths(skeleton)` — Convert skan.Skeleton to paths + properties for Shapes +- `sholl_shells_to_ellipses(center, radii)` — Generate ellipse bounding boxes for Sholl visualization + +**Status**: ✅ Implemented, needs tests + +--- + +## Testing Strategy + +### Test Organization + +``` +tests/ +├── conftest.py # Shared fixtures +├── test_labels.py # Label preprocessing tests +├── test_skeleton.py # Skeleton operations tests ✅ +├── test_sholl.py # Sholl analysis tests ✅ +├── test_branch.py # Branch analysis tests +├── test_soma.py # Soma detection tests +├── test_analysis.py # Per-cell pipeline tests +├── test_geometry.py # Visualization utility tests +└── resources/ # Test data files (if needed) +``` + +### Testing Priorities + +1. **Core functions first** — Pure Python, no napari dependency +2. **Unit tests > Integration tests** — Fast iteration +3. **Defer widget tests** — Manual testing with sample data is faster during development + +### Testing magicgui Functions Without napari + +The `@magic_factory` decorated functions can be tested without napari: + +```python +def test_skeleton_to_shapes_core(): + """Test skeleton_to_shapes logic without napari viewer.""" + from ndev_morphology.widgets.skeleton_widget import skeleton_to_shapes + + # Create test skeleton + skeleton = np.zeros((50, 50), dtype=np.uint8) + skeleton[25, 10:40] = 1 + + # Call the function directly (returns LayerDataTuple) + result = skeleton_to_shapes( + skeleton_image=skeleton, + spacing_y=1.0, + spacing_x=1.0, + edge_color='branch_distance', + edge_colormap='viridis', + edge_width=2.0, + ) + + # Validate result structure + data, kwargs, layer_type = result + assert layer_type == 'shapes' + assert 'properties' in kwargs +``` + +### ndevio Sample Data Integration + +Use `ndev-sampledata` for realistic test fixtures: + +```python +# In conftest.py +import pytest + +@pytest.fixture +def neuron_labels_data(): + """Load neuron labels from ndevio sample data.""" + from ndevio.sampledata import neuron_labels + + layer_data_tuples = neuron_labels() + # Extract the numpy array from LayerDataTuple + data, kwargs, layer_type = layer_data_tuples[0] + return data +``` + +--- + +## Widget Development + +### Pattern: Thin Wrapper Widgets + +```python +# In widgets/some_widget.py +from magicgui import magic_factory +from ..core_module import core_function + +@magic_factory( + call_button='Run Analysis', + param={'tooltip': 'Description of parameter'}, +) +def some_widget( + image: 'napari.types.LabelsData', + param: float = 1.0, +) -> 'napari.types.LayerDataTuple': + """Widget docstring shown in napari.""" + # Call core function + result = core_function(image, param) + + # Convert to napari layer format + return (result_data, {'name': 'Result'}, 'labels') +``` + +### Existing Widgets + +| Widget | Purpose | Core Function | +|--------|---------|---------------| +| `refine_labels_widget` | Filter/clean labels | `filter_labels_by_size`, `exclude_labels_on_edges`, `connect_breaks_between_labels` | +| `skeletonize_labels_widget` | Create label-aware skeleton | `skeletonize_labels`, `separate_touching_skeleton_labels` | +| `skeleton_to_shapes` | Visualize skeleton as Shapes | `skeleton_to_paths` | +| `sholl_analysis` | Interactive Sholl | `compute_sholl_profile`, `sholl_shells_to_ellipses` | +| `branch_analysis` | Branch measurements | `summarize_branches` | +| `directed_tree_analysis` | Directed tree from soma | `create_directed_trees_from_soma`, `compute_branch_order` | +| `prune_skeleton` | Remove short branches | `prune_short_branches` | + +--- + +## Implementation Status + +### Complete ✅ +- [x] `model.py` — MorphologyModel data wrapper +- [x] `labels.py` — Label preprocessing +- [x] `skeleton.py` — Skeleton creation and modification +- [x] `sholl.py` — Sholl analysis +- [x] `branch.py` — Branch measurements and classification +- [x] `soma.py` — Soma detection +- [x] `tree.py` — Directed tree analysis with branch ordering +- [x] `analysis.py` — Per-cell batch processing +- [x] `pruning.py` — Skeleton pruning +- [x] `_geometry.py` — Visualization utilities +- [x] Core widgets +- [x] 153 tests passing + +### In Progress 🔄 +- [ ] napari-assistant compatibility — ensure all widgets have proper `display_name` prefixes +- [ ] napari-workflows compatibility — verify all core functions work with `Workflow.set()` + +### Future ⏳ +- [ ] `swc.py` — SWC import/export +- [ ] Shaft ordering (Neurolucida-style main axon identification) +- [ ] Axon/dendrite classification (intensity-based) +- [ ] 3D support improvements +- [ ] Integration with napari-swc-reader +- [ ] Upstream improvements to napari-workflows if features are missing + +--- + +## Common Patterns + +### Creating a skan.Skeleton + +```python +import skan + +# From binary skeleton image +skeleton = skan.Skeleton(skeleton_image, spacing=(0.2, 0.2)) + +# From labels (use ndev-morphology first) +from ndev_morphology import skeletonize_labels +skeleton_labels = skeletonize_labels(labels) +# Then create skan.Skeleton per label or from combined binary +``` + +### Getting Branch Properties + +```python +import skan + +skeleton = skan.Skeleton(skeleton_image, spacing=(0.2, 0.2)) +branches = skan.summarize(skeleton) + +# branches is a DataFrame with columns: +# - skeleton_id, branch_type, branch_distance, euclidean_distance +# - coord_src_0, coord_src_1, coord_dst_0, coord_dst_1 (endpoints) +``` + +### Converting to networkx + +```python +from skan.csr import skeleton_to_nx + +G = skeleton_to_nx(skeleton) # Returns networkx.Graph + +# For directed tree from soma: +import networkx as nx +soma_node = find_closest_node_to_soma(skeleton, soma_centroid) +DG = nx.bfs_tree(G, soma_node) # Directed graph from soma +``` + +--- + +## Dependencies + +### Core Dependencies +```toml +dependencies = [ + "numpy", + "pandas", + "scipy", + "skan", + "scikit-image>=0.18.0", +] +``` + +### Optional Dependencies +```toml +[project.optional-dependencies] +napari = ["napari", "magicgui"] +gpu = ["pyclesperanto"] +``` + +### Dev Dependencies +```toml +[dependency-groups] +dev = [ + "pytest", + "pytest-cov", + "napari[pyqt6]", + "ndev-sampledata", # For realistic test fixtures +] +``` + +--- + +## Anti-Patterns to Avoid + +### ❌ Don't: Embed analysis logic in widgets + +```python +# BAD +@magic_factory() +def analyze_widget(labels): + # 50 lines of analysis code here + skeleton = skeletonize(labels) + branches = compute_branches(skeleton) + ... +``` + +### ✅ Do: Call core functions from widgets + +```python +# GOOD +@magic_factory() +def analyze_widget(labels): + from ..analysis import analyze_single_cell + result = analyze_single_cell(labels) + return (result['data'], result['kwargs'], 'shapes') +``` + +--- + +### ❌ Don't: Reinvent skan functionality + +```python +# BAD - skan already does this +def my_branch_summary(skeleton_image): + # Custom branch counting... +``` + +### ✅ Do: Wrap skan with convenience + +```python +# GOOD - add value on top of skan +def summarize_branches(skeleton, intensity_image=None): + branches = skan.summarize(skeleton, extra_properties=[intensity_mean]) + branches['tortuosity'] = branches['branch_distance'] / branches['euclidean_distance'] + return branches +``` + +--- + +## Resources + +### skan Documentation +- [skan API Reference](https://skeleton-analysis.org/stable/api/skan.csr.html) +- [skan.Skeleton class](https://skeleton-analysis.org/stable/api/skan.csr.html#skan.csr.Skeleton) +- [skan.summarize](https://skeleton-analysis.org/stable/api/skan.csr.html#skan.csr.summarize) +- [skan.sholl_analysis](https://skeleton-analysis.org/stable/api/skan.csr.html#skan.csr.sholl_analysis) +- [skeleton_to_nx](https://skeleton-analysis.org/stable/api/skan.csr.html#skan.csr.skeleton_to_nx) + +### SWC Format +- [SWC Specification](http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html) +- [NeuroMorpho.org](http://www.neuromorpho.org/) — Standard SWC archive + +### Related Projects +- [napari-workflows](https://github.com/haesleinhuepf/napari-workflows) — **Core dependency** for workflow management +- [napari-assistant](https://github.com/haesleinhuepf/napari-assistant) — **Target UI** for workflow building +- [devbio-napari](https://www.napari-hub.org/plugins/devbio-napari) — Bundle of compatible napari plugins +- [MorphoPy](https://github.com/berenslab/MorphoPy) — Morphology analysis inspiration +- [napari-skan](https://github.com/jni/skan) — skan's napari integration (reference) +- [napari-swc-reader](https://github.com/kephale/napari-swc-reader) — SWC I/O for napari +- [napari-swc-editor](https://github.com/LaboratoryOpticsBiosciences/napari-swc-editor) — SWC editing in napari + +### ndev-kit Ecosystem +- [ndevio](../ndevio/) — Image I/O and sample data +- [ndev-settings](../ndev-settings/) — Settings framework +- Root [AGENTS.md](../AGENTS.md) — Workspace-level guidance + +--- + +## Changelog + +- **2025-12-17**: napari-workflows integration decision + - Added "No Backwards Compatibility Required" statement + - Added napari-workflows and napari-assistant as core design principles + - Simplified MorphologyModel section (workflow management delegated to napari-workflows) + - Updated implementation priorities to focus on napari-assistant compatibility + +- **2025-12-17**: Major architecture update + - Added MorphologyModel data wrapper with pipeline philosophy + - Added SWC format specification and integration strategy + - Added tree.py module documentation + - Updated implementation status (all core modules now complete) + - Added 153 passing tests + +- **2025-12-15**: Initial AGENTS.md created + - Documented package architecture and module specifications + - Defined skan vs networkx design decisions + - Outlined implementation phases + - Established testing strategy + +--- + +## Contact + +**Package Maintainer**: Tim Monko (timmonko@gmail.com) + +**Issues**: GitHub Issues (when public) + +--- + +*This is living documentation. Update as the package evolves.* diff --git a/pyproject.toml b/pyproject.toml index 52c3551..90e8464 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,29 +30,32 @@ requires-python = ">=3.10" # See best practices: https://napari.org/stable/plugins/building_a_plugin/best_practices.html dependencies = [ "numpy", - "magicgui", - "napari", + "pandas", + "scipy", + "skan", "scikit-image>=0.18.0", - "pyclesperanto", - "ndevio>=0.5.0", - "nbatch>=0.0.4", ] [project.optional-dependencies] # Allow easily installation with the full, default napari installation # (including Qt backend) using ndev-morphology[all]. -qtpy-backend = [ - "napari[pyqt6]", +napari = [ + "napari", + "magicgui", +] +gpu = [ + "pyclesperanto", ] [dependency-groups] dev = [ "tox-uv", - "pytest", # https://docs.pytest.org/en/latest/contents.html - "pytest-cov", # https://pytest-cov.readthedocs.io/en/latest/ + "pytest", + "pytest-cov", "pre-commit", "napari[pyqt6]", + "ndevio", # for sample data ] [project.entry-points."napari.manifest"] diff --git a/src/ndev_morphology/__init__.py b/src/ndev_morphology/__init__.py index 65ad90b..c5a97a3 100644 --- a/src/ndev_morphology/__init__.py +++ b/src/ndev_morphology/__init__.py @@ -1,7 +1,149 @@ +""" +ndev-morphology: Morphology analysis of skeleton-like objects. + +A library for skeleton analysis, Sholl analysis, and branch +quantification. Designed for bioimage analysis of neuronal +and other branching structures. + +Core Modules +------------ +- skeleton: skeletonize_labels, exclude_region_from_skeleton, separate_touching_skeleton_labels +- labels: filter_labels_by_size, exclude_labels_on_edges, connect_breaks_between_labels +- sholl: compute_sholl_profile, ShollResult +- branch: summarize_branches, BranchType, compute_tortuosity, filter_branches_by_type +- soma: detect_soma_centroid, find_soma_node, get_label_centroid +- analysis: analyze_single_cell, analyze_all_cells, CellAnalysisResult + +For interactive napari visualization, see the widgets subpackage. + +Examples +-------- +>>> import skan +>>> from ndev_morphology import skeletonize_labels, compute_sholl_profile +>>> from ndev_morphology import summarize_branches, analyze_single_cell +>>> from ndev_morphology.widgets import skeleton_to_shapes, sholl_analysis +""" + try: from ._version import version as __version__ except ImportError: - __version__ = "unknown" + __version__ = 'unknown' + +# Geometry utilities (for programmatic use) +from ._geometry import sholl_shells_to_ellipses, skeleton_to_paths + +# Analysis pipeline +from .analysis import ( + CellAnalysisResult, + aggregate_branch_stats, + analyze_all_cells, + analyze_all_cells_generator, + analyze_single_cell, +) + +# Branch analysis +from .branch import ( + BranchType, + compute_tortuosity, + filter_branches_by_type, + summarize_branches, +) + +# Label operations +from .labels import ( + connect_breaks_between_labels, + exclude_labels_on_edges, + filter_labels_by_size, +) + +# Central data model +from .model import ( + MorphologyModel, + attach_model_to_layer, + get_model_from_layer, +) + +# Pruning operations +from .pruning import ( + prune_short_branches, + prune_skeleton_to_image, + remove_isolated_cycles, +) + +# Sholl analysis +from .sholl import ShollResult, compute_sholl_profile + +# Skeleton operations +from .skeleton import ( + exclude_region_from_skeleton, + fill_skeleton_gaps, + separate_touching_skeleton_labels, + skeletonize_labels, +) + +# Soma detection +from .soma import detect_soma_centroid, find_soma_node, get_label_centroid +# Directed tree analysis +from .tree import ( + BranchOrder, + DirectedTree, + compute_branch_order, + compute_strahler_order, + create_directed_tree, + create_directed_trees_from_soma, + find_longest_path, + get_paths_to_tips, + summarize_directed_tree, +) -__all__ = () +__all__ = [ + # Skeleton + 'skeletonize_labels', + 'exclude_region_from_skeleton', + 'separate_touching_skeleton_labels', + 'fill_skeleton_gaps', + # Labels + 'filter_labels_by_size', + 'exclude_labels_on_edges', + 'connect_breaks_between_labels', + # Pruning + 'prune_short_branches', + 'remove_isolated_cycles', + 'prune_skeleton_to_image', + # Sholl + 'ShollResult', + 'compute_sholl_profile', + # Branch + 'BranchType', + 'summarize_branches', + 'compute_tortuosity', + 'filter_branches_by_type', + # Soma + 'detect_soma_centroid', + 'find_soma_node', + 'get_label_centroid', + # Tree + 'DirectedTree', + 'BranchOrder', + 'create_directed_tree', + 'create_directed_trees_from_soma', + 'compute_branch_order', + 'compute_strahler_order', + 'find_longest_path', + 'get_paths_to_tips', + 'summarize_directed_tree', + # Analysis + 'CellAnalysisResult', + 'analyze_single_cell', + 'analyze_all_cells', + 'analyze_all_cells_generator', + 'aggregate_branch_stats', + # Model + 'MorphologyModel', + 'get_model_from_layer', + 'attach_model_to_layer', + # Geometry + 'skeleton_to_paths', + 'sholl_shells_to_ellipses', +] diff --git a/src/ndev_morphology/_geometry.py b/src/ndev_morphology/_geometry.py new file mode 100644 index 0000000..0f77d1f --- /dev/null +++ b/src/ndev_morphology/_geometry.py @@ -0,0 +1,151 @@ +""" +Geometry utilities for napari visualization. + +Internal module with functions to convert skeleton and Sholl data +to napari Shapes layer formats. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import pandas as pd +import skan + +if TYPE_CHECKING: + from numpy.typing import ArrayLike + +__all__ = [ + 'skeleton_to_paths', + 'sholl_shells_to_ellipses', +] + + +def skeleton_to_paths( + skeleton: skan.Skeleton, +) -> tuple[list[np.ndarray], pd.DataFrame]: + """ + Convert skan Skeleton to paths and properties for napari Shapes layer. + + Parameters + ---------- + skeleton : skan.Skeleton + A skan.Skeleton object. + + Returns + ------- + paths : list of np.ndarray + List of path coordinate arrays, each shape (n_points, ndim). + Coordinates are in pixel units (not physical units). + properties : pd.DataFrame + DataFrame with branch properties from skan.summarize(), plus path_id. + + Examples + -------- + >>> import skan + >>> import napari + >>> skel = skan.Skeleton(skeleton_image, spacing=(0.2, 0.2)) + >>> paths, props = skeleton_to_paths(skel) + >>> viewer = napari.Viewer() + >>> viewer.add_shapes( + ... paths, + ... shape_type='path', + ... properties=props, + ... edge_color='branch_distance', + ... edge_colormap='viridis', + ... edge_width=2, + ... ) + """ + # Get all path coordinates + paths = [skeleton.path_coordinates(i) for i in range(skeleton.n_paths)] + + # Get branch properties from skan + # Use separator='_' for consistent column names across skan versions + properties = skan.summarize(skeleton, separator='_') + + # Add path_id for coloring + properties['path_id'] = np.arange(len(properties)) + + return paths, properties + + +def sholl_shells_to_ellipses( + center: ArrayLike, + radii: ArrayLike, + *, + ndim: int = 2, +) -> list[np.ndarray]: + """ + Generate ellipse bounding boxes for napari Shapes layer. + + Creates circles (2D) as ellipses defined by their bounding boxes + for Sholl visualization. Using ellipses is more efficient than + polygon approximations and renders as true circles. + + Parameters + ---------- + center : ArrayLike + Center point coordinates (Y, X) for 2D or (Z, Y, X) for 3D. + Should be in pixel units for napari display. + radii : ArrayLike + Array of shell radii in pixel units. + ndim : int, optional + Number of dimensions (2 or 3). Default is 2. + For 3D, circles are drawn in the XY plane at the center Z. + + Returns + ------- + list of np.ndarray + List of ellipse bounding boxes, each shape (4, ndim). + Each ellipse is defined by 4 corner points of its bounding box: + [top-left, top-right, bottom-right, bottom-left]. + + Notes + ----- + napari's ellipse shape_type expects bounding box corners, not + center + radii. This function converts center + radius to the + 4-corner format. + + Examples + -------- + >>> shells = sholl_shells_to_ellipses(center=[100, 150], radii=[10, 20, 30]) + >>> viewer.add_shapes( + ... shells, + ... shape_type='ellipse', + ... edge_color='blue', + ... face_color='transparent', + ... ) + """ + center = np.asarray(center) + radii = np.asarray(radii) + + ellipses = [] + for radius in radii: + if ndim == 2: + # 2D ellipse bounding box: 4 corners (Y, X) + # [top-left, top-right, bottom-right, bottom-left] + y, x = center[0], center[1] + bbox = np.array( + [ + [y - radius, x - radius], # top-left + [y - radius, x + radius], # top-right + [y + radius, x + radius], # bottom-right + [y + radius, x - radius], # bottom-left + ] + ) + else: + # 3D ellipse in XY plane at center Z + z, y, x = center[0], center[1], center[2] + bbox = np.array( + [ + [z, y - radius, x - radius], + [z, y - radius, x + radius], + [z, y + radius, x + radius], + [z, y + radius, x - radius], + ] + ) + + ellipses.append(bbox) + + return ellipses diff --git a/src/ndev_morphology/analysis.py b/src/ndev_morphology/analysis.py new file mode 100644 index 0000000..956997e --- /dev/null +++ b/src/ndev_morphology/analysis.py @@ -0,0 +1,436 @@ +""" +Per-cell analysis pipelines for morphology analysis. + +Functions for analyzing individual cells and batch processing +multi-cell images. Combines skeleton, branch, Sholl, and soma +analysis into unified workflows. +""" + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import dataclass, field +from typing import TYPE_CHECKING + +import numpy as np +import pandas as pd +import skan + +from .branch import BranchType, summarize_branches +from .sholl import ShollResult, compute_sholl_profile +from .skeleton import skeletonize_labels +from .soma import detect_soma_centroid, get_label_centroid + +if TYPE_CHECKING: + from numpy.typing import ArrayLike + +__all__ = [ + 'CellAnalysisResult', + 'analyze_single_cell', + 'analyze_all_cells', + 'analyze_all_cells_generator', + 'aggregate_branch_stats', +] + + +@dataclass +class CellAnalysisResult: + """ + Container for single-cell morphology analysis results. + + Attributes + ---------- + label_id : int + Original label ID from the input image. + skeleton : skan.Skeleton + The skan.Skeleton object for this cell. + branches : pd.DataFrame + Branch properties from summarize_branches(). + sholl : ShollResult | None + Sholl analysis result, if soma was detected. + soma_centroid : np.ndarray | None + Soma centroid coordinates used for Sholl analysis. + summary : dict + Aggregate statistics for this cell. + """ + + label_id: int + skeleton: skan.Skeleton + branches: pd.DataFrame + sholl: ShollResult | None + soma_centroid: np.ndarray | None + summary: dict = field(default_factory=dict) + + def __post_init__(self): + """Compute summary statistics.""" + if not self.summary: + self.summary = self._compute_summary() + + def _compute_summary(self) -> dict: + """Compute aggregate statistics for the cell.""" + summary = { + 'label_id': self.label_id, + 'n_branches': len(self.branches), + 'total_length': self.branches['branch_distance'].sum(), + 'mean_branch_length': self.branches['branch_distance'].mean(), + } + + # Count by branch type + for bt in BranchType: + count = (self.branches['branch_type'] == bt.value).sum() + summary[f'n_{bt.name.lower()}'] = count + + # Tortuosity stats + if 'tortuosity' in self.branches.columns: + summary['mean_tortuosity'] = self.branches['tortuosity'].mean() + summary['max_tortuosity'] = self.branches['tortuosity'].max() + + # Sholl stats + if self.sholl is not None: + summary.update(self.sholl.to_dict()) + + return summary + + def to_dict(self) -> dict: + """Convert summary to dict for DataFrame creation.""" + return self.summary + + +def analyze_single_cell( + labels: ArrayLike, + label_id: int | None = None, + *, + spacing: tuple[float, ...] = (1.0, 1.0), + soma_labels: ArrayLike | None = None, + intensity_image: ArrayLike | None = None, + run_sholl: bool = True, + sholl_step: float = 1.0, +) -> CellAnalysisResult: + """ + Comprehensive morphology analysis of a single labeled cell. + + Performs skeletonization, branch analysis, and optionally Sholl analysis + on a single cell identified by label_id. + + Parameters + ---------- + labels : ArrayLike + Label image. If label_id is None, treats all non-zero pixels as one cell. + label_id : int, optional + Specific label ID to analyze. If None, uses the entire non-zero region. + spacing : tuple of float + Physical pixel spacing (Y, X) or (Z, Y, X). Default is (1.0, 1.0). + soma_labels : ArrayLike, optional + Separate label image for soma detection (e.g., DAPI-based nuclei). + If not provided, uses centroid of the cell mask. + intensity_image : ArrayLike, optional + Intensity image for measuring signal along branches. + run_sholl : bool + Whether to perform Sholl analysis. Default is True. + sholl_step : float + Step size for Sholl radii. Default is 1.0 (in physical units). + + Returns + ------- + CellAnalysisResult + Container with skeleton, branches, sholl, and summary statistics. + + Examples + -------- + >>> from ndev_morphology import analyze_single_cell + >>> result = analyze_single_cell( + ... cell_labels, + ... label_id=1, + ... spacing=(0.2, 0.2), + ... soma_labels=nuclei_labels, + ... ) + >>> print(f"Total length: {result.summary['total_length']:.1f} µm") + >>> print(f"Branch count: {result.summary['n_branches']}") + """ + labels = np.asarray(labels) + + # Extract single cell mask + if label_id is not None: + cell_mask = (labels == label_id).astype(np.uint16) + cell_mask[cell_mask > 0] = label_id + else: + cell_mask = (labels > 0).astype(np.uint16) + label_id = 1 + cell_mask[cell_mask > 0] = label_id + + # Skeletonize + skeleton_labels = skeletonize_labels(cell_mask) + skeleton_binary = skeleton_labels > 0 + + # Check if skeleton is empty + if not np.any(skeleton_binary): + raise ValueError( + f'Label {label_id} has no skeleton pixels. ' + f'The cell may be too small or have no elongated structure.' + ) + + # Create skan.Skeleton + skeleton = skan.Skeleton(skeleton_binary.astype(float), spacing=spacing) + + # Summarize branches + branches = summarize_branches( + skeleton, + intensity_image=intensity_image, + ) + branches['label_id'] = label_id + + # Detect soma centroid + soma_centroid = None + sholl_result = None + + if run_sholl: + try: + if soma_labels is not None: + soma_centroid = detect_soma_centroid( + soma_labels, + label_id=label_id, + method='largest_region', + ) + else: + soma_centroid = get_label_centroid(cell_mask, label_id) + + # Convert to physical units if spacing provided + soma_centroid_physical = soma_centroid * np.array(spacing) + + # Run Sholl analysis + sholl_result = compute_sholl_profile( + skeleton, + center=soma_centroid_physical, + step=sholl_step, + ) + except ValueError: + # No soma found or empty skeleton + pass + + return CellAnalysisResult( + label_id=label_id, + skeleton=skeleton, + branches=branches, + sholl=sholl_result, + soma_centroid=soma_centroid, + ) + + +def analyze_all_cells( + labels: ArrayLike, + *, + spacing: tuple[float, ...] = (1.0, 1.0), + soma_labels: ArrayLike | None = None, + intensity_image: ArrayLike | None = None, + run_sholl: bool = True, + sholl_step: float = 1.0, + min_skeleton_size: int = 10, +) -> tuple[pd.DataFrame, list[CellAnalysisResult]]: + """ + Analyze all labeled cells in an image. + + Iterates over each unique label and performs comprehensive morphology + analysis, returning both summary statistics and detailed results. + + Parameters + ---------- + labels : ArrayLike + Label image where each connected region has a unique integer ID. + spacing : tuple of float + Physical pixel spacing. Default is (1.0, 1.0). + soma_labels : ArrayLike, optional + Separate label image for soma detection. + intensity_image : ArrayLike, optional + Intensity image for measuring signal along branches. + run_sholl : bool + Whether to perform Sholl analysis. Default is True. + sholl_step : float + Step size for Sholl radii. Default is 1.0. + min_skeleton_size : int + Minimum skeleton size (in pixels) to include. Default is 10. + + Returns + ------- + summary_df : pd.DataFrame + DataFrame with one row per cell and columns for all summary metrics. + results : list of CellAnalysisResult + List of detailed results for each cell. + + Examples + -------- + >>> from ndev_morphology import analyze_all_cells + >>> summary, results = analyze_all_cells( + ... cell_labels, + ... spacing=(0.2, 0.2), + ... soma_labels=nuclei_labels, + ... ) + >>> # Save summary to CSV + >>> summary.to_csv('morphology_results.csv', index=False) + >>> # Access individual cell skeleton + >>> cell_1_skeleton = results[0].skeleton + """ + labels = np.asarray(labels) + + # Get unique label IDs (excluding 0) + label_ids = np.unique(labels) + label_ids = label_ids[label_ids > 0] + + results = [] + summaries = [] + + for label_id in label_ids: + try: + result = analyze_single_cell( + labels, + label_id=int(label_id), + spacing=spacing, + soma_labels=soma_labels, + intensity_image=intensity_image, + run_sholl=run_sholl, + sholl_step=sholl_step, + ) + + # Skip cells with too-small skeletons + if result.skeleton.n_paths < 1: + continue + + skeleton_size = np.sum(result.skeleton.path_lengths()) + if skeleton_size < min_skeleton_size: + continue + + results.append(result) + summaries.append(result.to_dict()) + + except Exception: + # Skip cells that fail analysis + continue + + summary_df = pd.DataFrame(summaries) if summaries else pd.DataFrame() + + return summary_df, results + + +def analyze_all_cells_generator( + labels: ArrayLike, + *, + spacing: tuple[float, ...] = (1.0, 1.0), + soma_labels: ArrayLike | None = None, + intensity_image: ArrayLike | None = None, + run_sholl: bool = True, + sholl_step: float = 1.0, + min_skeleton_size: int = 10, + on_progress: Callable[[int, int, int], None] | None = None, +): + """ + Generator version of analyze_all_cells for batch processing. + + Yields results one at a time, allowing for progress tracking + and cancellation. Compatible with nbatch.BatchRunner. + + Parameters + ---------- + labels : ArrayLike + Label image where each connected region has a unique integer ID. + spacing : tuple of float + Physical pixel spacing. Default is (1.0, 1.0). + soma_labels : ArrayLike, optional + Separate label image for soma detection. + intensity_image : ArrayLike, optional + Intensity image for measuring signal along branches. + run_sholl : bool + Whether to perform Sholl analysis. Default is True. + sholl_step : float + Step size for Sholl radii. Default is 1.0. + min_skeleton_size : int + Minimum skeleton size (in pixels) to include. Default is 10. + on_progress : callable, optional + Callback(current, total, label_id) called after each cell. + + Yields + ------ + CellAnalysisResult + Result for each successfully analyzed cell. + + Examples + -------- + >>> from ndev_morphology import analyze_all_cells_generator + >>> results = [] + >>> for result in analyze_all_cells_generator(labels, spacing=(0.2, 0.2)): + ... results.append(result) + ... print(f"Analyzed cell {result.label_id}") + """ + labels = np.asarray(labels) + + # Get unique label IDs (excluding 0) + label_ids = np.unique(labels) + label_ids = label_ids[label_ids > 0] + total = len(label_ids) + + for idx, label_id in enumerate(label_ids): + try: + result = analyze_single_cell( + labels, + label_id=int(label_id), + spacing=spacing, + soma_labels=soma_labels, + intensity_image=intensity_image, + run_sholl=run_sholl, + sholl_step=sholl_step, + ) + + # Skip cells with too-small skeletons + if result.skeleton.n_paths < 1: + continue + + skeleton_size = np.sum(result.skeleton.path_lengths()) + if skeleton_size < min_skeleton_size: + continue + + if on_progress is not None: + on_progress(idx + 1, total, int(label_id)) + + yield result + + except Exception: + # Skip cells that fail analysis + if on_progress is not None: + on_progress(idx + 1, total, int(label_id)) + continue + + +def aggregate_branch_stats( + results: list[CellAnalysisResult], +) -> pd.DataFrame: + """ + Combine branch DataFrames from multiple cells. + + Useful for population-level branch analysis. + + Parameters + ---------- + results : list of CellAnalysisResult + List of analysis results from analyze_all_cells or multiple + analyze_single_cell calls. + + Returns + ------- + pd.DataFrame + Combined DataFrame with all branches from all cells. + Includes 'label_id' column to identify source cell. + + Examples + -------- + >>> summary, results = analyze_all_cells(labels) + >>> all_branches = aggregate_branch_stats(results) + >>> # Plot branch length distribution + >>> all_branches['branch_distance'].hist() + """ + if not results: + return pd.DataFrame() + + all_branches = pd.concat( + [r.branches for r in results], + ignore_index=True, + ) + + return all_branches diff --git a/src/ndev_morphology/branch.py b/src/ndev_morphology/branch.py new file mode 100644 index 0000000..adbd35f --- /dev/null +++ b/src/ndev_morphology/branch.py @@ -0,0 +1,270 @@ +""" +Branch analysis for skeleton structures. + +Functions for computing branch-level measurements using skan, +including branch classification, tortuosity, and intensity measurements. +""" + +from __future__ import annotations + +from enum import IntEnum +from typing import TYPE_CHECKING + +import numpy as np +import pandas as pd +import skan + +if TYPE_CHECKING: + from numpy.typing import ArrayLike + +__all__ = [ + 'BranchType', + 'summarize_branches', + 'compute_tortuosity', + 'filter_branches_by_type', +] + + +class BranchType(IntEnum): + """ + Branch type enumeration matching skan's branch_type values. + + Attributes + ---------- + ENDPOINT_TO_ENDPOINT : int + Branch connecting two endpoints (isolated branch or terminal). + Value: 0 + JUNCTION_TO_ENDPOINT : int + Branch from a junction to an endpoint (terminal branch). + Value: 1 + JUNCTION_TO_JUNCTION : int + Branch connecting two junctions (internal branch). + Value: 2 + ISOLATED_CYCLE : int + Closed loop with no junctions. + Value: 3 + """ + + ENDPOINT_TO_ENDPOINT = 0 + JUNCTION_TO_ENDPOINT = 1 + JUNCTION_TO_JUNCTION = 2 + ISOLATED_CYCLE = 3 + + @classmethod + def label(cls, value: int) -> str: + """Get human-readable label for branch type value.""" + labels = { + 0: 'endpoint-endpoint', + 1: 'junction-endpoint', + 2: 'junction-junction', + 3: 'isolated-cycle', + } + return labels.get(value, 'unknown') + + +def summarize_branches( + skeleton: skan.Skeleton, + *, + intensity_image: ArrayLike | None = None, + compute_tortuosity: bool = True, +) -> pd.DataFrame: + """ + Summarize branch properties from a skeleton. + + Wraps `skan.summarize()` with additional computed metrics like tortuosity + and human-readable branch type labels. + + Parameters + ---------- + skeleton : skan.Skeleton + A skan.Skeleton object created with physical spacing. + For intensity measurements, create with source_image parameter: + `skan.Skeleton(skeleton_image, source_image=intensity_image)` + intensity_image : ArrayLike, optional + Image to sample for intensity measurements along branches. + If provided, adds mean_intensity/std_intensity columns. + Note: For best results, pass intensity_image to skan.Skeleton + constructor as source_image instead. + compute_tortuosity : bool, optional + Whether to compute tortuosity (path_length / euclidean_distance). + Default is True. + + Returns + ------- + pd.DataFrame + DataFrame with columns from skan.summarize() plus: + - 'branch_type_label': Human-readable branch type + - 'tortuosity': Path length / euclidean distance (if computed) + - 'mean_intensity', 'std_intensity': If intensity_image provided + or if skeleton has source_image + + Examples + -------- + >>> import skan + >>> from ndev_morphology import summarize_branches + >>> skeleton = skan.Skeleton(skeleton_image, spacing=(0.2, 0.2)) + >>> branches = summarize_branches(skeleton) + >>> # Filter to terminal branches only + >>> terminal = branches[branches['branch_type'] == 1] + + >>> # With intensity measurements + >>> skeleton = skan.Skeleton( + ... skeleton_image, spacing=(0.2, 0.2), source_image=intensity_image + ... ) + >>> branches = summarize_branches(skeleton) # Includes mean_pixel_value + """ + # Get base summary from skan + # Use separator='_' for consistent column names across skan versions + branches = skan.summarize(skeleton, separator='_') + + # Add human-readable branch type labels + branches['branch_type_label'] = branches['branch_type'].apply( + BranchType.label + ) + + # Compute tortuosity + if compute_tortuosity: + branches['tortuosity'] = _compute_tortuosity_series(branches) + + # Add intensity measurements if requested and not already present + if ( + intensity_image is not None + and 'mean_pixel_value' not in branches.columns + ): + intensity_image = np.asarray(intensity_image) + mean_intensities, std_intensities = _compute_path_intensities( + skeleton, intensity_image + ) + branches['mean_intensity'] = mean_intensities + branches['std_intensity'] = std_intensities + + return branches + + +def _compute_path_intensities( + skeleton: skan.Skeleton, + intensity_image: np.ndarray, +) -> tuple[np.ndarray, np.ndarray]: + """Compute mean and std intensity along each path.""" + means = [] + stds = [] + + for i in range(skeleton.n_paths): + coords = skeleton.path_coordinates(i).astype(int) + # Clip to image bounds + for dim in range(coords.shape[1]): + coords[:, dim] = np.clip( + coords[:, dim], 0, intensity_image.shape[dim] - 1 + ) + + # Sample intensity values + if coords.shape[1] == 2: + values = intensity_image[coords[:, 0], coords[:, 1]] + else: # 3D + values = intensity_image[coords[:, 0], coords[:, 1], coords[:, 2]] + + means.append(np.mean(values)) + stds.append(np.std(values)) + + return np.array(means), np.array(stds) + + +def _compute_tortuosity_series(branches: pd.DataFrame) -> pd.Series: + """ + Compute tortuosity for each branch. + + Tortuosity = branch_distance / euclidean_distance + A straight line has tortuosity = 1.0, curved branches > 1.0. + """ + # Avoid division by zero for very short branches + euclidean = branches['euclidean_distance'].replace(0, np.nan) + tortuosity = branches['branch_distance'] / euclidean + return tortuosity.fillna(1.0) + + +def compute_tortuosity( + branch_distance: ArrayLike, + euclidean_distance: ArrayLike, +) -> np.ndarray: + """ + Compute tortuosity from path and straight-line distances. + + Tortuosity = path_length / euclidean_distance + + A perfectly straight branch has tortuosity = 1.0. + More curved/tortuous branches have values > 1.0. + + Parameters + ---------- + branch_distance : ArrayLike + Path length along the branch (arc length). + euclidean_distance : ArrayLike + Straight-line distance between branch endpoints. + + Returns + ------- + np.ndarray + Tortuosity values. Returns 1.0 for branches with zero euclidean distance. + + Examples + -------- + >>> from ndev_morphology import compute_tortuosity + >>> tortuosity = compute_tortuosity( + ... branch_distance=[10.0, 15.0, 8.0], + ... euclidean_distance=[10.0, 10.0, 8.0], + ... ) + >>> # [1.0, 1.5, 1.0] + """ + branch_distance = np.asarray(branch_distance) + euclidean_distance = np.asarray(euclidean_distance) + + # Avoid division by zero + with np.errstate(divide='ignore', invalid='ignore'): + tortuosity = branch_distance / euclidean_distance + tortuosity = np.where( + euclidean_distance == 0, + 1.0, + tortuosity, + ) + + return tortuosity + + +def filter_branches_by_type( + branches: pd.DataFrame, + branch_types: int | list[int] | BranchType | list[BranchType], +) -> pd.DataFrame: + """ + Filter branch DataFrame to specific branch types. + + Parameters + ---------- + branches : pd.DataFrame + Branch summary DataFrame from `summarize_branches()` or `skan.summarize()`. + branch_types : int, list of int, BranchType, or list of BranchType + Branch type(s) to keep. Can use BranchType enum or raw int values. + + Returns + ------- + pd.DataFrame + Filtered DataFrame containing only specified branch types. + + Examples + -------- + >>> from ndev_morphology import summarize_branches, filter_branches_by_type, BranchType + >>> branches = summarize_branches(skeleton) + >>> # Get only terminal branches (junction to endpoint) + >>> terminal = filter_branches_by_type(branches, BranchType.JUNCTION_TO_ENDPOINT) + >>> # Get terminal and internal branches + >>> main_branches = filter_branches_by_type( + ... branches, + ... [BranchType.JUNCTION_TO_ENDPOINT, BranchType.JUNCTION_TO_JUNCTION], + ... ) + """ + # Normalize to list of ints + if isinstance(branch_types, int | BranchType): + branch_types = [int(branch_types)] + else: + branch_types = [int(bt) for bt in branch_types] + + return branches[branches['branch_type'].isin(branch_types)].copy() diff --git a/src/ndev_morphology/labels.py b/src/ndev_morphology/labels.py new file mode 100644 index 0000000..729057e --- /dev/null +++ b/src/ndev_morphology/labels.py @@ -0,0 +1,150 @@ +""" +Label preparation operations for morphology analysis. + +Functions for filtering, cleaning, and preparing label images +before skeletonization. All functions accept numpy arrays and +use scikit-image/scipy for processing. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +from skimage import measure, morphology +from skimage.segmentation import clear_border + +if TYPE_CHECKING: + from numpy.typing import ArrayLike + +__all__ = [ + 'filter_labels_by_size', + 'exclude_labels_on_edges', + 'connect_breaks_between_labels', +] + + +def filter_labels_by_size( + labels: ArrayLike, + min_size: int | None = None, + max_size: int | None = None, +) -> np.ndarray: + """ + Filter labels by area (number of pixels). + + Parameters + ---------- + labels : ArrayLike + Label image. + min_size : int, optional + Minimum area in pixels. Labels smaller than this are removed. + max_size : int, optional + Maximum area in pixels. Labels larger than this are removed. + + Returns + ------- + np.ndarray + Filtered label image with same dtype as input. + + Examples + -------- + >>> from ndev_morphology import filter_labels_by_size + >>> # Keep only labels between 100 and 10000 pixels + >>> filtered = filter_labels_by_size(labels, min_size=100, max_size=10000) + """ + labels = np.asarray(labels) + output = labels.copy() + + if min_size is None and max_size is None: + return output + + props = measure.regionprops(labels.astype(np.int32)) + + for prop in props: + area = prop.area + remove = False + + if min_size is not None and area < min_size: + remove = True + if max_size is not None and area > max_size: + remove = True + + if remove: + output[labels == prop.label] = 0 + + return output + + +def exclude_labels_on_edges(labels: ArrayLike) -> np.ndarray: + """ + Remove labels that touch the image border. + + Parameters + ---------- + labels : ArrayLike + Label image. + + Returns + ------- + np.ndarray + Label image with edge-touching labels removed. + + Examples + -------- + >>> from ndev_morphology import exclude_labels_on_edges + >>> filtered = exclude_labels_on_edges(labels) + """ + labels = np.asarray(labels) + return morphology.remove_small_objects(clear_border(labels), min_size=0) + + +def connect_breaks_between_labels( + labels: ArrayLike, + connect_distance: float, +) -> np.ndarray: + """ + Connect breaks between labels within a specified distance. + + Dilates labels, merges touching ones, then masks back to original label + extent. This connects nearby label fragments without changing overall + morphology. + + Parameters + ---------- + labels : ArrayLike + Label image. + connect_distance : float + Maximum distance (in pixels) to connect labels. Labels are dilated + by connect_distance/2 before merging. + + Returns + ------- + np.ndarray + Label image with connected labels. New merged labels may have + different IDs than originals. + + Examples + -------- + >>> from ndev_morphology import connect_breaks_between_labels + >>> # Connect labels within 3 pixels of each other + >>> connected = connect_breaks_between_labels(labels, connect_distance=3.0) + """ + labels = np.asarray(labels) + original_mask = labels > 0 + + # Dilate labels using distance transform approach + radius = int(np.ceil(connect_distance / 2)) + if radius < 1: + return labels.copy() + + # Create dilated version using morphological dilation + dilated = morphology.dilation(labels, footprint=morphology.disk(radius)) + + # Relabel to merge touching components + binary_dilated = dilated > 0 + merged = measure.label(binary_dilated, connectivity=2) + + # Mask back to original label extent + result = (merged * original_mask).astype(labels.dtype) + + return result diff --git a/src/ndev_morphology/model.py b/src/ndev_morphology/model.py new file mode 100644 index 0000000..885f13b --- /dev/null +++ b/src/ndev_morphology/model.py @@ -0,0 +1,400 @@ +""" +Central data model for morphology analysis and visualization. + +This module provides `MorphologyModel`, a unified data model that: +- Wraps skan.Skeleton with napari-specific context +- Caches computed results (summary, Sholl, branches, trees) +- Attaches to napari layers via layer.metadata +- Provides a clean API for widgets + +Usage +----- +>>> from ndev_morphology.model import MorphologyModel +>>> # From a napari layer +>>> model = MorphologyModel.from_layer(skeleton_layer) +>>> # Access cached skeleton and summary +>>> branches = model.summary +>>> # Compute branch analysis (cached) +>>> branch_df = model.compute_branches() + +The model follows the pattern used by napari-swc-editor for storing +structured data in layer.metadata. + +Design Philosophy +----------------- +MorphologyModel is a **convenience wrapper**, not a processing state machine. + +The processing pipeline should be explicit and replayable: + + segmentation = load_labels("neuron.tif") + skeleton = skeletonize_labels(segmentation) + skeleton_pruned = prune_short_branches(skeleton, min_length=5) + + # Don't like the pruning? Start fresh: + skeleton_alt = prune_short_branches(skeleton, min_length=10) + + # The model wraps the FINAL result for visualization/export: + model = MorphologyModel.from_array(skeleton_pruned, spacing=(0.2, 0.2)) + +The model does NOT: +- Track undo/redo history +- Manage mutable processing pipelines +- Replace explicit function calls +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Any + +import numpy as np +import pandas as pd +import skan + +from .branch import summarize_branches +from .sholl import ShollResult + +if TYPE_CHECKING: + import napari + from numpy.typing import ArrayLike + +__all__ = [ + 'MorphologyModel', + 'get_model_from_layer', + 'attach_model_to_layer', + # Backwards compatibility aliases + 'MorphologyAnalysis', + 'get_analysis_from_layer', + 'attach_analysis_to_layer', +] + +# Key used to store MorphologyModel in layer.metadata +METADATA_KEY = 'morphology_model' + + +@dataclass +class MorphologyModel: + """ + Unified data model for skeleton morphology analysis and visualization. + + Wraps skan.Skeleton with computed analysis results and napari context. + Designed to be stored in layer.metadata['morphology_model']. + + Parameters + ---------- + skeleton : skan.Skeleton + The skan Skeleton object. + spacing : tuple of float + Physical pixel spacing used to create the skeleton. + source_layer_name : str, optional + Name of the napari layer this was created from. + + Attributes + ---------- + skeleton : skan.Skeleton + The underlying skan Skeleton object. + spacing : tuple of float + Physical pixel spacing (e.g., (0.2, 0.2) for 200nm pixels). + source_layer_name : str + Name of the source napari layer. + summary : pd.DataFrame + Branch summary from skan.summarize() (lazily computed). + + Examples + -------- + >>> import skan + >>> from ndev_morphology.model import MorphologyModel + >>> skeleton = skan.Skeleton(binary_image, spacing=(0.2, 0.2)) + >>> model = MorphologyModel(skeleton, spacing=(0.2, 0.2)) + >>> print(f"Branches: {len(model.summary)}") + >>> branches = model.compute_branches() + """ + + skeleton: skan.Skeleton + spacing: tuple[float, ...] + source_layer_name: str = '' + + # Cached results (computed lazily) + _summary: pd.DataFrame | None = field(default=None, repr=False) + _branch_summary: pd.DataFrame | None = field(default=None, repr=False) + _sholl_results: dict[str, ShollResult] = field( + default_factory=dict, repr=False + ) + _directed_trees: list[Any] = field( + default_factory=list, repr=False + ) # Any to avoid circular import + + @classmethod + def from_array( + cls, + skeleton_array: ArrayLike, + *, + spacing: tuple[float, ...] = (1.0, 1.0), + source_name: str = '', + ) -> MorphologyModel: + """ + Create MorphologyModel from a numpy array. + + Parameters + ---------- + skeleton_array : ArrayLike + Binary or labeled skeleton image. + spacing : tuple of float, optional + Physical pixel spacing (default: (1.0, 1.0)). + source_name : str, optional + Name to identify the source (default: ''). + + Returns + ------- + MorphologyModel + New model object. + + Examples + -------- + >>> model = MorphologyModel.from_array( + ... skeleton_image, spacing=(0.2, 0.2), source_name='neuron_1' + ... ) + """ + arr = np.asarray(skeleton_array) + skeleton = skan.Skeleton(arr.astype(float), spacing=spacing) + return cls( + skeleton=skeleton, + spacing=spacing, + source_layer_name=source_name, + ) + + @classmethod + def from_layer( + cls, + layer: napari.layers.Labels, + *, + force_recompute: bool = False, + ) -> MorphologyModel: + """ + Create or retrieve MorphologyModel from a napari Labels layer. + + If the layer already has a cached model in its metadata, + that cached version is returned (unless force_recompute=True). + + Parameters + ---------- + layer : napari.layers.Labels + The napari Labels layer containing the skeleton. + force_recompute : bool, optional + If True, create a new model even if one is cached. + Default is False. + + Returns + ------- + MorphologyModel + The model object (new or cached). + + Examples + -------- + >>> # First call creates and caches + >>> model = MorphologyModel.from_layer(skeleton_layer) + >>> # Second call returns cached version + >>> same_model = MorphologyModel.from_layer(skeleton_layer) + >>> assert model is same_model + >>> # Force recomputation + >>> new_model = MorphologyModel.from_layer( + ... skeleton_layer, force_recompute=True + ... ) + """ + # Check cache first + if not force_recompute and METADATA_KEY in layer.metadata: + cached = layer.metadata[METADATA_KEY] + if isinstance(cached, MorphologyModel): + return cached + + # Create new model + arr = np.asarray(layer.data) + spacing = tuple(layer.scale[-2:]) + skeleton = skan.Skeleton(arr.astype(float), spacing=spacing) + + model = cls( + skeleton=skeleton, + spacing=spacing, + source_layer_name=layer.name, + ) + + # Cache on layer + layer.metadata[METADATA_KEY] = model + return model + + @property + def summary(self) -> pd.DataFrame: + """ + Lazily compute and cache skan summary. + + Returns + ------- + pd.DataFrame + Branch summary with columns like 'branch_distance', + 'euclidean_distance', 'branch_type', 'node_id_src', etc. + """ + if self._summary is None: + self._summary = skan.summarize(self.skeleton, separator='_') + return self._summary + + @property + def n_branches(self) -> int: + """Number of branches in the skeleton.""" + return len(self.summary) + + @property + def total_length(self) -> float: + """Total branch length in physical units.""" + return float(self.summary['branch_distance'].sum()) + + @property + def n_tips(self) -> int: + """Number of endpoint branches (branch_type 0 or 1).""" + return int((self.summary['branch_type'] <= 1).sum()) + + @property + def n_junctions(self) -> int: + """Number of junction-junction branches (branch_type 2).""" + return int((self.summary['branch_type'] == 2).sum()) + + def compute_branches( + self, intensity_image: ArrayLike | None = None + ) -> pd.DataFrame: + """ + Compute or retrieve branch summary with additional metrics. + + This extends skan.summarize() with computed metrics like + tortuosity. + + Parameters + ---------- + intensity_image : ArrayLike, optional + Intensity image for measuring mean intensity along branches. + + Returns + ------- + pd.DataFrame + Extended branch summary with tortuosity, etc. + + Notes + ----- + The result is cached. If called multiple times, the first + computed result is returned (unless the skeleton is recreated). + """ + if self._branch_summary is None: + self._branch_summary = summarize_branches( + self.skeleton, intensity_image=intensity_image + ) + return self._branch_summary + + def get_sholl_result(self, center_key: str) -> ShollResult | None: + """ + Get cached Sholl result for a center point. + + Parameters + ---------- + center_key : str + Key identifying the center (e.g., "100.0,150.0"). + + Returns + ------- + ShollResult or None + Cached result or None if not computed. + """ + return self._sholl_results.get(center_key) + + def add_sholl_result(self, center_key: str, result: ShollResult) -> None: + """ + Cache a Sholl result. + + Parameters + ---------- + center_key : str + Key identifying the center (e.g., "100.0,150.0"). + result : ShollResult + The computed Sholl result to cache. + """ + self._sholl_results[center_key] = result + + @property + def sholl_results(self) -> dict[str, ShollResult]: + """All cached Sholl results.""" + return self._sholl_results.copy() + + def add_directed_trees(self, trees: list) -> None: + """ + Store directed tree analysis results. + + Parameters + ---------- + trees : list of DirectedTree + Trees to cache. + """ + self._directed_trees.extend(trees) + + def clear_directed_trees(self) -> None: + """Clear cached directed trees.""" + self._directed_trees.clear() + + @property + def directed_trees(self) -> list: + """Get cached directed trees (list of DirectedTree).""" + return self._directed_trees + + def clear_cache(self) -> None: + """Clear all cached computation results.""" + self._summary = None + self._branch_summary = None + self._sholl_results.clear() + self._directed_trees.clear() + + def __repr__(self) -> str: + """Return a string representation.""" + return ( + f'MorphologyModel(' + f'n_branches={self.n_branches}, ' + f'total_length={self.total_length:.1f}, ' + f"source='{self.source_layer_name}')" + ) + + +def get_model_from_layer( + layer: napari.layers.Labels, +) -> MorphologyModel | None: + """ + Get MorphologyModel from a layer's metadata if it exists. + + Parameters + ---------- + layer : napari.layers.Labels + The napari layer to check. + + Returns + ------- + MorphologyModel or None + The cached model, or None if not present. + """ + return layer.metadata.get(METADATA_KEY) + + +def attach_model_to_layer( + layer: napari.layers.Labels, + model: MorphologyModel, +) -> None: + """ + Attach a MorphologyModel to a layer's metadata. + + Parameters + ---------- + layer : napari.layers.Labels + The napari layer. + model : MorphologyModel + The model to attach. + """ + layer.metadata[METADATA_KEY] = model + + +# Backwards compatibility aliases +MorphologyAnalysis = MorphologyModel +get_analysis_from_layer = get_model_from_layer +attach_analysis_to_layer = attach_model_to_layer diff --git a/src/ndev_morphology/napari.yaml b/src/ndev_morphology/napari.yaml index 10fa9ec..748d445 100644 --- a/src/ndev_morphology/napari.yaml +++ b/src/ndev_morphology/napari.yaml @@ -1,8 +1,83 @@ name: ndev-morphology display_name: ndev Morphology -# use 'hidden' to remove plugin from napari hub search results visibility: public -# see https://napari.org/stable/plugins/technical_references/manifest.html#fields for valid categories -# categories: [] +categories: [Annotation, Segmentation] contributions: commands: + - id: ndev-morphology.refine_labels + title: Refine Labels + python_name: ndev_morphology.widgets:refine_labels_widget + - id: ndev-morphology.skeletonize_labels + title: Skeletonize Labels + python_name: ndev_morphology.widgets:skeletonize_labels_widget + - id: ndev-morphology.prune_skeleton + title: Prune Skeleton + python_name: ndev_morphology.widgets:prune_skeleton_widget + - id: ndev-morphology.skeleton_to_shapes + title: Skeleton to Shapes + python_name: ndev_morphology.widgets:skeleton_to_shapes + - id: ndev-morphology.skeleton_graph + title: Skeleton Graph + python_name: ndev_morphology.widgets:skeleton_graph + - id: ndev-morphology.sholl_analysis + title: Sholl Analysis + python_name: ndev_morphology.widgets:sholl_analysis + - id: ndev-morphology.soma_detection + title: Soma Detection + python_name: ndev_morphology.widgets:soma_detection + - id: ndev-morphology.branch_analysis + title: Branch Analysis + python_name: ndev_morphology.widgets:branch_analysis + - id: ndev-morphology.cell_analysis + title: Cell Analysis + python_name: ndev_morphology.widgets:cell_analysis + - id: ndev-morphology.directed_tree + title: Directed Tree Analysis + python_name: ndev_morphology.widgets:directed_tree_analysis + - id: ndev-morphology.batch_analysis + title: Batch Analysis + python_name: ndev_morphology.widgets:BatchAnalysisWidget + widgets: + - command: ndev-morphology.refine_labels + display_name: Refine Labels + - command: ndev-morphology.skeletonize_labels + display_name: Skeletonize Labels + - command: ndev-morphology.prune_skeleton + display_name: Prune Skeleton + - command: ndev-morphology.skeleton_to_shapes + display_name: Skeleton to Shapes + - command: ndev-morphology.skeleton_graph + display_name: Skeleton Graph + - command: ndev-morphology.sholl_analysis + display_name: Sholl Analysis + - command: ndev-morphology.soma_detection + display_name: Soma Detection + - command: ndev-morphology.branch_analysis + display_name: Branch Analysis + - command: ndev-morphology.cell_analysis + display_name: Cell Analysis + - command: ndev-morphology.directed_tree + display_name: Directed Tree Analysis + - command: ndev-morphology.batch_analysis + display_name: Batch Analysis + # npe2 menu contributions for ndev-assistant discovery + menus: + # Register our submenu under napari's plugins menu + napari/plugins: + - submenu: ndev-morphology + # Define our operations in our submenu + ndev-morphology: + - command: ndev-morphology.refine_labels + - command: ndev-morphology.skeletonize_labels + - command: ndev-morphology.prune_skeleton + - command: ndev-morphology.skeleton_to_shapes + - command: ndev-morphology.skeleton_graph + - command: ndev-morphology.sholl_analysis + - command: ndev-morphology.soma_detection + - command: ndev-morphology.branch_analysis + - command: ndev-morphology.cell_analysis + - command: ndev-morphology.directed_tree + - command: ndev-morphology.batch_analysis + submenus: + - id: ndev-morphology + label: Morphology (ndev) diff --git a/src/ndev_morphology/pruning.py b/src/ndev_morphology/pruning.py new file mode 100644 index 0000000..5f0a084 --- /dev/null +++ b/src/ndev_morphology/pruning.py @@ -0,0 +1,235 @@ +""" +Skeleton pruning operations. + +Functions for removing unwanted branches from skeletons, such as +short spurious branches created during skeletonization. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import pandas as pd +import skan + +if TYPE_CHECKING: + from numpy.typing import ArrayLike + +__all__ = [ + 'prune_short_branches', + 'remove_isolated_cycles', + 'BranchType', +] + + +class BranchType: + """ + Branch type constants from skan. + + Branch types describe the topology of each branch based on its endpoints: + - ENDPOINT_TO_ENDPOINT (0): An isolated branch with endpoints on both ends + - JUNCTION_TO_ENDPOINT (1): One end is a junction, one is an endpoint (terminal branch) + - JUNCTION_TO_JUNCTION (2): Both ends are junctions (connecting branch) + - ISOLATED_CYCLE (3): A closed loop with no endpoints + """ + + ENDPOINT_TO_ENDPOINT = 0 + JUNCTION_TO_ENDPOINT = 1 + JUNCTION_TO_JUNCTION = 2 + ISOLATED_CYCLE = 3 + + +def prune_short_branches( + skeleton: skan.Skeleton, + min_branch_distance: float = 10.0, + *, + prune_endpoint_to_endpoint: bool = True, + prune_junction_to_endpoint: bool = True, + prune_junction_to_junction: bool = False, + prune_isolated_cycles: bool = True, +) -> tuple[skan.Skeleton, pd.DataFrame]: + """ + Remove short branches from a skeleton. + + Uses skan's `prune_paths()` to remove branches shorter than the + specified minimum distance. You can control which branch types + are eligible for pruning. + + Parameters + ---------- + skeleton : skan.Skeleton + The skeleton to prune. + min_branch_distance : float + Minimum branch length to keep. Branches shorter than this + (of the selected types) will be removed. + prune_endpoint_to_endpoint : bool + Whether to prune isolated branches (type 0). These are branches + that don't connect to anything else - usually artifacts. + prune_junction_to_endpoint : bool + Whether to prune terminal branches (type 1). These are branches + that end in an endpoint - the most common spurious branches. + prune_junction_to_junction : bool + Whether to prune connecting branches (type 2). Usually you want + to keep these as they connect major parts of the skeleton. + prune_isolated_cycles : bool + Whether to prune closed loops (type 3). These are usually + artifacts from holes in the original image. + + Returns + ------- + pruned_skeleton : skan.Skeleton + New skeleton with short branches removed. + summary : pd.DataFrame + Summary of remaining branches from skan.summarize(). + + Notes + ----- + The pruning process: + 1. Summarizes the skeleton to get branch properties + 2. Identifies branches that are both too short AND of a prunable type + 3. Calls `skeleton.prune_paths()` to remove those branches + 4. Re-summarizes the pruned skeleton + + After pruning, junction resolution may no longer be optimal. For + best results, consider re-skeletonizing the result if accuracy is + critical (though this is slower). + + Examples + -------- + >>> import skan + >>> from ndev_morphology import prune_short_branches + >>> # Create skeleton from binary image + >>> skeleton = skan.Skeleton(skeleton_image, spacing=(0.2, 0.2)) + >>> # Prune branches shorter than 5 microns + >>> pruned, summary = prune_short_branches(skeleton, min_branch_distance=5.0) + >>> print(f"Removed {skeleton.n_paths - pruned.n_paths} short branches") + """ + # Get branch summary + summary = skan.summarize(skeleton, separator='_') + + # Identify short branches + too_short = summary['branch_distance'] < min_branch_distance + + # Identify prunable branch types + types_to_prune = [] + if prune_endpoint_to_endpoint: + types_to_prune.append(BranchType.ENDPOINT_TO_ENDPOINT) + if prune_junction_to_endpoint: + types_to_prune.append(BranchType.JUNCTION_TO_ENDPOINT) + if prune_junction_to_junction: + types_to_prune.append(BranchType.JUNCTION_TO_JUNCTION) + if prune_isolated_cycles: + types_to_prune.append(BranchType.ISOLATED_CYCLE) + + # Branches of prunable types + prunable_type = summary['branch_type'].isin(types_to_prune) + + # Prune branches that are BOTH too short AND of a prunable type + to_prune = too_short & prunable_type + prune_indices = np.flatnonzero(to_prune) + + if len(prune_indices) == 0: + # Nothing to prune + return skeleton, summary + + # Use skan's prune_paths to remove branches + pruned_skeleton = skeleton.prune_paths(prune_indices) + + # Re-summarize the pruned skeleton + pruned_summary = skan.summarize(pruned_skeleton, separator='_') + + return pruned_skeleton, pruned_summary + + +def remove_isolated_cycles( + skeleton: skan.Skeleton, +) -> tuple[skan.Skeleton, pd.DataFrame]: + """ + Remove all isolated cycles (closed loops) from a skeleton. + + Isolated cycles are branches of type 3 in skan's classification. + They often occur at junction points or from small holes in the + original image. + + Parameters + ---------- + skeleton : skan.Skeleton + The skeleton to process. + + Returns + ------- + pruned_skeleton : skan.Skeleton + Skeleton with isolated cycles removed. + summary : pd.DataFrame + Summary of remaining branches. + + Examples + -------- + >>> from ndev_morphology import remove_isolated_cycles + >>> pruned, summary = remove_isolated_cycles(skeleton) + """ + summary = skan.summarize(skeleton, separator='_') + + # Find all isolated cycles + is_cycle = summary['branch_type'] == BranchType.ISOLATED_CYCLE + cycle_indices = np.flatnonzero(is_cycle) + + if len(cycle_indices) == 0: + return skeleton, summary + + pruned_skeleton = skeleton.prune_paths(cycle_indices) + pruned_summary = skan.summarize(pruned_skeleton, separator='_') + + return pruned_skeleton, pruned_summary + + +def prune_skeleton_to_image( + skeleton: skan.Skeleton, + prune_indices: ArrayLike, + shape: tuple[int, ...] | None = None, +) -> np.ndarray: + """ + Prune a skeleton and convert back to a binary image. + + This is useful when you want to re-skeletonize after pruning + to get optimal junction resolution. + + Parameters + ---------- + skeleton : skan.Skeleton + The skeleton to prune. + prune_indices : ArrayLike + Indices of branches to remove. + shape : tuple, optional + Shape of output image. If None, uses skeleton.coordinates bounds. + + Returns + ------- + np.ndarray + Binary skeleton image with pruned branches removed. + + Notes + ----- + To get a truly clean skeleton after pruning, you can: + 1. Use this function to get a binary image + 2. Optionally dilate slightly then re-skeletonize + 3. Create a new skan.Skeleton from the result + + Examples + -------- + >>> from ndev_morphology.pruning import prune_skeleton_to_image + >>> # Get branches to prune + >>> summary = skan.summarize(skeleton) + >>> too_short = summary['branch_distance'] < 5.0 + >>> # Create pruned image + >>> pruned_image = prune_skeleton_to_image(skeleton, np.flatnonzero(too_short)) + """ + prune_indices = np.asarray(prune_indices) + + if len(prune_indices) == 0: + # Return all paths as image + return np.asarray(skeleton).astype(bool) + + pruned = skeleton.prune_paths(prune_indices) + return np.asarray(pruned).astype(bool) diff --git a/src/ndev_morphology/sholl.py b/src/ndev_morphology/sholl.py new file mode 100644 index 0000000..29f396b --- /dev/null +++ b/src/ndev_morphology/sholl.py @@ -0,0 +1,189 @@ +""" +Sholl analysis for skeleton structures. + +Wraps skan.sholl_analysis with convenient result dataclass and summary statistics. +Sholl analysis counts the number of skeleton crossings at concentric shells +from a center point (typically the soma/cell body of a neuron). +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING + +import numpy as np +import skan + +if TYPE_CHECKING: + from numpy.typing import ArrayLike + +__all__ = [ + 'ShollResult', + 'compute_sholl_profile', +] + + +@dataclass +class ShollResult: + """ + Result container for Sholl analysis. + + Attributes + ---------- + center : np.ndarray + Center point coordinates used for analysis. + radii : np.ndarray + Shell radii at which crossings were counted. + counts : np.ndarray + Number of skeleton crossings at each radius. + max_crossings : int + Maximum number of crossings (peak of Sholl profile). + critical_radius : float + Radius at which maximum crossings occur. + enclosing_radius : float + Largest radius with non-zero crossings. + mean_crossings : float + Mean number of crossings across all radii. + total_crossings : int + Sum of all crossings (related to total branch complexity). + """ + + center: np.ndarray + radii: np.ndarray + counts: np.ndarray + max_crossings: int = field(init=False) + critical_radius: float = field(init=False) + enclosing_radius: float = field(init=False) + mean_crossings: float = field(init=False) + total_crossings: int = field(init=False) + + def __post_init__(self): + """Compute derived statistics from counts.""" + self.max_crossings = ( + int(np.max(self.counts)) if len(self.counts) > 0 else 0 + ) + self.total_crossings = int(np.sum(self.counts)) + self.mean_crossings = ( + float(np.mean(self.counts)) if len(self.counts) > 0 else 0.0 + ) + + if self.max_crossings > 0: + max_idx = np.argmax(self.counts) + self.critical_radius = float(self.radii[max_idx]) + else: + self.critical_radius = 0.0 + + # Find enclosing radius (last radius with non-zero crossings) + nonzero_indices = np.nonzero(self.counts)[0] + if len(nonzero_indices) > 0: + self.enclosing_radius = float(self.radii[nonzero_indices[-1]]) + else: + self.enclosing_radius = 0.0 + + def to_dict(self) -> dict: + """Convert result to dictionary for DataFrame creation.""" + return { + 'center': self.center.tolist(), + 'max_crossings': self.max_crossings, + 'critical_radius': self.critical_radius, + 'enclosing_radius': self.enclosing_radius, + 'mean_crossings': self.mean_crossings, + 'total_crossings': self.total_crossings, + } + + +def compute_sholl_profile( + skeleton: skan.Skeleton, + center: ArrayLike, + radii: ArrayLike | None = None, + *, + min_radius: float = 0.0, + max_radius: float | None = None, + step: float = 1.0, +) -> ShollResult: + """ + Perform Sholl analysis on a skeleton. + + Counts the number of skeleton branch crossings at concentric circular (2D) + or spherical (3D) shells centered on a given point. + + Parameters + ---------- + skeleton : skan.Skeleton + A skan.Skeleton object. Create one using `create_skeleton()`. + If the skeleton was created with physical spacing, center and radii + should be in the same physical units. + center : ArrayLike + Center point coordinates for Sholl analysis, typically the soma/nucleus + centroid. Should be in the same units as the skeleton's coordinate system. + radii : ArrayLike, optional + Explicit array of radii at which to count crossings. + If not provided, radii are generated from min_radius to max_radius with step. + min_radius : float, optional + Minimum shell radius. Default is 0. + max_radius : float, optional + Maximum shell radius. If None, estimated from skeleton extent. + step : float, optional + Step size between radii. Default is 1.0. + + Returns + ------- + ShollResult + Dataclass containing radii, counts, and summary statistics. + + Examples + -------- + >>> from ndev_morphology import create_skeleton, compute_sholl_profile + >>> skel = create_skeleton(skeleton_image, spacing=(0.2, 0.2)) + >>> # Center is in physical units (µm) since spacing was provided + >>> center = np.array([10.0, 12.0]) # Y, X in µm + >>> result = compute_sholl_profile(skel, center, min_radius=1, max_radius=50, step=1) + >>> print(f"Max crossings: {result.max_crossings} at {result.critical_radius} µm") + + Notes + ----- + The center coordinates should match the units of the skeleton. If the skeleton + was created with physical spacing, center should be in physical units. + If no spacing was provided (pixel units), center should be in pixels. + """ + center = np.asarray(center) + + # Handle empty skeleton (no paths) + if skeleton.n_paths == 0: + # Empty skeleton - return result with zero counts + if radii is None: + if max_radius is None: + max_radius = 100.0 # Default fallback + radii = np.arange(min_radius, max_radius, step) + radii = np.asarray(radii) + return ShollResult( + center=center, + radii=radii, + counts=np.zeros(len(radii), dtype=int), + ) + + # Generate radii if not provided + if radii is None: + if max_radius is None: + # Estimate max radius from skeleton extent + all_coords = np.vstack( + [skeleton.path_coordinates(i) for i in range(skeleton.n_paths)] + ) + # If spacing was used, coordinates are in physical units + distances = np.linalg.norm(all_coords - center, axis=1) + max_radius = float(np.max(distances)) * 1.1 # 10% margin + + radii = np.arange(min_radius, max_radius, step) + + radii = np.asarray(radii) + + # Perform Sholl analysis using skan + center_result, radii_result, counts = skan.sholl_analysis( + skeleton, center=center, shells=radii + ) + + return ShollResult( + center=center_result, + radii=radii_result, + counts=counts, + ) diff --git a/src/ndev_morphology/skeleton.py b/src/ndev_morphology/skeleton.py new file mode 100644 index 0000000..7b21f21 --- /dev/null +++ b/src/ndev_morphology/skeleton.py @@ -0,0 +1,235 @@ +""" +Skeleton operations for morphology analysis. + +Functions for creating and manipulating skeletons from label images. +All functions accept numpy arrays and use scikit-image/scipy for processing. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +from scipy import ndimage +from skimage import morphology + +if TYPE_CHECKING: + from numpy.typing import ArrayLike + +__all__ = [ + 'skeletonize_labels', + 'separate_touching_skeleton_labels', + 'exclude_region_from_skeleton', + 'fill_skeleton_gaps', +] + + +def skeletonize_labels(labels: ArrayLike) -> np.ndarray: + """ + Create skeletons while maintaining label identities from a label image. + + Each labeled region is skeletonized independently and the skeleton pixels + retain their original label values; normally, scikit-image's morphology.skeletonize + produces a binary skeleton image without label information. + + Parameters + ---------- + labels : ArrayLike + Label image where each unique non-zero value represents a distinct object. + + Returns + ------- + np.ndarray + Skeletonized label image with same dtype as input labels. + Background (0) pixels remain 0, skeleton pixels have original label values. + + Examples + -------- + >>> import numpy as np + >>> from ndev_morphology import skeletonize_labels + >>> labels = np.array([[0, 1, 1, 1], + ... [0, 1, 0, 1], + ... [0, 1, 1, 1]]) + >>> skeleton = skeletonize_labels(labels) + """ + labels = np.asarray(labels) + binary = labels > 0 + skeleton = morphology.skeletonize(binary) + return (labels * skeleton).astype(labels.dtype) + + +def separate_touching_skeleton_labels( + skeleton_labels: ArrayLike, +) -> np.ndarray: + """ + Remove skeleton pixels where different labels touch. + + This is useful when skeletonizing adjacent labeled regions that result in + touching skeletons. The function removes the minimal pixels needed to + separate connected components from different original labels. Separates + based on 8-connectivity in 2D or 26-connectivity in 3D. + + Parameters + ---------- + skeleton_labels : ArrayLike + Labeled skeleton image where each pixel value is the original label ID. + Typically the output of `skeletonize_labels`. + + Returns + ------- + np.ndarray + Skeleton with touching pixels removed. Pixels at label boundaries + are set to 0. + + Notes + ----- + For most biological images, skeletons rarely touch because objects are + naturally separated. This function handles the edge case where skeletons + from adjacent labels happen to be neighbors (e.g., touching cells). + + This approach is much more efficient than eroding labels before + skeletonizing, as it only removes the 1-2 pixels at actual touching + boundaries rather than eroding all edges. + + Examples + -------- + >>> from ndev_morphology import skeletonize_labels, separate_touching_skeleton_labels + >>> # Create labels that touch + >>> labels = np.zeros((10, 20), dtype=np.uint16) + >>> labels[:, :10] = 1 + >>> labels[:, 10:] = 2 + >>> # Skeletonize - skeletons may touch at boundary + >>> skeleton = skeletonize_labels(labels) + >>> # Separate touching skeletons + >>> separated = separate_touching_skeleton_labels(skeleton) + """ + skeleton_labels = np.asarray(skeleton_labels) + separated = skeleton_labels.copy() + + # Use full connectivity (8-connected in 2D, 26-connected in 3D) to detect + # both orthogonal AND diagonal neighbors, matching skan's connectivity + structure = ndimage.generate_binary_structure(skeleton_labels.ndim, 2) + + # Find pixels adjacent to a different nonzero label + for label_id in np.unique(skeleton_labels): + if label_id == 0: + continue + + this_label = skeleton_labels == label_id + other_labels = (skeleton_labels > 0) & (skeleton_labels != label_id) + + # Pixels of this label that touch other labels (including diagonals) + touching = this_label & ndimage.binary_dilation( + other_labels, structure=structure + ) + # Replace touching pixels with 0 to separate + separated[touching] = 0 + + return separated.astype(skeleton_labels.dtype) + + +def exclude_region_from_skeleton( + skeleton: ArrayLike, + mask: ArrayLike, + dilation_iterations: int = 0, +) -> np.ndarray: + """ + Remove skeleton pixels that overlap with a mask region. + + Useful for excluding soma/nucleus regions from neuron skeletons. + + Parameters + ---------- + skeleton : ArrayLike + Skeleton image (binary or labeled). + mask : ArrayLike + Binary mask of region to exclude. Will be dilated if dilation_iterations > 0. + dilation_iterations : int, optional + Number of binary dilation iterations to apply to mask before exclusion. + Default is 0 (no dilation). + + Returns + ------- + np.ndarray + Skeleton with masked region removed. + + Examples + -------- + >>> from ndev_morphology import skeletonize_labels, exclude_region_from_skeleton + >>> # Exclude nucleus region (with 3-pixel buffer) from neuron skeleton + >>> skeleton_no_soma = exclude_region_from_skeleton( + ... skeleton, nucleus_mask, dilation_iterations=3 + ... ) + """ + skeleton = np.asarray(skeleton) + mask = np.asarray(mask).astype(bool) + + if dilation_iterations > 0: + mask = ndimage.binary_dilation(mask, iterations=dilation_iterations) + + return skeleton * ~mask + + +def fill_skeleton_gaps( + skeleton: ArrayLike, + dilation_size: int = 2, +) -> np.ndarray: + """ + Fill small gaps in a skeleton by dilation and re-skeletonization. + + This function bridges discontinuities in skeletons caused by + incomplete segmentation or thresholding artifacts. + + Parameters + ---------- + skeleton : ArrayLike + Binary skeleton image (or labeled - will be binarized). + dilation_size : int + Radius of disk structuring element for dilation. + Larger values bridge bigger gaps but may create artifacts + at junction points (small cycles). + + Returns + ------- + np.ndarray + Binary skeleton with gaps filled. + + Warnings + -------- + This operation creates small cycles at junction points because + dilation "fattens" the skeleton before re-skeletonizing. + Consider using `prune_short_branches(..., prune_cycles=True)` + after this function to remove unwanted cycles. + + Notes + ----- + For labeled skeletons, the output will be binary. To restore + labels, you can use the original label image to re-assign IDs + to the filled skeleton. + + See Also + -------- + connect_breaks_between_labels : Similar operation on labels (before skeletonization) + + Examples + -------- + >>> from ndev_morphology import fill_skeleton_gaps, prune_short_branches + >>> import skan + >>> # Fill gaps then remove cycles + >>> filled = fill_skeleton_gaps(skeleton, dilation_size=2) + >>> skel = skan.Skeleton(filled) + >>> pruned, _ = prune_short_branches(skel, prune_cycles=True) + """ + from skimage.morphology import disk, skeletonize + + skeleton = np.asarray(skeleton) + binary = skeleton.astype(bool) + + # Dilate to bridge gaps + selem = disk(dilation_size) + dilated = ndimage.binary_dilation(binary, structure=selem) + + # Re-skeletonize to get thin skeleton + filled = skeletonize(dilated) + + return filled diff --git a/src/ndev_morphology/soma.py b/src/ndev_morphology/soma.py new file mode 100644 index 0000000..5cc0c1f --- /dev/null +++ b/src/ndev_morphology/soma.py @@ -0,0 +1,243 @@ +""" +Soma detection for neuronal morphology analysis. + +Functions for identifying cell body (soma) location from label images, +intensity images, or skeleton structure. The soma centroid serves as +the origin for Sholl analysis and the root for directed tree analysis. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal + +import numpy as np +from scipy import ndimage +from skimage import measure + +if TYPE_CHECKING: + import skan + from numpy.typing import ArrayLike + +__all__ = [ + 'detect_soma_centroid', + 'find_soma_node', + 'get_label_centroid', +] + + +def detect_soma_centroid( + labels: ArrayLike, + *, + method: Literal[ + 'largest_region', 'roundest_region', 'intensity_peak' + ] = 'largest_region', + intensity_image: ArrayLike | None = None, + label_id: int | None = None, +) -> np.ndarray: + """ + Detect soma centroid from a label image. + + The soma (cell body) is typically the largest or most circular region + in a neuronal segmentation. This function identifies the soma and + returns its centroid coordinates. + + Parameters + ---------- + labels : ArrayLike + Label image where each connected region has a unique integer ID. + For single-cell analysis, this should be a mask of one cell. + For multi-cell, use `label_id` to specify which cell. + method : {'largest_region', 'roundest_region', 'intensity_peak'} + Method to identify the soma: + - 'largest_region': Centroid of the largest connected component (default) + - 'roundest_region': Centroid of most circular region (lowest eccentricity) + - 'intensity_peak': Location of peak intensity (requires intensity_image) + intensity_image : ArrayLike, optional + Intensity image for 'intensity_peak' method. Typically DAPI/nuclear stain. + label_id : int, optional + Specific label ID to analyze. If None, uses all non-zero labels. + + Returns + ------- + np.ndarray + Centroid coordinates as (Y, X) for 2D or (Z, Y, X) for 3D. + Coordinates are in pixel units. + + Raises + ------ + ValueError + If method is 'intensity_peak' but intensity_image is not provided. + If no regions are found in the label image. + + Examples + -------- + >>> from ndev_morphology import detect_soma_centroid + >>> # Find soma as largest region + >>> soma_center = detect_soma_centroid(cell_labels, method='largest_region') + >>> # Use DAPI channel to find nucleus + >>> soma_center = detect_soma_centroid( + ... cell_labels, + ... method='intensity_peak', + ... intensity_image=dapi_channel, + ... ) + """ + labels = np.asarray(labels) + + # Filter to specific label if requested + if label_id is not None: + labels = (labels == label_id).astype(labels.dtype) + + if method == 'intensity_peak': + if intensity_image is None: + raise ValueError( + "intensity_image is required for 'intensity_peak' method" + ) + return _detect_by_intensity_peak(labels, intensity_image) + + elif method == 'roundest_region': + return _detect_by_roundness(labels) + + else: # 'largest_region' (default) + return _detect_by_size(labels) + + +def _detect_by_size(labels: np.ndarray) -> np.ndarray: + """Find centroid of the largest connected component.""" + props = measure.regionprops(labels.astype(np.int32)) + + if not props: + raise ValueError('No regions found in label image') + + # Find largest by area + largest = max(props, key=lambda p: p.area) + return np.array(largest.centroid) + + +def _detect_by_roundness(labels: np.ndarray) -> np.ndarray: + """Find centroid of the most circular (lowest eccentricity) region.""" + props = measure.regionprops(labels.astype(np.int32)) + + if not props: + raise ValueError('No regions found in label image') + + # Find most circular (lowest eccentricity) + # For 3D, use extent as proxy (higher extent = more compact) + if labels.ndim == 3: + roundest = max(props, key=lambda p: p.extent) + else: + roundest = min(props, key=lambda p: p.eccentricity) + + return np.array(roundest.centroid) + + +def _detect_by_intensity_peak( + labels: np.ndarray, + intensity_image: ArrayLike, +) -> np.ndarray: + """Find location of peak intensity within labeled region.""" + intensity_image = np.asarray(intensity_image) + mask = labels > 0 + + # Mask intensity to labeled region + masked_intensity = np.where(mask, intensity_image, 0) + + # Find peak location + peak_idx = np.unravel_index( + np.argmax(masked_intensity), + masked_intensity.shape, + ) + + return np.array(peak_idx, dtype=float) + + +def get_label_centroid( + labels: ArrayLike, + label_id: int, +) -> np.ndarray: + """ + Get centroid of a specific label. + + Simple utility to extract the centroid of a single labeled region. + + Parameters + ---------- + labels : ArrayLike + Label image. + label_id : int + ID of the label to get centroid for. + + Returns + ------- + np.ndarray + Centroid coordinates as (Y, X) or (Z, Y, X). + + Raises + ------ + ValueError + If label_id is not found in the label image. + + Examples + -------- + >>> from ndev_morphology import get_label_centroid + >>> centroid = get_label_centroid(labels, label_id=5) + """ + labels = np.asarray(labels) + mask = labels == label_id + + if not np.any(mask): + raise ValueError(f'Label {label_id} not found in image') + + return np.array(ndimage.center_of_mass(mask)) + + +def find_soma_node( + skeleton: skan.Skeleton, + soma_centroid: ArrayLike, +) -> int: + """ + Find the skeleton node closest to the soma centroid. + + This node can serve as the root for directed tree analysis, + where branches are oriented from soma toward endpoints. + + Parameters + ---------- + skeleton : skan.Skeleton + A skan.Skeleton object. + soma_centroid : ArrayLike + Soma centroid coordinates in the same units as skeleton coordinates. + If skeleton was created with physical spacing, use physical units. + + Returns + ------- + int + Index of the skeleton node closest to the soma centroid. + This can be used as the source node for networkx tree construction. + + Examples + -------- + >>> import skan + >>> from skan.csr import skeleton_to_nx + >>> import networkx as nx + >>> from ndev_morphology import detect_soma_centroid, find_soma_node + >>> + >>> # Create skeleton and find soma + >>> skeleton = skan.Skeleton(skeleton_image, spacing=(0.2, 0.2)) + >>> soma = detect_soma_centroid(cell_labels) + >>> soma_node = find_soma_node(skeleton, soma) + >>> + >>> # Create directed tree from soma + >>> G = skeleton_to_nx(skeleton) + >>> directed_tree = nx.bfs_tree(G, soma_node) + """ + soma_centroid = np.asarray(soma_centroid) + + # Get all node coordinates from skeleton + # skeleton.coordinates is shape (n_nodes, ndim) + node_coords = skeleton.coordinates + + # Compute distances to soma + distances = np.linalg.norm(node_coords - soma_centroid, axis=1) + + # Return index of closest node + return int(np.argmin(distances)) diff --git a/src/ndev_morphology/tree.py b/src/ndev_morphology/tree.py new file mode 100644 index 0000000..2321780 --- /dev/null +++ b/src/ndev_morphology/tree.py @@ -0,0 +1,843 @@ +""" +Directional tree analysis for skeleton structures. + +This module provides tools for analyzing skeletons as directed trees +rooted at a soma/origin point. This enables: + +- Branch ordering (primary, secondary, tertiary branches) +- Path analysis from soma to tips +- Axon/dendrite length measurements +- Directed graph traversal + +Key Concepts +------------ +- **Undirected skeleton**: skan represents skeletons as undirected graphs + where edges are paths between junctions/endpoints. +- **Directed tree**: By specifying a root (soma), we can create a directed + tree where edges flow from soma toward tips. +- **Branch order**: The "generation" of a branch. Primary branches connect + directly to soma, secondary branch off primary, etc. +- **Centrifugal ordering**: Counting from soma outward (1, 2, 3...) +- **Strahler ordering**: Counting from tips inward (tips=1, merging increases) + +This module uses networkx for graph operations, building on skan's +`skeleton_to_nx()` function. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TYPE_CHECKING + +import networkx as nx +import numpy as np +import pandas as pd +import skan +from skan.csr import skeleton_to_nx + +if TYPE_CHECKING: + from numpy.typing import ArrayLike + +__all__ = [ + 'DirectedTree', + 'BranchOrder', + 'create_directed_tree', + 'create_directed_trees_from_soma', + 'compute_branch_order', + 'compute_strahler_order', + 'find_longest_path', + 'get_paths_to_tips', + 'summarize_directed_tree', +] + + +class BranchOrder: + """Branch order constants for semantic clarity.""" + + PRIMARY = 1 + SECONDARY = 2 + TERTIARY = 3 + QUATERNARY = 4 + + +@dataclass +class DirectedTree: + """ + A skeleton represented as a directed tree from a root node. + + This class wraps a networkx DiGraph with additional metadata + for morphological analysis. + + Attributes + ---------- + graph : nx.DiGraph + Directed graph where edges flow from root toward tips. + root_node : int + The node ID of the root (soma). + root_coords : np.ndarray + Coordinates of the root node. + skeleton : skan.Skeleton + The original skan Skeleton object. + summary : pd.DataFrame + Branch summary from skan.summarize(). + undirected_graph : nx.MultiGraph + The original undirected graph from skeleton_to_nx(). + + Properties + ---------- + n_branches : int + Number of branches (edges) in the tree. + n_tips : int + Number of terminal endpoints. + n_junctions : int + Number of branch points. + tip_nodes : list[int] + Node IDs of all tips (out-degree 0). + junction_nodes : list[int] + Node IDs of all junctions (out-degree > 1). + """ + + graph: nx.DiGraph + root_node: int + root_coords: np.ndarray + skeleton: skan.Skeleton + summary: pd.DataFrame + undirected_graph: nx.MultiGraph + _branch_orders: dict = field(default_factory=dict, repr=False) + + @property + def n_branches(self) -> int: + """Number of branches (edges) in the tree.""" + return self.graph.number_of_edges() + + @property + def n_tips(self) -> int: + """Number of terminal endpoints (out-degree 0, not root).""" + return len(self.tip_nodes) + + @property + def n_junctions(self) -> int: + """Number of branch points (out-degree > 1).""" + return len(self.junction_nodes) + + @property + def tip_nodes(self) -> list[int]: + """Node IDs of all tips (leaves of the tree).""" + return [n for n in self.graph.nodes() if self.graph.out_degree(n) == 0] + + @property + def junction_nodes(self) -> list[int]: + """Node IDs of branch points (out-degree > 1).""" + return [n for n in self.graph.nodes() if self.graph.out_degree(n) > 1] + + @property + def primary_branches(self) -> list[tuple[int, int]]: + """Edges that are primary branches (directly from root).""" + return list(self.graph.out_edges(self.root_node)) + + def get_branch_order(self, edge: tuple[int, int]) -> int: + """ + Get the centrifugal branch order of an edge. + + Parameters + ---------- + edge : tuple[int, int] + Edge as (source, target) node IDs. + + Returns + ------- + int + Branch order (1=primary, 2=secondary, etc.) + """ + if not self._branch_orders: + self._branch_orders = compute_branch_order(self) + return self._branch_orders.get(edge, 0) + + def path_to_node(self, target: int) -> list[int]: + """ + Get the path from root to a target node. + + Parameters + ---------- + target : int + Target node ID. + + Returns + ------- + list[int] + List of node IDs from root to target. + + Raises + ------ + nx.NodeNotFound + If target is not in the directed tree. + nx.NetworkXNoPath + If no path exists (shouldn't happen in a tree). + """ + if target not in self.graph: + raise nx.NodeNotFound( + f'Node {target} is not in the directed tree. ' + f'It may be in a disconnected skeleton component.' + ) + return nx.shortest_path(self.graph, self.root_node, target) + + def distance_to_node(self, target: int) -> float: + """ + Get the total path distance from root to a target node. + + Parameters + ---------- + target : int + Target node ID. + + Returns + ------- + float + Sum of branch distances along the path. + + Raises + ------ + nx.NodeNotFound + If target is not in the directed tree. + """ + if target not in self.graph: + raise nx.NodeNotFound( + f'Node {target} is not in the directed tree.' + ) + path = self.path_to_node(target) + total = 0.0 + for i in range(len(path) - 1): + edge_data = self.graph.get_edge_data(path[i], path[i + 1]) + total += edge_data.get('branch_distance', 0.0) + return total + + def subtree_from_node(self, node: int) -> nx.DiGraph: + """ + Get the subtree rooted at a given node. + + Parameters + ---------- + node : int + Node ID to use as subtree root. + + Returns + ------- + nx.DiGraph + Subgraph containing node and all its descendants. + """ + descendants = nx.descendants(self.graph, node) + descendants.add(node) + return self.graph.subgraph(descendants).copy() + + +def create_directed_tree( + skeleton: skan.Skeleton, + root_coords: ArrayLike, + *, + summary: pd.DataFrame | None = None, + skeleton_id: int | None = None, +) -> DirectedTree: + """ + Create a directed tree from a skeleton rooted at specified coordinates. + + Parameters + ---------- + skeleton : skan.Skeleton + The skeleton to analyze. + root_coords : ArrayLike + Coordinates of the root (soma) position. The closest skeleton + node to these coordinates becomes the root. + summary : pd.DataFrame, optional + Pre-computed summary from skan.summarize(). If not provided, + it will be computed. + skeleton_id : int, optional + If provided, only consider nodes from this skeleton component. + This is useful when analyzing multi-cell images where each cell + has a separate skeleton_id in the skan summary. + + Returns + ------- + DirectedTree + A directed tree representation of the skeleton. + + Examples + -------- + >>> import skan + >>> from ndev_morphology.tree import create_directed_tree + >>> skeleton = skan.Skeleton(skeleton_image, spacing=(0.2, 0.2)) + >>> soma_centroid = [100, 150] # y, x coordinates + >>> tree = create_directed_tree(skeleton, soma_centroid) + >>> print(f"Primary branches: {len(tree.primary_branches)}") + >>> print(f"Tips: {tree.n_tips}") + """ + root_coords = np.asarray(root_coords) + + # Get or compute summary + if summary is None: + summary = skan.summarize(skeleton, separator='_') + + # Convert to networkx + undirected = skeleton_to_nx(skeleton, summary) + + # Find the root node + if skeleton_id is not None: + # Filter to nodes in the specified skeleton component + root_node = _find_root_node_for_skeleton( + skeleton, summary, root_coords, skeleton_id + ) + else: + # Find the closest node to root_coords (original behavior) + root_node = _find_closest_node(skeleton.coordinates, root_coords) + + # Create directed tree via BFS from root + directed = nx.bfs_tree(undirected, root_node) + + # Copy edge attributes from undirected graph + _copy_edge_attributes(undirected, directed, summary) + + return DirectedTree( + graph=directed, + root_node=root_node, + root_coords=root_coords, + skeleton=skeleton, + summary=summary, + undirected_graph=undirected, + ) + + +def _find_root_node_for_skeleton( + skeleton: skan.Skeleton, + summary: pd.DataFrame, + target_coords: np.ndarray, + skeleton_id: int, +) -> int: + """ + Find the root node for a specific skeleton component. + + Parameters + ---------- + skeleton : skan.Skeleton + The skeleton object. + summary : pd.DataFrame + Branch summary from skan.summarize(). + target_coords : np.ndarray + Target coordinates (soma centroid). + skeleton_id : int + The skeleton component ID to search within. + + Returns + ------- + int + Node ID of the root node. + + Raises + ------ + ValueError + If no nodes found for the given skeleton_id. + """ + # Get all node IDs that belong to this skeleton + skel_rows = summary[summary['skeleton_id'] == skeleton_id] + if len(skel_rows) == 0: + raise ValueError(f'No branches found for skeleton_id={skeleton_id}') + + # Collect all unique node IDs from this skeleton + node_ids = set() + for _, row in skel_rows.iterrows(): + node_ids.add(int(row['node_id_src'])) + node_ids.add(int(row['node_id_dst'])) + + node_ids = list(node_ids) + + # Get coordinates of these nodes + node_coords = skeleton.coordinates[node_ids] + + # Find closest to target + distances = np.linalg.norm(node_coords - target_coords, axis=1) + closest_idx = np.argmin(distances) + + return node_ids[closest_idx] + + +def _find_closest_node( + coordinates: np.ndarray, + target: np.ndarray, +) -> int: + """Find the node index closest to target coordinates.""" + # coordinates shape: (n_nodes, ndim) + distances = np.linalg.norm(coordinates - target, axis=1) + return int(np.argmin(distances)) + + +def _copy_edge_attributes( + undirected: nx.MultiGraph, + directed: nx.DiGraph, + summary: pd.DataFrame, +) -> None: + """Copy edge attributes from undirected graph to directed graph.""" + # Build lookup from node pairs to summary rows (with row index) + edge_lookup = {} + for idx, row in summary.iterrows(): + src, dst = int(row['node_id_src']), int(row['node_id_dst']) + edge_lookup[(src, dst)] = (idx, row) + edge_lookup[(dst, src)] = (idx, row) # Both directions + + for u, v in directed.edges(): + # Copy path from undirected graph first (this is the most important) + if undirected.has_edge(u, v): + edge_data = undirected.get_edge_data(u, v) + if edge_data: + # MultiGraph returns dict of edge keys + first_edge = list(edge_data.values())[0] + if 'path' in first_edge: + directed[u][v]['path'] = first_edge['path'] + + # Also copy summary data + if (u, v) in edge_lookup: + idx, row = edge_lookup[(u, v)] + directed[u][v]['branch_distance'] = row['branch_distance'] + directed[u][v]['euclidean_distance'] = row['euclidean_distance'] + directed[u][v]['branch_type'] = row['branch_type'] + directed[u][v]['summary_index'] = idx + + +def create_directed_trees_from_soma( + skeleton_image: ArrayLike, + soma_mask: ArrayLike, + soma_centroid: ArrayLike, + *, + spacing: tuple[float, ...] = (1.0, 1.0), + dilation_iterations: int = 2, + skeleton_label: int | None = None, +) -> list[DirectedTree]: + """ + Create directed trees for all branches radiating from a soma. + + This function handles the case where multiple skeleton branches + radiate outward from a soma region. It: + + 1. Optionally filters to a specific skeleton label + 2. Excludes the soma region from the skeleton (creating disconnected fragments) + 3. For each fragment, finds the node closest to the soma as its root + 4. Creates a directed tree for each fragment + + Parameters + ---------- + skeleton_image : ArrayLike + Binary or labeled skeleton image. + soma_mask : ArrayLike + Binary mask of the soma region. This region will be excluded + from the skeleton before analysis. + soma_centroid : ArrayLike + Centroid of the soma (y, x) or (z, y, x). Used to find root nodes. + spacing : tuple of float, optional + Physical pixel spacing (default: (1.0, 1.0)). + dilation_iterations : int, optional + How much to dilate the soma mask before exclusion (default: 2). + This ensures clean separation of branches. + skeleton_label : int, optional + If provided, only analyze the skeleton with this label value. + This is important for multi-cell images where each neuron has + a unique skeleton label. + + Returns + ------- + list of DirectedTree + One DirectedTree for each disconnected skeleton fragment radiating + from the soma. Empty list if no skeleton fragments found. + + Examples + -------- + >>> from ndev_morphology.tree import create_directed_trees_from_soma + >>> # skeleton_image: labeled skeleton (each cell has unique label) + >>> # soma_mask: binary mask of one cell body + >>> # soma_centroid: (y, x) center of the soma + >>> trees = create_directed_trees_from_soma( + ... skeleton_image, soma_mask, soma_centroid, + ... spacing=(0.2, 0.2), dilation_iterations=3, + ... skeleton_label=1, # Analyze only skeleton label 1 + ... ) + >>> print(f"Found {len(trees)} branches radiating from soma") + >>> for i, tree in enumerate(trees): + ... print(f" Branch {i+1}: {tree.n_tips} tips, order {tree.n_junctions} junctions") + + Notes + ----- + This function is designed for the common neuroscience case where: + - A neuron has a central cell body (soma) + - Multiple dendrites/axons radiate outward from the soma + - Each radiating branch should be analyzed as a separate tree + - All branches belong to the same neuron (same label_id) + """ + from .skeleton import exclude_region_from_skeleton + + skeleton_image = np.asarray(skeleton_image) + soma_mask = np.asarray(soma_mask).astype(bool) + soma_centroid = np.asarray(soma_centroid) + + # Filter to specific skeleton label if provided + if skeleton_label is not None: + skeleton_image = (skeleton_image == skeleton_label).astype(np.uint8) + + # Exclude soma region from skeleton + skeleton_no_soma = exclude_region_from_skeleton( + skeleton_image, soma_mask, dilation_iterations=dilation_iterations + ) + + # Check if anything remains + if not np.any(skeleton_no_soma): + return [] + + # Create skan skeleton from the remaining fragments + # Convert to binary first (in case it was labeled) + skeleton_binary = (skeleton_no_soma > 0).astype(np.uint8) + skel = skan.Skeleton(skeleton_binary, spacing=spacing) + + if skel.n_paths == 0: + return [] + + # Get summary and undirected graph + summary = skan.summarize(skel, separator='_') + undirected = skeleton_to_nx(skel, summary) + + # Find connected components - each is a separate tree + components = list(nx.connected_components(undirected)) + + trees = [] + for component_nodes in components: + if len(component_nodes) < 2: + # Skip single-node components + continue + + # Find the node in this component closest to soma centroid + component_nodes_list = list(component_nodes) + node_coords = skel.coordinates[component_nodes_list] + distances = np.linalg.norm(node_coords - soma_centroid, axis=1) + root_idx = np.argmin(distances) + root_node = component_nodes_list[root_idx] + + # Create directed tree for this component + directed = nx.bfs_tree(undirected.subgraph(component_nodes), root_node) + + # Copy edge attributes + _copy_edge_attributes(undirected, directed, summary) + + tree = DirectedTree( + graph=directed, + root_node=root_node, + root_coords=soma_centroid, + skeleton=skel, + summary=summary, + undirected_graph=undirected, + ) + trees.append(tree) + + return trees + + +def compute_branch_order(tree: DirectedTree) -> dict[tuple[int, int], int]: + """ + Compute centrifugal branch order for all edges. + + Branch order is computed as: + - Primary (1): Edges directly from root + - Secondary (2): Edges from nodes reached by primary branches + - Tertiary (3): Edges from nodes reached by secondary branches + - etc. + + Parameters + ---------- + tree : DirectedTree + The directed tree to analyze. + + Returns + ------- + dict + Mapping of (source, target) edge tuples to branch order (int). + + Examples + -------- + >>> orders = compute_branch_order(tree) + >>> primary = [e for e, o in orders.items() if o == 1] + >>> secondary = [e for e, o in orders.items() if o == 2] + """ + orders = {} + + # BFS from root, tracking depth + for edge in nx.bfs_edges(tree.graph, tree.root_node): + src, dst = edge + if src == tree.root_node: + orders[edge] = BranchOrder.PRIMARY + else: + # Find parent edge order + parent_edges = [(u, v) for u, v in orders if v == src] + if parent_edges: + parent_order = orders[parent_edges[0]] + # If src is a junction (has multiple children), increment order + if tree.graph.out_degree(src) > 1: + orders[edge] = parent_order + 1 + else: + # Same order as parent (continuation) + orders[edge] = parent_order + else: + orders[edge] = BranchOrder.PRIMARY + + return orders + + +def find_longest_path(tree: DirectedTree) -> tuple[list[int], float]: + """ + Find the longest path from root to any tip. + + This represents the "main axis" of the structure (e.g., axon length). + + Parameters + ---------- + tree : DirectedTree + The directed tree to analyze. + + Returns + ------- + path : list[int] + Node IDs along the longest path. + length : float + Total path length in physical units. + + Examples + -------- + >>> path, length = find_longest_path(tree) + >>> print(f"Axon length: {length:.2f} µm") + """ + longest_path = [] + longest_length = 0.0 + + for tip in tree.tip_nodes: + path = tree.path_to_node(tip) + length = tree.distance_to_node(tip) + + if length > longest_length: + longest_length = length + longest_path = path + + return longest_path, longest_length + + +def get_paths_to_tips( + tree: DirectedTree, +) -> list[dict]: + """ + Get all paths from root to tips with their properties. + + Parameters + ---------- + tree : DirectedTree + The directed tree to analyze. + + Returns + ------- + list of dict + Each dict contains: + - 'tip_node': int, the tip node ID + - 'path': list[int], node IDs from root to tip + - 'length': float, total path length + - 'n_branches': int, number of branches traversed + - 'max_order': int, maximum branch order along path + """ + paths = [] + + for tip in tree.tip_nodes: + path = tree.path_to_node(tip) + length = tree.distance_to_node(tip) + + # Count branches and max order + n_branches = len(path) - 1 + max_order = 0 + for i in range(len(path) - 1): + edge = (path[i], path[i + 1]) + order = tree.get_branch_order(edge) + max_order = max(max_order, order) + + paths.append( + { + 'tip_node': tip, + 'path': path, + 'length': length, + 'n_branches': n_branches, + 'max_order': max_order, + } + ) + + # Sort by length descending + paths.sort(key=lambda x: x['length'], reverse=True) + + return paths + + +def compute_strahler_order(tree: DirectedTree) -> dict[tuple[int, int], int]: + """ + Compute Strahler order for all edges. + + Strahler ordering starts from tips: + - Tips have order 1 + - When two branches of order n merge, result is order n+1 + - When branches of different orders merge, result is max order + + Parameters + ---------- + tree : DirectedTree + The directed tree to analyze. + + Returns + ------- + dict + Mapping of (source, target) edge tuples to Strahler order (int). + + Notes + ----- + Strahler order is commonly used in hydrology and neuroscience to + describe branching complexity. Higher orders indicate more complex + upstream structure. + + The algorithm works by: + 1. Assigning order 1 to all terminal edges (edges leading to tips) + 2. Processing nodes from tips toward root + 3. At each junction: if children have same max order, parent gets max+1; + otherwise parent gets the max order + """ + orders = {} + + # First, identify all edges and their "downstream" (toward tips) structure + # In a directed tree from root, edges point AWAY from root toward tips + + # Get all edges + all_edges = list(tree.graph.edges()) + + # Assign order 1 to terminal edges (edges where target is a tip) + for u, v in all_edges: + if tree.graph.out_degree(v) == 0: + # v is a tip, so this edge is terminal + orders[(u, v)] = 1 + + # Now propagate orders upward (from tips toward root) + # Process in reverse BFS order (tips first, then their parents, etc.) + # We need to process a node only after all its children are processed + + # Get nodes in reverse BFS order + bfs_order = list(nx.bfs_tree(tree.graph, tree.root_node).nodes()) + + # Process from tips toward root (reverse order) + for node in reversed(bfs_order): + if node == tree.root_node: + continue + + # Get the edge FROM parent TO this node + in_edges = [(u, v) for u, v in all_edges if v == node] + if not in_edges: + continue + + parent_edge = in_edges[0] # Should be exactly one in a tree + if parent_edge in orders: + continue # Already assigned (terminal edge) + + # Get orders of edges FROM this node to its children + child_edge_orders = [] + for u, v in all_edges: + if u == node and (u, v) in orders: + child_edge_orders.append(orders[(u, v)]) + + if not child_edge_orders: + # This shouldn't happen in a well-formed tree + orders[parent_edge] = 1 + elif len(child_edge_orders) == 1: + # Single child - inherit order + orders[parent_edge] = child_edge_orders[0] + else: + # Multiple children - Strahler rule + max_order = max(child_edge_orders) + count_max = child_edge_orders.count(max_order) + if count_max >= 2: + orders[parent_edge] = max_order + 1 + else: + orders[parent_edge] = max_order + + return orders + + +def summarize_directed_tree(tree: DirectedTree) -> pd.DataFrame: + """ + Create a summary DataFrame with directional branch information. + + This extends skan's summary with: + - branch_order: Centrifugal order from soma + - strahler_order: Strahler order from tips + - distance_from_root: Path distance from soma to branch start + - is_terminal: Whether branch ends at a tip + - in_tree: Whether the branch is in the directed tree (connected to soma) + + Parameters + ---------- + tree : DirectedTree + The directed tree to summarize. + + Returns + ------- + pd.DataFrame + Extended summary with directional metrics. + + Notes + ----- + Branches from disconnected skeleton components (not connected to the + root/soma) will have NaN values for distance_from_root and order 0. + """ + # Start with skan summary + df = tree.summary.copy() + + # Compute orders + centrifugal = compute_branch_order(tree) + strahler = compute_strahler_order(tree) + + # Get set of nodes in tree for fast lookup + tree_nodes = set(tree.graph.nodes()) + + # Add new columns + branch_orders = [] + strahler_orders = [] + distances_from_root = [] + is_terminal = [] + in_tree = [] + + for _idx, row in df.iterrows(): + src = int(row['node_id_src']) + dst = int(row['node_id_dst']) + + # Check if this branch is in the directed tree + src_in_tree = src in tree_nodes + dst_in_tree = dst in tree_nodes + branch_in_tree = src_in_tree and dst_in_tree + in_tree.append(branch_in_tree) + + # Try both edge directions + edge = (src, dst) if (src, dst) in centrifugal else (dst, src) + + branch_orders.append(centrifugal.get(edge, 0)) + strahler_orders.append(strahler.get(edge, 0)) + + # Distance from root to start of branch + if branch_in_tree: + try: + dist = tree.distance_to_node(src) + except (nx.NetworkXNoPath, nx.NodeNotFound): + dist = np.nan + else: + dist = np.nan + distances_from_root.append(dist) + + # Is this a terminal branch? + is_terminal.append(dst in tree.tip_nodes or src in tree.tip_nodes) + + df['branch_order'] = branch_orders + df['strahler_order'] = strahler_orders + df['distance_from_root'] = distances_from_root + df['is_terminal'] = is_terminal + df['in_tree'] = in_tree + + return df diff --git a/src/ndev_morphology/widgets/__init__.py b/src/ndev_morphology/widgets/__init__.py new file mode 100644 index 0000000..63c5fc6 --- /dev/null +++ b/src/ndev_morphology/widgets/__init__.py @@ -0,0 +1,31 @@ +""" +napari widgets for ndev-morphology. + +magicgui-based widgets for skeleton and Sholl analysis visualization. +""" + +from .analysis_widget import cell_analysis +from .batch_widget import BatchAnalysisWidget +from .branch_widget import branch_analysis +from .graph_widget import skeleton_graph +from .labels_widget import refine_labels_widget +from .pruning_widget import prune_skeleton_widget +from .sholl_widget import sholl_analysis +from .skeleton_widget import skeleton_to_shapes +from .skeletonize_widget import skeletonize_labels_widget +from .soma_widget import soma_detection +from .tree_widget import directed_tree_analysis + +__all__ = [ + 'refine_labels_widget', + 'skeletonize_labels_widget', + 'prune_skeleton_widget', + 'skeleton_to_shapes', + 'skeleton_graph', + 'sholl_analysis', + 'soma_detection', + 'branch_analysis', + 'cell_analysis', + 'directed_tree_analysis', + 'BatchAnalysisWidget', +] diff --git a/src/ndev_morphology/widgets/_utils.py b/src/ndev_morphology/widgets/_utils.py new file mode 100644 index 0000000..cf76c79 --- /dev/null +++ b/src/ndev_morphology/widgets/_utils.py @@ -0,0 +1,144 @@ +""" +Shared utilities for morphology widgets. + +This module provides common helper functions used across widgets +to reduce boilerplate and ensure consistent behavior. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from ..model import MorphologyModel + +if TYPE_CHECKING: + import napari + +__all__ = [ + 'get_or_create_model', + 'get_spacing_from_layer', + 'validate_skeleton_layer', +] + + +def get_or_create_model( + layer: napari.layers.Labels, + *, + force_recompute: bool = False, +) -> MorphologyModel: + """ + Get or create MorphologyModel for a skeleton layer. + + This is the recommended way to get a model in widgets. + It handles caching automatically. + + Parameters + ---------- + layer : napari.layers.Labels + Skeleton layer. + force_recompute : bool, optional + Force creation of a new model. Default False. + + Returns + ------- + MorphologyModel + The model object. + + Raises + ------ + ValueError + If skeleton is empty. + + Examples + -------- + >>> model = get_or_create_model(skeleton_layer) + >>> branches = model.summary + """ + # Validate first + validate_skeleton_layer(layer) + + return MorphologyModel.from_layer(layer, force_recompute=force_recompute) + + +def get_spacing_from_layer( + layer: napari.layers.Labels | napari.layers.Image, +) -> tuple[float, ...]: + """ + Extract physical spacing from layer scale. + + Parameters + ---------- + layer : napari.layers.Labels or napari.layers.Image + The napari layer. + + Returns + ------- + tuple of float + Spacing as (y, x) or (z, y, x) depending on dimensionality. + """ + return tuple(layer.scale[-2:]) + + +def validate_skeleton_layer(layer: napari.layers.Labels) -> None: + """ + Validate that a layer contains a non-empty skeleton. + + Parameters + ---------- + layer : napari.layers.Labels + The skeleton layer to validate. + + Raises + ------ + ValueError + If skeleton is empty. + TypeError + If layer is not a Labels layer. + """ + if not hasattr(layer, 'data'): + raise TypeError(f'Expected napari layer, got {type(layer)}') + + arr = np.asarray(layer.data) + if not np.any(arr): + raise ValueError('Skeleton is empty') + + +def ensure_binary_skeleton(arr: np.ndarray) -> np.ndarray: + """ + Convert labeled skeleton to binary. + + Parameters + ---------- + arr : np.ndarray + Binary or labeled skeleton. + + Returns + ------- + np.ndarray + Binary skeleton (dtype uint8). + """ + return (arr > 0).astype(np.uint8) + + +def get_layer_name_suffix( + layer: napari.layers.Layer, + suffix: str, +) -> str: + """ + Generate a derived layer name with suffix. + + Parameters + ---------- + layer : napari.layers.Layer + Source layer. + suffix : str + Suffix to add. + + Returns + ------- + str + Name like "skeleton_shapes" or "skeleton_sholl". + """ + return f'{layer.name}_{suffix}' diff --git a/src/ndev_morphology/widgets/analysis_widget.py b/src/ndev_morphology/widgets/analysis_widget.py new file mode 100644 index 0000000..3d2caf5 --- /dev/null +++ b/src/ndev_morphology/widgets/analysis_widget.py @@ -0,0 +1,159 @@ +""" +Widget for per-cell morphology analysis. + +Provides interactive single-cell analysis with visualization. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +from magicgui import magic_factory + +if TYPE_CHECKING: + import napari + +__all__ = ['cell_analysis'] + + +@magic_factory( + call_button='Analyze Cell', + labels={'label': 'Cell Labels'}, + label_id={'label': 'Label ID', 'min': 1}, + soma_labels={ + 'label': 'Soma Labels (optional)', + 'nullable': True, + }, + run_sholl={'tooltip': 'Perform Sholl analysis from soma'}, + sholl_step={'label': 'Sholl Step (pixels)', 'min': 1.0, 'step': 1.0}, +) +def cell_analysis( + labels: napari.layers.Labels, + label_id: int = 1, + soma_labels: napari.layers.Labels | None = None, + run_sholl: bool = True, + sholl_step: float = 5.0, +) -> list[napari.types.LayerDataTuple]: + """ + Analyze a single labeled cell's morphology. + + Returns visualization layers and prints summary to console. + + Parameters + ---------- + labels : Labels + Cell label image. + label_id : int + Which label ID to analyze. + soma_labels : Labels, optional + Separate nuclei labels for soma detection. + run_sholl : bool + Whether to perform Sholl analysis. + sholl_step : float + Step size for Sholl radii in pixels. + + Returns + ------- + list of LayerDataTuple + Layers for skeleton paths and soma point. + """ + from .._geometry import sholl_shells_to_ellipses, skeleton_to_paths + from ..analysis import analyze_single_cell + + labels_data = np.asarray(labels.data) + scale = labels.scale + spacing = tuple(scale[-2:]) + + # Check if label exists + if label_id not in np.unique(labels_data): + raise ValueError(f'Label {label_id} not found in image') + + # Check if label has enough pixels + cell_mask = labels_data == label_id + if cell_mask.sum() < 10: + raise ValueError( + f'Label {label_id} is too small ({cell_mask.sum()} pixels)' + ) + + soma_data = None + if soma_labels is not None: + soma_data = np.asarray(soma_labels.data) + + # Run analysis (function handles empty skeleton errors) + result = analyze_single_cell( + labels_data, + label_id=label_id, + spacing=spacing, + soma_labels=soma_data, + run_sholl=run_sholl, + sholl_step=sholl_step * spacing[0], # Convert to physical + ) + + layers = [] + + # 1. Skeleton as shapes + paths, props = skeleton_to_paths(result.skeleton) + if paths: + layers.append( + ( + paths, + { + 'name': f'Skeleton (cell {label_id})', + 'shape_type': 'path', + 'edge_color': 'branch_distance', + 'edge_colormap': 'viridis', + 'edge_width': 2.0, + 'properties': props, + 'scale': scale, + }, + 'shapes', + ) + ) + + # 2. Soma as point + if result.soma_centroid is not None: + layers.append( + ( + np.array([result.soma_centroid]), + { + 'name': f'Soma (cell {label_id})', + 'size': 15, + 'face_color': 'magenta', + 'symbol': 'disc', + 'scale': scale, + }, + 'points', + ) + ) + + # 3. Sholl shells + if result.sholl is not None and result.soma_centroid is not None: + radii_px = result.sholl.radii / spacing[0] + shells = sholl_shells_to_ellipses(result.soma_centroid, radii_px) + + layers.append( + ( + shells, + { + 'name': f'Sholl (cell {label_id})', + 'shape_type': 'ellipse', + 'edge_color': 'turbo', + 'edge_width': 1.5, + 'face_color': 'transparent', + 'scale': scale, + }, + 'shapes', + ) + ) + + # Print summary + print(f'\n=== Cell {label_id} Analysis ===') + for key, value in result.summary.items(): + if isinstance(value, float): + print(f' {key}: {value:.3f}') + else: + print(f' {key}: {value}') + print('=' * 35) + + return layers diff --git a/src/ndev_morphology/widgets/batch_widget.py b/src/ndev_morphology/widgets/batch_widget.py new file mode 100644 index 0000000..e3fe708 --- /dev/null +++ b/src/ndev_morphology/widgets/batch_widget.py @@ -0,0 +1,253 @@ +""" +Widget for batch cell morphology analysis. + +Provides batch processing with progress tracking and cancellation +using nbatch.BatchRunner. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +from magicgui.widgets import ( + CheckBox, + ComboBox, + Container, + FileEdit, + FloatSpinBox, + Label, + ProgressBar, + PushButton, + SpinBox, + Table, +) + +if TYPE_CHECKING: + from napari import Viewer + +__all__ = ['BatchAnalysisWidget'] + + +class BatchAnalysisWidget(Container): + """ + Widget for batch morphology analysis with progress tracking. + + Uses analyze_all_cells_generator for item-by-item progress + and nbatch.BatchRunner for threaded execution. + """ + + def __init__(self, viewer: Viewer): + super().__init__() + self.viewer = viewer + self._results = [] + self._summary_df = None + self._is_running = False + + # Build UI + self._build_ui() + + def _build_ui(self): + """Construct widget UI elements.""" + # Layer selection + self._labels_combo = ComboBox( + label='Cell Labels', + choices=self._get_labels_layers, + ) + self._soma_combo = ComboBox( + label='Soma Labels (optional)', + choices=self._get_labels_layers_optional, + nullable=True, + ) + + # Spacing + self._spacing_y = FloatSpinBox( + label='Spacing Y (µm)', + value=1.0, + min=0.001, + step=0.1, + ) + self._spacing_x = FloatSpinBox( + label='Spacing X (µm)', + value=1.0, + min=0.001, + step=0.1, + ) + + # Analysis options + self._run_sholl = CheckBox(label='Run Sholl Analysis', value=True) + self._sholl_step = FloatSpinBox( + label='Sholl Step (µm)', + value=1.0, + min=0.1, + step=0.5, + ) + self._min_size = SpinBox( + label='Min Skeleton Size (px)', + value=10, + min=1, + ) + + # Output + self._output_path = FileEdit( + label='Save Results To', + mode='w', + filter='*.csv', + ) + + # Status and progress + self._status_label = Label(value='Ready') + self._progress = ProgressBar(min=0, max=100, value=0) + + # Buttons + self._run_button = PushButton(text='Run Batch Analysis') + self._cancel_button = PushButton(text='Cancel', enabled=False) + + # Results table (shows summary) + self._results_table = Table(value={'Column': []}) + + # Connect signals + self._run_button.clicked.connect(self._on_run) + self._cancel_button.clicked.connect(self._on_cancel) + + # Add all widgets + self.extend( + [ + self._labels_combo, + self._soma_combo, + self._spacing_y, + self._spacing_x, + self._run_sholl, + self._sholl_step, + self._min_size, + self._output_path, + self._status_label, + self._progress, + self._run_button, + self._cancel_button, + self._results_table, + ] + ) + + def _get_labels_layers(self, widget=None): + """Get list of Labels layers in viewer.""" + import napari + + return [ + layer.name + for layer in self.viewer.layers + if isinstance(layer, napari.layers.Labels) + ] + + def _get_labels_layers_optional(self, widget=None): + """Get list of Labels layers plus None option.""" + layers = self._get_labels_layers() + return [None] + layers + + def _on_run(self): + """Start batch analysis.""" + import pandas as pd + + from ..analysis import analyze_all_cells_generator + + # Get selected layers + labels_name = self._labels_combo.value + if not labels_name: + self._status_label.value = 'Error: Select a labels layer' + return + + labels_layer = self.viewer.layers[labels_name] + labels = np.asarray(labels_layer.data) + + soma_labels = None + soma_name = self._soma_combo.value + if soma_name: + soma_labels = np.asarray(self.viewer.layers[soma_name].data) + + # Get parameters + spacing = (self._spacing_y.value, self._spacing_x.value) + + # Count cells + label_ids = np.unique(labels) + label_ids = label_ids[label_ids > 0] + total_cells = len(label_ids) + + if total_cells == 0: + self._status_label.value = 'Error: No cells found in labels' + return + + # Update UI state + self._is_running = True + self._run_button.enabled = False + self._cancel_button.enabled = True + self._progress.max = total_cells + self._progress.value = 0 + self._results = [] + + self._status_label.value = f'Analyzing 0/{total_cells} cells...' + + # Run analysis (in main thread for now, nbatch threading can be added) + try: + for result in analyze_all_cells_generator( + labels, + spacing=spacing, + soma_labels=soma_labels, + run_sholl=self._run_sholl.value, + sholl_step=self._sholl_step.value, + min_skeleton_size=self._min_size.value, + on_progress=self._on_progress_update, + ): + if not self._is_running: + # Cancelled + break + self._results.append(result) + + # Create summary DataFrame + if self._results: + summaries = [r.to_dict() for r in self._results] + self._summary_df = pd.DataFrame(summaries) + + # Update table display + self._results_table.value = self._summary_df.head(50).to_dict( + 'list' + ) + + # Save if path provided + output_path = self._output_path.value + if output_path: + self._summary_df.to_csv(output_path, index=False) + self._status_label.value = ( + f'Done! Analyzed {len(self._results)} cells. ' + f'Saved to {output_path}' + ) + else: + self._status_label.value = ( + f'Done! Analyzed {len(self._results)} cells.' + ) + else: + self._status_label.value = 'No cells passed filtering criteria' + + except Exception as e: + self._status_label.value = f'Error: {e}' + + finally: + self._is_running = False + self._run_button.enabled = True + self._cancel_button.enabled = False + + def _on_progress_update(self, current: int, total: int, label_id: int): + """Update progress bar and status.""" + self._progress.value = current + self._status_label.value = ( + f'Analyzing {current}/{total} cells (cell {label_id})...' + ) + # Allow Qt to process events + from qtpy.QtWidgets import QApplication + + QApplication.processEvents() + + def _on_cancel(self): + """Cancel running analysis.""" + self._is_running = False + self._status_label.value = 'Cancelled' + self._cancel_button.enabled = False diff --git a/src/ndev_morphology/widgets/branch_widget.py b/src/ndev_morphology/widgets/branch_widget.py new file mode 100644 index 0000000..01b6b08 --- /dev/null +++ b/src/ndev_morphology/widgets/branch_widget.py @@ -0,0 +1,120 @@ +""" +Widget for branch analysis and visualization. + +Provides interactive branch summarization with property-based coloring. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import skan +from magicgui import magic_factory + +from .._geometry import skeleton_to_paths +from ..branch import summarize_branches + +if TYPE_CHECKING: + import napari + +__all__ = ['branch_analysis'] + + +@magic_factory( + call_button='Analyze Branches', + skeleton_layer={'label': 'Skeleton Layer'}, + color_by={ + 'choices': ['branch_distance', 'tortuosity', 'branch_type'], + 'tooltip': 'Property to color branches by', + }, + edge_width={'min': 0.5, 'max': 10.0, 'step': 0.5}, +) +def branch_analysis( + skeleton_layer: napari.layers.Labels, + color_by: str = 'branch_distance', + edge_width: float = 2.0, +) -> napari.types.LayerDataTuple: + """ + Analyze skeleton branches and visualize with property-based coloring. + + Prints branch summary statistics to console. + + Parameters + ---------- + skeleton_layer : Labels + Skeleton image layer. + color_by : str + Property to use for coloring. + edge_width : float + Width of branch lines. + + Returns + ------- + list of LayerDataTuple + Shapes layer with branches as paths. + """ + skeleton_arr = np.asarray(skeleton_layer.data) + scale = skeleton_layer.scale + spacing = tuple(scale[-2:]) + + if not np.any(skeleton_arr): + raise ValueError('Skeleton is empty') + + # Create skeleton and summarize + skeleton = skan.Skeleton(skeleton_arr.astype(float), spacing=spacing) + branches = summarize_branches(skeleton) # Already includes tortuosity + + # Print summary + print('=' * 50) + print(f'Branch Analysis: {len(branches)} branches') + print(f' Total length: {branches["branch_distance"].sum():.1f}') + print(f' Mean length: {branches["branch_distance"].mean():.2f}') + if 'tortuosity' in branches.columns: + print(f' Mean tortuosity: {branches["tortuosity"].mean():.3f}') + print('=' * 50) + + # Get paths for visualization + paths, props = skeleton_to_paths(skeleton) + + if not paths: + raise ValueError('No branches found') + + # For branch_type, use discrete colors + if color_by == 'branch_type': + type_colors = { + 0: [1, 0, 0, 1], # endpoint-endpoint: red + 1: [0, 1, 0, 1], # junction-endpoint: green + 2: [0, 0, 1, 1], # junction-junction: blue + 3: [1, 1, 0, 1], # isolated cycle: yellow + } + colors = [ + type_colors.get(int(t), [0.5, 0.5, 0.5, 1]) + for t in props['branch_type'] + ] + return ( + paths, + { + 'shape_type': 'path', + 'properties': props, + 'edge_color': colors, + 'edge_width': edge_width, + 'name': 'branches (by type)', + 'scale': scale, + }, + 'shapes', + ) + + return ( + paths, + { + 'shape_type': 'path', + 'properties': props, + 'edge_color': color_by, + 'edge_colormap': 'viridis', + 'edge_width': edge_width, + 'name': f'branches (by {color_by})', + 'scale': scale, + }, + 'shapes', + ) diff --git a/src/ndev_morphology/widgets/graph_widget.py b/src/ndev_morphology/widgets/graph_widget.py new file mode 100644 index 0000000..6c71e76 --- /dev/null +++ b/src/ndev_morphology/widgets/graph_widget.py @@ -0,0 +1,177 @@ +""" +Widget for visualizing skeleton graph structure. + +Shows nodes (junctions/endpoints) and edges (branches) as separate layers. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import skan +from magicgui import magic_factory + +if TYPE_CHECKING: + import napari + +__all__ = ['skeleton_graph'] + + +@magic_factory( + call_button='Show Graph Structure', + skeleton_layer={'label': 'Skeleton Layer'}, + node_size={'min': 3, 'max': 30, 'tooltip': 'Size of node markers'}, + edge_width={'min': 0.5, 'max': 10.0, 'step': 0.5}, +) +def skeleton_graph( + skeleton_layer: napari.layers.Labels, + node_size: float = 8.0, + edge_width: float = 2.0, +) -> list[napari.types.LayerDataTuple]: + """ + Visualize skeleton as graph: nodes (junctions/endpoints) and edges (branches). + + skan represents skeletons as graphs where: + - **Nodes**: Junction points (degree >= 3) and endpoints (degree 1) + - **Edges**: The traced pixel paths between nodes (not straight lines) + - **skeleton_id**: Each disconnected component gets a unique ID + + Parameters + ---------- + skeleton_layer : Labels + Skeleton image. + node_size : float + Size of node markers. + edge_width : float + Width of edge lines. + + Returns + ------- + list of LayerDataTuple + Points layer for nodes, Shapes layer for edges. + """ + skeleton_arr = np.asarray(skeleton_layer.data) + scale = skeleton_layer.scale + spacing = tuple(scale[-2:]) + + if not np.any(skeleton_arr): + raise ValueError('Skeleton is empty') + + # Create skan Skeleton + skel = skan.Skeleton(skeleton_arr.astype(float), spacing=spacing) + + # Get branch summary for node info + branches = skan.summarize(skel, separator='_') + + # Extract unique nodes (source and destination of each branch) + # Nodes are stored as coordinates in the summary + node_coords = set() + node_types = {} # (y, x) -> 'junction' or 'endpoint' + + for _idx, row in branches.iterrows(): + # Source node + src = (row['coord_src_0'], row['coord_src_1']) + node_coords.add(src) + + # Destination node + dst = (row['coord_dst_0'], row['coord_dst_1']) + node_coords.add(dst) + + # Determine node types from branch_type + # branch_type: 0=endpoint-endpoint, 1=junction-endpoint, 2=junction-junction + bt = int(row['branch_type']) + if bt == 0: # endpoint-endpoint + node_types[src] = 'endpoint' + node_types[dst] = 'endpoint' + elif bt == 1: # junction-endpoint + # One is junction, one is endpoint - can't tell which from summary alone + # Mark as 'mixed' and we'll color by degree later + if src not in node_types: + node_types[src] = 'mixed' + if dst not in node_types: + node_types[dst] = 'mixed' + elif bt == 2: # junction-junction + node_types[src] = 'junction' + node_types[dst] = 'junction' + + # Convert to arrays + nodes = np.array(list(node_coords)) + + # Colorblind-friendly colors (Wong 2011 palette) + # Vermillion for endpoints, Blue for junctions, Orange for mixed + COLOR_ENDPOINT = [0.84, 0.37, 0.0, 1.0] # Vermillion + COLOR_JUNCTION = [0.0, 0.45, 0.70, 1.0] # Blue + COLOR_MIXED = [0.90, 0.62, 0.0, 1.0] # Orange + + # Color nodes by type + node_colors = [] + for coord in node_coords: + ntype = node_types.get(coord, 'unknown') + if ntype == 'endpoint': + node_colors.append(COLOR_ENDPOINT) + elif ntype == 'junction': + node_colors.append(COLOR_JUNCTION) + else: + node_colors.append(COLOR_MIXED) + + # Get edges as paths + paths = [] + edge_props = {'branch_distance': [], 'branch_type': [], 'skeleton_id': []} + + for i in range(skel.n_paths): + coords = skel.path_coordinates(i) + if len(coords) >= 2: + paths.append(coords) + edge_props['branch_distance'].append( + branches.iloc[i]['branch_distance'] + ) + edge_props['branch_type'].append(branches.iloc[i]['branch_type']) + edge_props['skeleton_id'].append(branches.iloc[i]['skeleton_id']) + + layers = [] + + # Nodes layer + if len(nodes) > 0: + layers.append( + ( + nodes, + { + 'name': 'Graph Nodes', + 'size': node_size, + 'face_color': node_colors, + 'symbol': 'disc', + 'scale': scale, + }, + 'points', + ) + ) + + # Edges layer + if paths: + layers.append( + ( + paths, + { + 'name': 'Graph Edges', + 'shape_type': 'path', + 'properties': edge_props, + 'edge_color': 'skeleton_id', + 'edge_colormap': 'tab10', + 'edge_width': edge_width, + 'scale': scale, + }, + 'shapes', + ) + ) + + # Print summary + print('=' * 50) + print('Skeleton Graph Structure:') + print(f' Nodes: {len(nodes)}') + print(f' Edges (branches): {len(paths)}') + print(f' Connected components: {branches["skeleton_id"].nunique()}') + print(' Node colors: Vermillion=endpoint, Blue=junction, Orange=mixed') + print('=' * 50) + + return layers diff --git a/src/ndev_morphology/widgets/labels_widget.py b/src/ndev_morphology/widgets/labels_widget.py new file mode 100644 index 0000000..032d4d9 --- /dev/null +++ b/src/ndev_morphology/widgets/labels_widget.py @@ -0,0 +1,143 @@ +""" +Labels processing widget for napari. + +Provides label refinement operations like filtering by size, +removing edge-touching labels, and connecting nearby fragments. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +from magicgui import magic_factory + +from ..labels import ( + connect_breaks_between_labels, + exclude_labels_on_edges, + filter_labels_by_size, +) + +if TYPE_CHECKING: + import napari + +__all__ = ['refine_labels_widget'] + + +@magic_factory( + call_button='Refine Labels', + labels={'label': 'Labels Layer'}, + min_size={ + 'label': 'Min Size (pixels)', + 'min': 0, + 'max': 1000000, + 'tooltip': 'Remove labels smaller than this. Set to 0 to disable.', + }, + max_size={ + 'label': 'Max Size (pixels)', + 'min': 0, + 'max': 10000000, + 'tooltip': 'Remove labels larger than this. Set to 0 to disable.', + }, + exclude_edges={ + 'label': 'Exclude Edge Labels', + 'tooltip': 'Remove labels that touch the image border.', + }, + connect_distance={ + 'label': 'Connect Distance (pixels)', + 'min': 0.0, + 'max': 50.0, + 'step': 0.5, + 'tooltip': 'Connect labels within this distance. Set to 0 to disable.', + }, +) +def refine_labels_widget( + labels: napari.layers.Labels, + min_size: int = 0, + max_size: int = 0, + exclude_edges: bool = False, + connect_distance: float = 0.0, +) -> napari.types.LayerDataTuple: + """ + Refine a labels image with filtering and connection operations. + + Apply a series of label refinement operations: + 1. Filter by size (min/max area in pixels) + 2. Exclude labels touching image edges + 3. Connect nearby label fragments + + Operations are applied in order, and each can be individually + enabled/disabled. + + Parameters + ---------- + labels : napari.types.LabelsData + Input labeled image where each unique value represents a distinct object. + min_size : int + Minimum label size in pixels. Labels smaller than this are removed. + Set to 0 to disable minimum size filtering. + max_size : int + Maximum label size in pixels. Labels larger than this are removed. + Set to 0 to disable maximum size filtering. + exclude_edges : bool + If True, remove labels that touch the image border. + connect_distance : float + Connect labels within this distance (in pixels). + Set to 0 to disable connecting. + + Returns + ------- + napari.types.LayerDataTuple + Refined labels layer. + + Notes + ----- + This widget combines multiple label operations for convenience. + Each operation can also be used independently via the API: + + - `filter_labels_by_size()` + - `exclude_labels_on_edges()` + - `connect_breaks_between_labels()` + """ + result = np.asarray(labels.data).copy() + + # Apply operations in order + # 1. Size filtering + min_sz = min_size if min_size > 0 else None + max_sz = max_size if max_size > 0 else None + if min_sz is not None or max_sz is not None: + result = filter_labels_by_size( + result, min_size=min_sz, max_size=max_sz + ) + + # 2. Edge exclusion + if exclude_edges: + result = exclude_labels_on_edges(result) + + # 3. Connect nearby fragments + if connect_distance > 0: + result = connect_breaks_between_labels( + result, connect_distance=connect_distance + ) + + # Build description of operations applied + ops = [] + if min_sz is not None: + ops.append(f'min>{min_sz}') + if max_sz is not None: + ops.append(f'max<{max_sz}') + if exclude_edges: + ops.append('no_edges') + if connect_distance > 0: + ops.append(f'connect={connect_distance:.1f}') + + suffix = f' ({", ".join(ops)})' if ops else '' + + return ( + result, + { + 'name': f'refined_labels{suffix}', + 'scale': labels.scale, + }, + 'labels', + ) diff --git a/src/ndev_morphology/widgets/pruning_widget.py b/src/ndev_morphology/widgets/pruning_widget.py new file mode 100644 index 0000000..3245a50 --- /dev/null +++ b/src/ndev_morphology/widgets/pruning_widget.py @@ -0,0 +1,159 @@ +""" +Widget for skeleton pruning operations. + +Provides interactive skeleton branch pruning with type-based filtering. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import skan +from magicgui import magic_factory + +from ..pruning import prune_short_branches + +if TYPE_CHECKING: + import napari + +__all__ = ['prune_skeleton_widget'] + + +@magic_factory( + call_button='Prune Skeleton', + skeleton_layer={'label': 'Skeleton Layer'}, + min_branch_distance={ + 'label': 'Min Branch Length', + 'min': 0.0, + 'max': 1000.0, + 'step': 1.0, + 'tooltip': 'Branches shorter than this will be pruned (in pixels or physical units).', + }, + prune_terminal={ + 'label': 'Prune Terminal Branches (type 1)', + 'tooltip': ( + 'Prune junction-to-endpoint branches. ' + 'These are the most common spurious branches from skeletonization.' + ), + }, + prune_isolated={ + 'label': 'Prune Isolated Branches (type 0)', + 'tooltip': ( + 'Prune endpoint-to-endpoint branches. ' + 'These are completely disconnected branch fragments.' + ), + }, + prune_cycles={ + 'label': 'Prune Isolated Cycles (type 3)', + 'tooltip': 'Remove closed loops. Often artifacts from holes in the image.', + }, + prune_connecting={ + 'label': 'Prune Connecting Branches (type 2)', + 'tooltip': ( + 'Prune junction-to-junction branches. ' + 'Usually you want to KEEP these as they connect major skeleton parts!' + ), + }, +) +def prune_skeleton_widget( + skeleton_layer: napari.layers.Labels, + min_branch_distance: float = 10.0, + prune_terminal: bool = True, + prune_isolated: bool = True, + prune_cycles: bool = True, + prune_connecting: bool = False, +) -> napari.types.LayerDataTuple: + """ + Prune short branches from a skeleton. + + Removes branches shorter than the specified minimum distance. + You can control which branch types are eligible for pruning. + + Branch Types + ------------ + - Type 0 (endpoint-to-endpoint): Isolated branch fragments + - Type 1 (junction-to-endpoint): Terminal branches (most common artifacts) + - Type 2 (junction-to-junction): Connecting branches (usually keep these!) + - Type 3 (isolated cycle): Closed loops (usually artifacts) + + Parameters + ---------- + skeleton_layer : Labels + Skeleton image (from skeletonize_labels widget). + min_branch_distance : float + Minimum branch length to keep. + prune_terminal : bool + Prune junction-to-endpoint branches. + prune_isolated : bool + Prune endpoint-to-endpoint branches. + prune_cycles : bool + Prune isolated cycles. + prune_connecting : bool + Prune junction-to-junction branches (careful!). + + Returns + ------- + LayerDataTuple + Labels layer with pruned skeleton. + """ + skeleton_arr = np.asarray(skeleton_layer.data) + scale = skeleton_layer.scale + spacing = tuple(scale[-2:]) + + if not np.any(skeleton_arr): + raise ValueError('Skeleton is empty') + + # Create skan Skeleton - use float to get proper measurements + skel = skan.Skeleton(skeleton_arr.astype(float), spacing=spacing) + + # Count branches before pruning + n_before = skel.n_paths + + # Prune + pruned_skel, summary = prune_short_branches( + skel, + min_branch_distance=min_branch_distance, + prune_endpoint_to_endpoint=prune_isolated, + prune_junction_to_endpoint=prune_terminal, + prune_junction_to_junction=prune_connecting, + prune_isolated_cycles=prune_cycles, + ) + + n_after = pruned_skel.n_paths + n_removed = n_before - n_after + + # Convert back to image + pruned_image = np.asarray(pruned_skel) + + # For labeled skeletons, try to preserve label IDs + # This is approximate - we mark pruned skeleton pixels with original labels + if skeleton_arr.max() > 1: + # Original was labeled - try to preserve labels + # Create output with original label IDs where pruned skeleton exists + pruned_binary = pruned_image > 0 + pruned_labeled = skeleton_arr * pruned_binary + result = pruned_labeled.astype(skeleton_arr.dtype) + else: + # Binary skeleton - return pruned labels from skan + result = pruned_image.astype(skeleton_arr.dtype) + + print(f'Pruned {n_removed} branches (from {n_before} to {n_after})') + print( + f'Remaining branch types: {summary["branch_type"].value_counts().to_dict()}' + ) + + return ( + result, + { + 'name': f'{skeleton_layer.name}_pruned', + 'scale': scale, + 'metadata': { + 'n_branches_before': n_before, + 'n_branches_after': n_after, + 'n_removed': n_removed, + 'min_branch_distance': min_branch_distance, + }, + }, + 'labels', + ) diff --git a/src/ndev_morphology/widgets/sholl_widget.py b/src/ndev_morphology/widgets/sholl_widget.py new file mode 100644 index 0000000..c7bca2d --- /dev/null +++ b/src/ndev_morphology/widgets/sholl_widget.py @@ -0,0 +1,140 @@ +""" +Sholl analysis widget for napari. + +Performs Sholl analysis on skeleton images and visualizes results +as concentric shell shapes. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import skan +from magicgui import magic_factory + +from .._geometry import sholl_shells_to_ellipses +from ..sholl import compute_sholl_profile + +if TYPE_CHECKING: + import napari + +__all__ = ['sholl_analysis'] + + +@magic_factory( + call_button='Run Sholl Analysis', + skeleton_layer={'label': 'Skeleton Layer'}, + center_point={'label': 'Center Point (optional)', 'nullable': True}, + center_y={'label': 'Center Y (pixels)', 'min': 0.0, 'max': 10000.0}, + center_x={'label': 'Center X (pixels)', 'min': 0.0, 'max': 10000.0}, + radius_step={ + 'label': 'Radius Step (pixels)', + 'min': 1.0, + 'max': 50.0, + 'tooltip': 'Step between Sholl shells in pixels.', + }, + max_radius={ + 'label': 'Max Radius (pixels)', + 'min': 0.0, + 'tooltip': 'Maximum radius. Set to 0 for auto (half diagonal).', + }, +) +def sholl_analysis( + skeleton_layer: napari.layers.Labels, + center_point: napari.layers.Points | None = None, + center_y: float = 100.0, + center_x: float = 100.0, + radius_step: float = 5.0, + max_radius: float = 0.0, +) -> napari.types.LayerDataTuple: + """ + Perform Sholl analysis and visualize with concentric shells. + + Parameters + ---------- + skeleton_layer : Labels + Skeleton image (binary or labeled). + center_point : Points, optional + Points layer with center. Uses first point if provided. + center_y, center_x : float + Manual center coordinates if no Points layer. + radius_step : float + Step between shells in pixels. + max_radius : float + Maximum radius. 0 = auto (half diagonal). + + Returns + ------- + list of LayerDataTuple + Shapes layer with Sholl shells. + """ + skeleton_arr = np.asarray(skeleton_layer.data) + scale = skeleton_layer.scale + + if not np.any(skeleton_arr): + raise ValueError('Skeleton is empty') + + # Get center from Points layer or manual input + if center_point is not None and len(center_point.data) > 0: + center_px = center_point.data[0][:2] # First point, YX + else: + center_px = np.array([center_y, center_x]) + + # Convert to physical units using scale + spacing = tuple(scale[-2:]) # YX spacing + center_physical = center_px * np.array(spacing) + + # Auto max radius if not set + if max_radius <= 0: + diag = np.sqrt( + skeleton_arr.shape[-2] ** 2 + skeleton_arr.shape[-1] ** 2 + ) + max_radius = diag / 2 + + # Create radii array (in physical units) + radii_physical = np.arange( + radius_step * spacing[0], # Start at one step + max_radius * spacing[0], + radius_step * spacing[0], + ) + + if len(radii_physical) == 0: + raise ValueError('No valid radii generated') + + # Create skeleton and run Sholl + skel = skan.Skeleton(skeleton_arr.astype(float), spacing=spacing) + result = compute_sholl_profile( + skel, center=center_physical, radii=radii_physical + ) + + # Print summary + print('=' * 50) + print('Sholl Analysis Results:') + print(f' Max crossings: {result.max_crossings}') + print(f' Critical radius: {result.critical_radius:.2f}') + print(f' Enclosing radius: {result.enclosing_radius:.2f}') + print(f' Total crossings: {result.total_crossings}') + print('=' * 50) + + # Generate shells in pixel coordinates + radii_px = result.radii / spacing[0] + shells = sholl_shells_to_ellipses(center_px, radii_px, ndim=2) + + return ( + shells, + { + 'shape_type': 'ellipse', + 'properties': { + 'radius': result.radii.tolist(), + 'crossings': result.counts.tolist(), + }, + 'edge_color': 'crossings', + 'edge_colormap': 'turbo', + 'edge_width': 1.5, + 'face_color': 'transparent', + 'name': 'Sholl shells', + 'scale': scale, + }, + 'shapes', + ) diff --git a/src/ndev_morphology/widgets/skeleton_widget.py b/src/ndev_morphology/widgets/skeleton_widget.py new file mode 100644 index 0000000..cc76130 --- /dev/null +++ b/src/ndev_morphology/widgets/skeleton_widget.py @@ -0,0 +1,86 @@ +""" +Skeleton visualization widget for napari. + +Converts skeleton images to Shapes layers with branch properties. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from magicgui import magic_factory + +from .._geometry import skeleton_to_paths +from ._utils import get_or_create_model + +if TYPE_CHECKING: + import napari + +__all__ = ['skeleton_to_shapes'] + + +@magic_factory( + call_button='Create Skeleton Shapes', + skeleton_layer={'label': 'Skeleton Layer'}, + color_by={ + 'label': 'Color by', + 'choices': ['branch_distance', 'euclidean_distance', 'branch_type'], + 'tooltip': 'Property to color branches by', + }, + edge_width={ + 'label': 'Line Width', + 'min': 0.5, + 'max': 10.0, + 'step': 0.5, + }, +) +def skeleton_to_shapes( + skeleton_layer: napari.layers.Labels, + color_by: str = 'branch_distance', + edge_width: float = 2.0, +) -> napari.types.LayerDataTuple: + """ + Convert skeleton to Shapes layer with colored branches. + + Parameters + ---------- + skeleton_layer : Labels + Skeleton image (from skeletonize_labels widget). + color_by : str + Property to use for coloring branches. + edge_width : float + Width of branch lines. + + Returns + ------- + LayerDataTuple + Shapes layer with branches as paths. + + Notes + ----- + This widget uses a cached MorphologyModel object stored in + layer.metadata. Subsequent runs reuse the same skan.Skeleton, + improving performance for repeated analysis. + """ + # Use cached model (or create if first time) + model = get_or_create_model(skeleton_layer) + + # Get paths and properties from the cached skeleton + paths, properties = skeleton_to_paths(model.skeleton) + + if not paths: + raise ValueError('No branches found in skeleton') + + return ( + paths, + { + 'shape_type': 'path', + 'properties': properties, + 'edge_color': color_by, + 'edge_colormap': 'viridis', + 'edge_width': edge_width, + 'name': f'{skeleton_layer.name}_shapes', + 'scale': skeleton_layer.scale, + }, + 'shapes', + ) diff --git a/src/ndev_morphology/widgets/skeletonize_widget.py b/src/ndev_morphology/widgets/skeletonize_widget.py new file mode 100644 index 0000000..f9ae2c9 --- /dev/null +++ b/src/ndev_morphology/widgets/skeletonize_widget.py @@ -0,0 +1,78 @@ +""" +Skeletonize labels widget for napari. + +Provides label-aware skeletonization that preserves label identities. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from magicgui import magic_factory + +from ..skeleton import separate_touching_skeleton_labels, skeletonize_labels + +if TYPE_CHECKING: + import napari + +__all__ = ['skeletonize_labels_widget'] + + +@magic_factory( + call_button='Skeletonize Labels', + labels={'label': 'Labels Layer'}, + separate_touching={ + 'label': 'Separate Touching Skeletons', + 'tooltip': ( + 'Remove pixels where skeletons from different labels touch. ' + 'This ensures skan treats each label as a separate skeleton.' + ), + }, +) +def skeletonize_labels_widget( + labels: napari.layers.Labels, + separate_touching: bool = True, +) -> napari.types.LayerDataTuple: + """ + Skeletonize a labels image while preserving label identities. + + Unlike standard skeletonization which produces a binary image, + this function preserves which original label each skeleton pixel + belongs to. This is essential for per-object analysis like + Sholl analysis on individual neurons. + + Parameters + ---------- + labels : napari.types.LabelsData + Labeled image where each unique value represents a distinct object. + separate_touching : bool + If True, remove pixels where skeletons from different labels touch. + This ensures skan treats each labeled region as a separate skeleton. + + Returns + ------- + napari.types.LayerDataTuple + Labels layer with skeletonized objects, preserving original label values. + + Notes + ----- + This is unique functionality not available in skan. Use skan's + `labels_to_skeleton_shapes` widget if you want skeleton visualization + as a Shapes layer. + """ + import numpy as np + + data = np.asarray(labels.data) + skeleton = skeletonize_labels(data) + + if separate_touching: + skeleton = separate_touching_skeleton_labels(skeleton) + + return ( + skeleton, + { + 'name': f'{labels.name}_skeleton', + 'scale': labels.scale, + }, + 'labels', + ) diff --git a/src/ndev_morphology/widgets/soma_widget.py b/src/ndev_morphology/widgets/soma_widget.py new file mode 100644 index 0000000..a87fa21 --- /dev/null +++ b/src/ndev_morphology/widgets/soma_widget.py @@ -0,0 +1,136 @@ +""" +Widget for soma detection and visualization. + +Provides interactive soma centroid detection using different methods. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal + +import numpy as np +from magicgui import magic_factory + +if TYPE_CHECKING: + import napari + +__all__ = ['soma_detection'] + + +@magic_factory( + call_button='Detect Soma', + labels={'label': 'Labels (soma regions)'}, + id_labels={ + 'label': 'ID Labels (optional)', + 'nullable': True, + 'tooltip': ( + 'Optional labels layer to assign IDs from. ' + 'Each detected soma will be assigned the ID of the overlapping ' + 'region in this layer. Useful for matching soma to skeleton labels.' + ), + }, + method={ + 'choices': ['largest_region', 'roundest_region'], + 'tooltip': ( + 'Detection method:\n' + '- largest_region: Centroid of largest connected component\n' + '- roundest_region: Most circular region (lowest eccentricity)' + ), + }, + point_size={'min': 1, 'max': 50, 'tooltip': 'Size of soma marker points'}, +) +def soma_detection( + labels: napari.layers.Labels, + id_labels: napari.layers.Labels | None = None, + method: Literal['largest_region', 'roundest_region'] = 'largest_region', + point_size: float = 10.0, +) -> napari.types.LayerDataTuple: + """ + Detect soma centroids in labeled regions. + + Returns a Points layer with detected soma positions. Each point has a + 'label_id' property that can be used to match soma to skeleton labels. + + Parameters + ---------- + labels : Labels + Label image with soma regions (e.g., nuclei or cell bodies). + id_labels : Labels, optional + Optional labels layer to assign IDs from. If provided, each detected + soma centroid is assigned the ID from this layer at that position. + This is useful when you want soma IDs to match a separate cell mask + or skeleton layer. + method : str + Detection method. + point_size : float + Size of soma marker points. + + Returns + ------- + LayerDataTuple + Points layer with soma centroids and label_id properties. + """ + from ..soma import detect_soma_centroid + + labels_data = np.asarray(labels.data) + scale = labels.scale + + # Get ID labels if provided + id_labels_data = None + if id_labels is not None: + id_labels_data = np.asarray(id_labels.data) + + # Get unique labels + label_ids = np.unique(labels_data) + label_ids = label_ids[label_ids > 0] + + # Detect soma for each label + centroids = [] + detected_labels = [] + + for label_id in label_ids: + try: + centroid = detect_soma_centroid( + labels_data, + label_id=int(label_id), + method=method, + ) + centroids.append(centroid) + + # Assign ID from id_labels if provided, otherwise use original + if id_labels_data is not None: + # Get the ID at the centroid position + centroid_int = tuple(int(c) for c in centroid) + assigned_id = int(id_labels_data[centroid_int]) + detected_labels.append(assigned_id) + else: + detected_labels.append(int(label_id)) + except (ValueError, IndexError): + continue + + if not centroids: + return ( + np.empty((0, labels_data.ndim)), + { + 'name': 'Soma Centroids', + 'size': point_size, + 'face_color': 'magenta', + }, + 'points', + ) + + points = np.array(centroids) + + return ( + points, + { + 'name': 'Soma Centroids', + 'size': point_size, + 'face_color': 'magenta', + 'border_color': 'white', + 'symbol': 'disc', + 'properties': {'label_id': detected_labels}, + 'scale': scale, + }, + 'points', + ) diff --git a/src/ndev_morphology/widgets/tree_widget.py b/src/ndev_morphology/widgets/tree_widget.py new file mode 100644 index 0000000..9fcde49 --- /dev/null +++ b/src/ndev_morphology/widgets/tree_widget.py @@ -0,0 +1,347 @@ +""" +Widget for directional tree analysis. + +Visualizes skeleton as a directed tree from soma, showing branch orders. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +from magicgui import magic_factory + +if TYPE_CHECKING: + import napari + +__all__ = ['directed_tree_analysis'] + + +# Colorblind-friendly palette for branch orders (Wong 2011) +BRANCH_ORDER_COLORS = { + 1: [0.0, 0.45, 0.70, 1.0], # Blue - primary + 2: [0.90, 0.62, 0.0, 1.0], # Orange - secondary + 3: [0.0, 0.62, 0.45, 1.0], # Bluish green - tertiary + 4: [0.80, 0.47, 0.65, 1.0], # Reddish purple - quaternary + 5: [0.94, 0.89, 0.26, 1.0], # Yellow - quinary + 0: [0.5, 0.5, 0.5, 0.5], # Gray - not in tree +} + + +@magic_factory( + call_button='Analyze Tree', + skeleton_layer={ + 'label': 'Skeleton Layer', + 'tooltip': 'Binary or labeled skeleton image.', + }, + soma_labels_layer={ + 'label': 'Soma Labels', + 'tooltip': ( + 'Labels layer containing soma/cell body regions. ' + 'Each unique label is analyzed separately.' + ), + }, + soma_label_id={ + 'label': 'Soma Label ID', + 'min': 1, + 'tooltip': 'Which soma label to analyze (1 = first label).', + }, + dilation_iterations={ + 'label': 'Soma Exclusion Dilation', + 'min': 0, + 'max': 20, + 'tooltip': ( + 'Pixels to dilate soma mask before excluding from skeleton. ' + 'Larger values create cleaner separation of branches.' + ), + }, + color_by={ + 'label': 'Color by', + 'choices': ['branch_order', 'strahler_order', 'distance_from_root'], + 'tooltip': 'Property to color branches by.', + }, + edge_width={'min': 0.5, 'max': 10.0, 'step': 0.5}, +) +def directed_tree_analysis( + skeleton_layer: napari.layers.Labels, + soma_labels_layer: napari.layers.Labels, + soma_label_id: int = 1, + dilation_iterations: int = 3, + color_by: str = 'branch_order', + edge_width: float = 3.0, +) -> list[napari.types.LayerDataTuple]: + """ + Analyze skeleton as directed trees radiating from a soma. + + This widget handles the common neuroscience case where multiple + dendrites/axons radiate outward from a central cell body (soma). + Each radiating branch is analyzed as a separate directed tree. + + The workflow is: + 1. Exclude the soma region from the skeleton (creating disconnected fragments) + 2. For each fragment, find the node closest to soma as its root + 3. Analyze each fragment as a directed tree + 4. Aggregate statistics for all trees belonging to the neuron + + Parameters + ---------- + skeleton_layer : Labels + Binary or labeled skeleton image. + soma_labels_layer : Labels + Labels layer containing soma/cell body regions. + soma_label_id : int + Which soma label to analyze (1 = first label). + dilation_iterations : int + How much to dilate soma mask before exclusion. + color_by : str + Property to use for coloring branches. + edge_width : float + Width of branch lines. + + Returns + ------- + list of LayerDataTuple + Shapes layer with colored branches, Points layer for soma centroid. + """ + from scipy import ndimage as ndi + + from ..tree import ( + compute_branch_order, + compute_strahler_order, + create_directed_trees_from_soma, + find_longest_path, + ) + + skeleton_arr = np.asarray(skeleton_layer.data) + soma_labels_arr = np.asarray(soma_labels_layer.data) + scale = skeleton_layer.scale + spacing = tuple(scale[-2:]) + + if not np.any(skeleton_arr): + raise ValueError('Skeleton is empty') + + if not np.any(soma_labels_arr == soma_label_id): + raise ValueError( + f'Soma label {soma_label_id} not found in soma labels layer' + ) + + # Create binary mask for the selected soma + soma_mask = soma_labels_arr == soma_label_id + + # Get soma centroid + soma_centroid = ndi.center_of_mass(soma_mask) + soma_centroid = np.array(soma_centroid) + + # Find which skeleton label overlaps with this soma + # This handles the case where skeleton labels may differ from soma labels + skeleton_at_soma = skeleton_arr[soma_mask] + skeleton_labels_in_soma = skeleton_at_soma[skeleton_at_soma > 0] + if len(skeleton_labels_in_soma) > 0: + # Use the most common skeleton label found in the soma region + unique_labels, counts = np.unique( + skeleton_labels_in_soma, return_counts=True + ) + skeleton_label = int(unique_labels[np.argmax(counts)]) + print( + f'Found skeleton label {skeleton_label} overlapping with soma {soma_label_id}' + ) + else: + # No skeleton directly in soma - search nearby + # Dilate soma mask and check again + dilated_soma = ndi.binary_dilation(soma_mask, iterations=10) + skeleton_near_soma = skeleton_arr[dilated_soma] + skeleton_labels_near = skeleton_near_soma[skeleton_near_soma > 0] + if len(skeleton_labels_near) > 0: + unique_labels, counts = np.unique( + skeleton_labels_near, return_counts=True + ) + skeleton_label = int(unique_labels[np.argmax(counts)]) + print( + f'Found skeleton label {skeleton_label} near soma {soma_label_id}' + ) + else: + raise ValueError( + f'No skeleton found overlapping or near soma {soma_label_id}' + ) + + # Create directed trees for all branches radiating from this soma + # Filter to only the skeleton label that matches this soma + trees = create_directed_trees_from_soma( + skeleton_image=skeleton_arr, + soma_mask=soma_mask, + soma_centroid=soma_centroid, + spacing=spacing, + dilation_iterations=dilation_iterations, + skeleton_label=skeleton_label, + ) + + if not trees: + raise ValueError( + 'No skeleton branches found radiating from soma. ' + 'Try reducing dilation_iterations or check skeleton/soma overlap.' + ) + + # Build paths for visualization from all trees + paths = [] + properties = { + 'branch_order': [], + 'strahler_order': [], + 'distance_from_root': [], + 'branch_distance': [], + 'is_terminal': [], + 'tree_id': [], # Which tree (radiating branch) this path belongs to + } + + total_branches = 0 + total_tips = 0 + total_junctions = 0 + max_path_length = 0.0 + order_distribution = {} + + for tree_idx, tree in enumerate(trees): + # Compute orders for this tree + branch_orders = compute_branch_order(tree) + strahler_orders = compute_strahler_order(tree) + + # Find longest path in this tree + _, path_length = find_longest_path(tree) + max_path_length = max(max_path_length, path_length) + + total_tips += tree.n_tips + total_junctions += tree.n_junctions + + # Compute distance from root for each node + node_distances = {} + for node in tree.graph.nodes(): + node_distances[node] = tree.distance_to_node(node) + + # Add paths from this tree + for u, v in tree.graph.edges(): + edge_data = tree.graph.get_edge_data(u, v) + branch_dist = edge_data.get('branch_distance', 0.0) + + # Get path coordinates for this edge + # The 'path' attribute contains coordinates from skeleton_to_nx + if 'path' in edge_data: + # Path is stored directly from skeleton_to_nx + coords = edge_data['path'] + else: + # Fallback: create simple line between nodes + # Note: skeleton.coordinates are in pixel units + coords = np.array( + [ + tree.skeleton.coordinates[u], + tree.skeleton.coordinates[v], + ] + ) + + if len(coords) >= 2: + paths.append(coords) + order = branch_orders.get((u, v), 0) + properties['branch_order'].append(order) + properties['strahler_order'].append( + strahler_orders.get((u, v), 0) + ) + properties['distance_from_root'].append( + node_distances.get(u, 0.0) + ) + properties['branch_distance'].append(branch_dist) + properties['is_terminal'].append(tree.graph.out_degree(v) == 0) + properties['tree_id'].append(tree_idx) + + total_branches += 1 + order_distribution[order] = ( + order_distribution.get(order, 0) + 1 + ) + + layers = [] + + # Branches layer + if paths: + if color_by == 'branch_order': + # Use categorical colors for orders + edge_colors = [] + for order in properties['branch_order']: + color = BRANCH_ORDER_COLORS.get( + order, BRANCH_ORDER_COLORS.get(5) + ) + if order > 5: + # Cycle through colors for higher orders + cycle_order = ((order - 1) % 5) + 1 + color = BRANCH_ORDER_COLORS.get(cycle_order) + edge_colors.append(color) + + layers.append( + ( + paths, + { + 'name': f'Directed Trees (soma {soma_label_id})', + 'shape_type': 'path', + 'properties': properties, + 'edge_color': edge_colors, + 'edge_width': edge_width, + 'scale': scale, + }, + 'shapes', + ) + ) + else: + # Use colormap for continuous properties + layers.append( + ( + paths, + { + 'name': f'Directed Trees (soma {soma_label_id})', + 'shape_type': 'path', + 'properties': properties, + 'edge_color': color_by, + 'edge_colormap': 'viridis', + 'edge_width': edge_width, + 'scale': scale, + }, + 'shapes', + ) + ) + + # Soma centroid marker + soma_centroid_scaled = soma_centroid * np.array( + scale[-len(soma_centroid) :] + ) + layers.append( + ( + np.array([soma_centroid_scaled]), + { + 'name': f'Soma Centroid (label {soma_label_id})', + 'size': 15, + 'face_color': [0.84, 0.37, 0.0, 1.0], # Vermillion + 'border_color': 'white', + 'border_width': 0.2, + 'symbol': 'star', + 'scale': (1.0,) * len(scale), # Already scaled + }, + 'points', + ) + ) + + # Print summary + print('=' * 60) + print(f'Directed Tree Analysis - Soma {soma_label_id}') + print('=' * 60) + print(f' Soma centroid: ({soma_centroid[0]:.1f}, {soma_centroid[1]:.1f})') + print(f' Dilation applied: {dilation_iterations} pixels') + print(f' Radiating branches (trees): {len(trees)}') + print(f' Total branches: {total_branches}') + print(f' Total tips: {total_tips}') + print(f' Total junctions: {total_junctions}') + print(f' Longest path length: {max_path_length:.2f}') + print() + print('Branch Order Distribution:') + for order in sorted(order_distribution.keys()): + if order > 0: + count = order_distribution[order] + print(f' Order {order}: {count} branches') + print() + print('Colors: Blue=1°, Orange=2°, Green=3°, Purple=4°, Yellow=5°') + print('=' * 60) + + return layers diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..cd70176 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,104 @@ +"""Shared test fixtures for ndev-morphology tests.""" + +import numpy as np +import pytest +import skan + + +@pytest.fixture +def simple_labels_2d(): + """Simple 2D label image with two rectangular regions.""" + labels = np.zeros((64, 64), dtype=np.uint16) + labels[10:20, 10:30] = 1 # Horizontal rectangle + labels[30:50, 25:35] = 2 # Vertical rectangle + return labels + + +@pytest.fixture +def simple_skeleton_2d(): + """Simple 2D binary skeleton (cross shape).""" + skeleton = np.zeros((64, 64), dtype=bool) + # Vertical line + skeleton[10:50, 32] = True + # Horizontal line + skeleton[30, 10:55] = True + return skeleton + + +@pytest.fixture +def branching_skeleton_2d(): + """2D skeleton with multiple branches for testing.""" + skeleton = np.zeros((100, 100), dtype=bool) + + # Main trunk + skeleton[50, 20:80] = True + + # Branch 1: up-left + for i in range(20): + skeleton[50 - i, 30 + i] = True + + # Branch 2: up-right + for i in range(15): + skeleton[50 - i, 60 - i] = True + + # Branch 3: down + skeleton[50:70, 50] = True + + return skeleton + + +@pytest.fixture +def branching_skan_skeleton(branching_skeleton_2d): + """skan.Skeleton from branching_skeleton_2d.""" + return skan.Skeleton(branching_skeleton_2d.astype(float)) + + +@pytest.fixture +def circular_mask(): + """Circular binary mask centered in image.""" + mask = np.zeros((64, 64), dtype=bool) + center = (32, 32) + radius = 10 + y, x = np.ogrid[:64, :64] + mask[(y - center[0]) ** 2 + (x - center[1]) ** 2 <= radius**2] = True + return mask + + +@pytest.fixture +def skeleton_with_center(): + """skan.Skeleton radiating from center point for Sholl testing.""" + skeleton = np.zeros((100, 100), dtype=bool) + center = (50, 50) + + # Create rays radiating from center + for angle in np.linspace(0, 2 * np.pi, 8, endpoint=False): + for r in range(5, 40): + y = int(center[0] + r * np.sin(angle)) + x = int(center[1] + r * np.cos(angle)) + if 0 <= y < 100 and 0 <= x < 100: + skeleton[y, x] = True + + skel = skan.Skeleton(skeleton.astype(float)) + return skel, np.array(center) + + +@pytest.fixture +def simple_labels_3d(): + """Simple 3D label image.""" + labels = np.zeros((32, 64, 64), dtype=np.uint16) + labels[10:20, 20:40, 20:40] = 1 + labels[15:25, 30:50, 35:55] = 2 + return labels + + +@pytest.fixture +def simple_skeleton_3d(): + """skan.Skeleton for 3D skeleton testing.""" + skeleton = np.zeros((32, 64, 64), dtype=bool) + # Line along Z axis + skeleton[:, 32, 32] = True + # Line along Y axis + skeleton[16, :, 32] = True + # Line along X axis + skeleton[16, 32, :] = True + return skan.Skeleton(skeleton.astype(float)) diff --git a/tests/test_analysis.py b/tests/test_analysis.py new file mode 100644 index 0000000..763e794 --- /dev/null +++ b/tests/test_analysis.py @@ -0,0 +1,227 @@ +"""Tests for analysis module.""" + +import numpy as np +import pandas as pd +import pytest + +from ndev_morphology import ( + CellAnalysisResult, + aggregate_branch_stats, + analyze_all_cells, + analyze_single_cell, +) + + +@pytest.fixture +def simple_cell_labels(): + """Create a simple cell-like label for testing.""" + labels = np.zeros((100, 100), dtype=np.uint16) + + # Cell body (soma-like) + labels[45:55, 45:55] = 1 + + # Dendrite-like extensions + labels[40:46, 20:46] = 1 # Left branch + labels[54:60, 54:80] = 1 # Right branch + labels[20:46, 48:52] = 1 # Top branch + labels[54:80, 48:52] = 1 # Bottom branch + + return labels + + +@pytest.fixture +def multi_cell_labels(): + """Create label image with multiple cells.""" + labels = np.zeros((200, 200), dtype=np.uint16) + + # Cell 1 - top left + labels[20:30, 20:30] = 1 + labels[25:28, 30:60] = 1 # Branch + + # Cell 2 - top right + labels[20:30, 120:130] = 2 + labels[25:28, 130:170] = 2 # Branch + + # Cell 3 - bottom center + labels[150:160, 90:100] = 3 + labels[155:158, 50:90] = 3 # Left branch + labels[155:158, 100:150] = 3 # Right branch + + return labels + + +class TestCellAnalysisResult: + """Tests for CellAnalysisResult dataclass.""" + + def test_summary_computed_on_init(self, simple_cell_labels): + """Summary statistics are computed on initialization.""" + result = analyze_single_cell(simple_cell_labels, label_id=1) + + assert isinstance(result.summary, dict) + assert 'label_id' in result.summary + assert 'n_branches' in result.summary + assert 'total_length' in result.summary + + def test_to_dict_returns_summary(self, simple_cell_labels): + """to_dict returns the summary dictionary.""" + result = analyze_single_cell(simple_cell_labels, label_id=1) + + result_dict = result.to_dict() + + assert result_dict == result.summary + + +class TestAnalyzeSingleCell: + """Tests for analyze_single_cell function.""" + + def test_returns_cell_analysis_result(self, simple_cell_labels): + """Returns a CellAnalysisResult object.""" + result = analyze_single_cell(simple_cell_labels, label_id=1) + + assert isinstance(result, CellAnalysisResult) + + def test_has_skeleton(self, simple_cell_labels): + """Result contains skan.Skeleton object.""" + import skan + + result = analyze_single_cell(simple_cell_labels, label_id=1) + + assert isinstance(result.skeleton, skan.Skeleton) + + def test_has_branches_dataframe(self, simple_cell_labels): + """Result contains branches DataFrame.""" + result = analyze_single_cell(simple_cell_labels, label_id=1) + + assert isinstance(result.branches, pd.DataFrame) + assert len(result.branches) > 0 + + def test_has_sholl_result(self, simple_cell_labels): + """Result contains ShollResult when run_sholl=True.""" + from ndev_morphology import ShollResult + + result = analyze_single_cell( + simple_cell_labels, label_id=1, run_sholl=True + ) + + assert isinstance(result.sholl, ShollResult) + + def test_no_sholl_when_disabled(self, simple_cell_labels): + """Sholl is None when run_sholl=False.""" + result = analyze_single_cell( + simple_cell_labels, label_id=1, run_sholl=False + ) + + assert result.sholl is None + + def test_spacing_used(self, simple_cell_labels): + """Physical spacing is used in analysis.""" + result_pixel = analyze_single_cell( + simple_cell_labels, label_id=1, spacing=(1.0, 1.0) + ) + result_scaled = analyze_single_cell( + simple_cell_labels, label_id=1, spacing=(0.5, 0.5) + ) + + # Total length should differ based on spacing + assert ( + result_scaled.summary['total_length'] + < result_pixel.summary['total_length'] + ) + + def test_no_label_id_uses_all_nonzero(self, simple_cell_labels): + """When label_id is None, uses all non-zero pixels.""" + result = analyze_single_cell(simple_cell_labels, label_id=None) + + assert result.label_id == 1 # Gets set to 1 internally + + def test_branches_have_label_id_column(self, simple_cell_labels): + """Branches DataFrame includes label_id column.""" + result = analyze_single_cell(simple_cell_labels, label_id=1) + + assert 'label_id' in result.branches.columns + assert (result.branches['label_id'] == 1).all() + + +class TestAnalyzeAllCells: + """Tests for analyze_all_cells function.""" + + def test_returns_summary_and_results(self, multi_cell_labels): + """Returns tuple of DataFrame and list of results.""" + summary, results = analyze_all_cells(multi_cell_labels) + + assert isinstance(summary, pd.DataFrame) + assert isinstance(results, list) + + def test_one_row_per_cell(self, multi_cell_labels): + """Summary has one row per analyzed cell.""" + summary, results = analyze_all_cells(multi_cell_labels) + + # Should have 3 cells + assert len(summary) == 3 + assert len(results) == 3 + + def test_results_match_summary(self, multi_cell_labels): + """Results list matches summary DataFrame rows.""" + summary, results = analyze_all_cells(multi_cell_labels) + + assert len(summary) == len(results) + for i, result in enumerate(results): + assert result.label_id == summary.iloc[i]['label_id'] + + def test_skips_small_skeletons(self, multi_cell_labels): + """Skips cells with skeletons below min_skeleton_size.""" + # Add a tiny cell + labels = multi_cell_labels.copy() + labels[5:7, 5:7] = 4 # Very small, will have tiny skeleton + + summary, results = analyze_all_cells(labels, min_skeleton_size=20) + + # Should skip the tiny cell (label 4) + label_ids = summary['label_id'].tolist() + assert 4 not in label_ids + + def test_spacing_applied_to_all(self, multi_cell_labels): + """Spacing is applied to all cells.""" + summary_pixel, _ = analyze_all_cells( + multi_cell_labels, spacing=(1.0, 1.0) + ) + summary_scaled, _ = analyze_all_cells( + multi_cell_labels, spacing=(0.5, 0.5) + ) + + # Total lengths should differ + assert ( + summary_scaled['total_length'].sum() + < summary_pixel['total_length'].sum() + ) + + +class TestAggregateBranchStats: + """Tests for aggregate_branch_stats function.""" + + def test_combines_all_branches(self, multi_cell_labels): + """Combines branches from all cells.""" + _, results = analyze_all_cells(multi_cell_labels) + + all_branches = aggregate_branch_stats(results) + + # Total branches should equal sum from all cells + expected_count = sum(len(r.branches) for r in results) + assert len(all_branches) == expected_count + + def test_preserves_label_id(self, multi_cell_labels): + """Combined DataFrame preserves label_id column.""" + _, results = analyze_all_cells(multi_cell_labels) + + all_branches = aggregate_branch_stats(results) + + assert 'label_id' in all_branches.columns + # Should have branches from multiple cells + assert len(all_branches['label_id'].unique()) > 1 + + def test_empty_results_returns_empty_df(self): + """Returns empty DataFrame for empty results list.""" + result = aggregate_branch_stats([]) + + assert isinstance(result, pd.DataFrame) + assert len(result) == 0 diff --git a/tests/test_branch.py b/tests/test_branch.py new file mode 100644 index 0000000..dcc8065 --- /dev/null +++ b/tests/test_branch.py @@ -0,0 +1,218 @@ +"""Tests for branch module.""" + +import numpy as np +import pandas as pd +import pytest +import skan + +from ndev_morphology import ( + BranchType, + compute_tortuosity, + filter_branches_by_type, + summarize_branches, +) + + +@pytest.fixture +def branching_skeleton(): + """Create a branching skeleton for testing.""" + skeleton = np.zeros((100, 100), dtype=bool) + + # Main horizontal trunk + skeleton[50, 20:80] = True + + # Branch up-left + for i in range(20): + skeleton[50 - i, 30 + i] = True + + # Branch up-right + for i in range(15): + skeleton[50 - i, 60 - i] = True + + # Branch down + skeleton[50:70, 50] = True + + return skan.Skeleton(skeleton.astype(float), spacing=(1.0, 1.0)) + + +@pytest.fixture +def simple_intensity_image(): + """Create a simple intensity image for testing.""" + img = np.zeros((100, 100), dtype=np.float32) + img[40:60, :] = 100.0 # High intensity band + return img + + +class TestBranchType: + """Tests for BranchType enum.""" + + def test_branch_type_values(self): + """Branch type values match skan conventions.""" + assert BranchType.ENDPOINT_TO_ENDPOINT == 0 + assert BranchType.JUNCTION_TO_ENDPOINT == 1 + assert BranchType.JUNCTION_TO_JUNCTION == 2 + assert BranchType.ISOLATED_CYCLE == 3 + + def test_branch_type_label(self): + """Branch type labels are human-readable.""" + assert BranchType.label(0) == 'endpoint-endpoint' + assert BranchType.label(1) == 'junction-endpoint' + assert BranchType.label(2) == 'junction-junction' + assert BranchType.label(3) == 'isolated-cycle' + assert BranchType.label(99) == 'unknown' + + +class TestSummarizeBranches: + """Tests for summarize_branches function.""" + + def test_returns_dataframe(self, branching_skeleton): + """Returns a pandas DataFrame.""" + result = summarize_branches(branching_skeleton) + + assert isinstance(result, pd.DataFrame) + assert len(result) > 0 + + def test_has_expected_columns(self, branching_skeleton): + """Result has expected columns from skan plus extras.""" + result = summarize_branches(branching_skeleton) + + # skan columns + assert 'branch_distance' in result.columns + assert 'euclidean_distance' in result.columns + assert 'branch_type' in result.columns + + # Added columns + assert 'branch_type_label' in result.columns + assert 'tortuosity' in result.columns + + def test_tortuosity_computed(self, branching_skeleton): + """Tortuosity is computed by default.""" + result = summarize_branches(branching_skeleton) + + assert 'tortuosity' in result.columns + # Tortuosity should be >= 1.0 (path_length >= euclidean_distance) + assert (result['tortuosity'] >= 1.0).all() + + def test_no_tortuosity_option(self, branching_skeleton): + """Can disable tortuosity computation.""" + result = summarize_branches( + branching_skeleton, compute_tortuosity=False + ) + + assert 'tortuosity' not in result.columns + + def test_branch_type_labels_added(self, branching_skeleton): + """Human-readable branch type labels are added.""" + result = summarize_branches(branching_skeleton) + + assert 'branch_type_label' in result.columns + # Check labels are valid + valid_labels = { + 'endpoint-endpoint', + 'junction-endpoint', + 'junction-junction', + 'isolated-cycle', + } + assert all( + label in valid_labels for label in result['branch_type_label'] + ) + + def test_with_intensity_image( + self, branching_skeleton, simple_intensity_image + ): + """Intensity measurements are added when image provided.""" + result = summarize_branches( + branching_skeleton, intensity_image=simple_intensity_image + ) + + # Check for intensity columns (either our custom ones or skan's) + has_mean = ( + 'mean_intensity' in result.columns + or 'mean_pixel_value' in result.columns + ) + has_std = ( + 'std_intensity' in result.columns + or 'stdev_pixel_value' in result.columns + ) + assert has_mean + assert has_std + + +class TestComputeTortuosity: + """Tests for compute_tortuosity function.""" + + def test_straight_line_tortuosity_one(self): + """Straight lines have tortuosity of 1.0.""" + branch_dist = np.array([10.0, 20.0, 5.0]) + euclidean_dist = np.array([10.0, 20.0, 5.0]) + + result = compute_tortuosity(branch_dist, euclidean_dist) + + np.testing.assert_array_almost_equal(result, [1.0, 1.0, 1.0]) + + def test_curved_branch_tortuosity_greater_than_one(self): + """Curved branches have tortuosity > 1.0.""" + branch_dist = np.array([15.0, 25.0]) + euclidean_dist = np.array([10.0, 20.0]) + + result = compute_tortuosity(branch_dist, euclidean_dist) + + assert result[0] == 1.5 + assert result[1] == 1.25 + + def test_zero_euclidean_returns_one(self): + """Zero euclidean distance returns 1.0 (not inf).""" + branch_dist = np.array([5.0, 10.0]) + euclidean_dist = np.array([0.0, 10.0]) + + result = compute_tortuosity(branch_dist, euclidean_dist) + + assert result[0] == 1.0 + assert result[1] == 1.0 + + +class TestFilterBranchesByType: + """Tests for filter_branches_by_type function.""" + + @pytest.fixture + def sample_branches(self): + """Create sample branch DataFrame.""" + return pd.DataFrame( + { + 'branch_type': [0, 1, 1, 2, 2, 2, 3], + 'branch_distance': [10, 20, 15, 25, 30, 22, 5], + } + ) + + def test_filter_single_type_int(self, sample_branches): + """Filter by single branch type as int.""" + result = filter_branches_by_type(sample_branches, 1) + + assert len(result) == 2 + assert (result['branch_type'] == 1).all() + + def test_filter_single_type_enum(self, sample_branches): + """Filter by single branch type as enum.""" + result = filter_branches_by_type( + sample_branches, BranchType.JUNCTION_TO_JUNCTION + ) + + assert len(result) == 3 + assert (result['branch_type'] == 2).all() + + def test_filter_multiple_types(self, sample_branches): + """Filter by multiple branch types.""" + result = filter_branches_by_type( + sample_branches, + [BranchType.JUNCTION_TO_ENDPOINT, BranchType.JUNCTION_TO_JUNCTION], + ) + + assert len(result) == 5 + assert set(result['branch_type'].unique()) == {1, 2} + + def test_returns_copy(self, sample_branches): + """Returns a copy, not a view.""" + result = filter_branches_by_type(sample_branches, 1) + + result['branch_distance'] = 999 + assert sample_branches['branch_distance'].iloc[1] != 999 diff --git a/tests/test_geometry.py b/tests/test_geometry.py new file mode 100644 index 0000000..aef6fa0 --- /dev/null +++ b/tests/test_geometry.py @@ -0,0 +1,148 @@ +"""Tests for geometry module.""" + +import numpy as np +import pandas as pd +import pytest +import skan + +from ndev_morphology import sholl_shells_to_ellipses, skeleton_to_paths + + +@pytest.fixture +def simple_skeleton(): + """Create a simple skeleton for testing.""" + skeleton = np.zeros((50, 50), dtype=bool) + # Horizontal line + skeleton[25, 10:40] = True + # Vertical branch + skeleton[15:35, 25] = True + return skan.Skeleton(skeleton.astype(float), spacing=(1.0, 1.0)) + + +class TestSkeletonToPaths: + """Tests for skeleton_to_paths function.""" + + def test_returns_paths_and_properties(self, simple_skeleton): + """Returns list of paths and DataFrame of properties.""" + paths, properties = skeleton_to_paths(simple_skeleton) + + assert isinstance(paths, list) + assert isinstance(properties, pd.DataFrame) + + def test_paths_are_coordinate_arrays(self, simple_skeleton): + """Each path is a numpy array of coordinates.""" + paths, _ = skeleton_to_paths(simple_skeleton) + + assert len(paths) > 0 + for path in paths: + assert isinstance(path, np.ndarray) + assert path.ndim == 2 + assert path.shape[1] == 2 # (Y, X) coordinates + + def test_properties_has_path_id(self, simple_skeleton): + """Properties DataFrame includes path_id column.""" + _, properties = skeleton_to_paths(simple_skeleton) + + assert 'path_id' in properties.columns + # path_id should be sequential + expected_ids = np.arange(len(properties)) + np.testing.assert_array_equal( + properties['path_id'].values, expected_ids + ) + + def test_properties_has_branch_distance(self, simple_skeleton): + """Properties DataFrame includes branch_distance column.""" + _, properties = skeleton_to_paths(simple_skeleton) + + assert 'branch_distance' in properties.columns + # All distances should be positive + assert (properties['branch_distance'] >= 0).all() + + def test_number_of_paths_matches_properties(self, simple_skeleton): + """Number of paths matches number of rows in properties.""" + paths, properties = skeleton_to_paths(simple_skeleton) + + assert len(paths) == len(properties) + + +class TestShollShellsToEllipses: + """Tests for sholl_shells_to_ellipses function.""" + + def test_returns_list_of_arrays(self): + """Returns list of numpy arrays.""" + center = [50, 50] + radii = [10, 20, 30] + + result = sholl_shells_to_ellipses(center, radii) + + assert isinstance(result, list) + assert len(result) == 3 + + def test_ellipse_bounding_box_shape_2d(self): + """Each ellipse has 4 corner points in 2D.""" + center = [50, 50] + radii = [10] + + result = sholl_shells_to_ellipses(center, radii, ndim=2) + + assert len(result) == 1 + ellipse = result[0] + assert ellipse.shape == (4, 2) # 4 corners, 2D coords + + def test_ellipse_bounding_box_shape_3d(self): + """Each ellipse has 4 corner points in 3D.""" + center = [25, 50, 50] # Z, Y, X + radii = [10] + + result = sholl_shells_to_ellipses(center, radii, ndim=3) + + assert len(result) == 1 + ellipse = result[0] + assert ellipse.shape == (4, 3) # 4 corners, 3D coords + + def test_ellipse_corners_2d(self): + """2D ellipse corners are computed correctly.""" + center = [100, 150] # Y, X + radii = [10] + + result = sholl_shells_to_ellipses(center, radii, ndim=2) + + ellipse = result[0] + # Expected corners for radius 10: + # top-left: (90, 140), top-right: (90, 160) + # bottom-right: (110, 160), bottom-left: (110, 140) + expected = np.array( + [ + [90, 140], # top-left + [90, 160], # top-right + [110, 160], # bottom-right + [110, 140], # bottom-left + ] + ) + np.testing.assert_array_equal(ellipse, expected) + + def test_multiple_radii(self): + """Multiple radii produce multiple ellipses.""" + center = [50, 50] + radii = [10, 20, 30, 40, 50] + + result = sholl_shells_to_ellipses(center, radii) + + assert len(result) == 5 + + def test_ellipse_size_increases_with_radius(self): + """Larger radii produce larger ellipse bounding boxes.""" + center = [50, 50] + radii = [10, 20] + + result = sholl_shells_to_ellipses(center, radii) + + # Check that the second ellipse is larger + # By comparing the span from top-left to bottom-right + ellipse_small = result[0] + ellipse_large = result[1] + + span_small = ellipse_small[2, 0] - ellipse_small[0, 0] # Y span + span_large = ellipse_large[2, 0] - ellipse_large[0, 0] + + assert span_large > span_small diff --git a/tests/test_labels.py b/tests/test_labels.py new file mode 100644 index 0000000..8ed33ee --- /dev/null +++ b/tests/test_labels.py @@ -0,0 +1,162 @@ +"""Tests for labels module.""" + +import numpy as np + +from ndev_morphology import ( + connect_breaks_between_labels, + exclude_labels_on_edges, + filter_labels_by_size, +) + + +class TestFilterLabelsBySize: + """Tests for filter_labels_by_size function.""" + + def test_filter_by_min_size(self): + """Labels smaller than min_size are removed.""" + labels = np.zeros((50, 50), dtype=np.uint16) + labels[5:10, 5:10] = 1 # 25 pixels + labels[20:40, 20:40] = 2 # 400 pixels + + result = filter_labels_by_size(labels, min_size=100) + + assert 1 not in result + assert 2 in result + assert np.sum(result == 2) == 400 + + def test_filter_by_max_size(self): + """Labels larger than max_size are removed.""" + labels = np.zeros((50, 50), dtype=np.uint16) + labels[5:10, 5:10] = 1 # 25 pixels + labels[20:40, 20:40] = 2 # 400 pixels + + result = filter_labels_by_size(labels, max_size=100) + + assert 1 in result + assert 2 not in result + + def test_filter_by_min_and_max_size(self): + """Labels outside range are removed.""" + labels = np.zeros((50, 50), dtype=np.uint16) + labels[5:10, 5:10] = 1 # 25 pixels - too small + labels[15:20, 15:20] = 2 # 25 pixels - too small + labels[25:30, 25:35] = 3 # 50 pixels - in range + labels[35:45, 35:45] = 4 # 100 pixels - too big + + result = filter_labels_by_size(labels, min_size=30, max_size=80) + + assert 1 not in result + assert 2 not in result + assert 3 in result + assert 4 not in result + + def test_no_filter_returns_copy(self): + """No min/max returns a copy of input.""" + labels = np.array([[1, 2], [3, 4]], dtype=np.uint16) + + result = filter_labels_by_size(labels) + + np.testing.assert_array_equal(result, labels) + assert result is not labels + + def test_preserves_dtype(self): + """Output dtype matches input dtype.""" + labels = np.array([[0, 1], [2, 0]], dtype=np.uint32) + + result = filter_labels_by_size(labels, min_size=1) + + assert result.dtype == labels.dtype + + +class TestExcludeLabelsOnEdges: + """Tests for exclude_labels_on_edges function.""" + + def test_removes_edge_touching_labels(self): + """Labels touching image border are removed.""" + labels = np.zeros((20, 20), dtype=np.uint16) + labels[0:5, 5:10] = 1 # Touches top edge + labels[8:12, 8:12] = 2 # Interior, not touching + + result = exclude_labels_on_edges(labels) + + assert 1 not in result + assert 2 in result + + def test_removes_all_edges(self): + """Labels on any edge are removed.""" + labels = np.zeros((20, 20), dtype=np.uint16) + labels[0:3, 10:12] = 1 # Top edge + labels[17:20, 10:12] = 2 # Bottom edge + labels[10:12, 0:3] = 3 # Left edge + labels[10:12, 17:20] = 4 # Right edge + labels[8:12, 8:12] = 5 # Interior + + result = exclude_labels_on_edges(labels) + + assert 1 not in result + assert 2 not in result + assert 3 not in result + assert 4 not in result + assert 5 in result + + def test_empty_image(self): + """Empty image returns empty image.""" + labels = np.zeros((10, 10), dtype=np.uint16) + + result = exclude_labels_on_edges(labels) + + assert np.all(result == 0) + + +class TestConnectBreaksBetweenLabels: + """Tests for connect_breaks_between_labels function.""" + + def test_connects_nearby_fragments(self): + """Nearby label fragments are connected.""" + labels = np.zeros((30, 30), dtype=np.uint16) + labels[10:15, 5:10] = 1 # First fragment + labels[10:15, 12:17] = 1 # Second fragment, 2 pixels away + + result = connect_breaks_between_labels(labels, connect_distance=4.0) + + # Both fragments should now be part of same connected component + # (though possibly with a different label ID) + assert np.sum(result > 0) > 0 + + def test_small_distance_no_connection(self): + """Fragments too far apart remain separate.""" + labels = np.zeros((50, 50), dtype=np.uint16) + labels[10:15, 5:10] = 1 # First fragment + labels[10:15, 25:30] = 1 # Second fragment, far away + + result = connect_breaks_between_labels(labels, connect_distance=2.0) + + # Should still have separate components + from skimage import measure + + n_regions = len(measure.regionprops(result)) + assert n_regions == 2 + + def test_zero_distance_returns_copy(self): + """Distance < 1 pixel returns input with same structure.""" + labels = np.array([[1, 0, 2], [0, 0, 0], [3, 0, 4]], dtype=np.uint16) + + result = connect_breaks_between_labels(labels, connect_distance=0.5) + + # With radius < 1, no connection happens, but relabeling may occur + # Check that the structure (mask) is preserved + np.testing.assert_array_equal(result > 0, labels > 0) + + def test_preserves_mask_extent(self): + """Connected result stays within original label extent.""" + labels = np.zeros((30, 30), dtype=np.uint16) + labels[10:12, 10:12] = 1 + labels[14:16, 14:16] = 2 + + original_mask = labels > 0 + result = connect_breaks_between_labels(labels, connect_distance=6.0) + result_mask = result > 0 + + # Result mask should be subset of original mask extent + # (the function masks back to original extent) + np.testing.assert_array_equal(result_mask, original_mask) diff --git a/tests/test_model.py b/tests/test_model.py new file mode 100644 index 0000000..d7ecccf --- /dev/null +++ b/tests/test_model.py @@ -0,0 +1,290 @@ +""" +Tests for the MorphologyModel data model. +""" + +import numpy as np +import pandas as pd +import pytest +import skan + +from ndev_morphology.model import ( + METADATA_KEY, + MorphologyModel, + attach_model_to_layer, + get_model_from_layer, +) + + +@pytest.fixture +def simple_skeleton(): + """Create a simple Y-shaped skeleton.""" + skeleton = np.zeros((50, 50), dtype=np.uint8) + # Main trunk + skeleton[25, 10:30] = 1 + # Two branches + skeleton[15:26, 30] = 1 # Upper branch + skeleton[25:36, 30] = 1 # Lower branch + return skeleton + + +@pytest.fixture +def simple_model(simple_skeleton): + """Create MorphologyModel from simple skeleton.""" + return MorphologyModel.from_array( + simple_skeleton, + spacing=(1.0, 1.0), + source_name='test_skeleton', + ) + + +class TestMorphologyModelCreation: + """Tests for creating MorphologyModel objects.""" + + def test_from_array(self, simple_skeleton): + """Can create from numpy array.""" + model = MorphologyModel.from_array(simple_skeleton, spacing=(0.2, 0.2)) + assert isinstance(model.skeleton, skan.Skeleton) + assert model.spacing == (0.2, 0.2) + + def test_from_array_with_name(self, simple_skeleton): + """Source name is stored.""" + model = MorphologyModel.from_array( + simple_skeleton, source_name='my_skeleton' + ) + assert model.source_layer_name == 'my_skeleton' + + def test_direct_construction(self, simple_skeleton): + """Can construct directly with skan.Skeleton.""" + skeleton = skan.Skeleton( + simple_skeleton.astype(float), spacing=(1.0, 1.0) + ) + model = MorphologyModel( + skeleton=skeleton, + spacing=(1.0, 1.0), + source_layer_name='direct', + ) + assert model.n_branches > 0 + + +class TestMorphologyModelProperties: + """Tests for computed properties.""" + + def test_summary_is_dataframe(self, simple_model): + """Summary property returns a DataFrame.""" + assert isinstance(simple_model.summary, pd.DataFrame) + + def test_summary_is_cached(self, simple_model): + """Summary is computed once and cached.""" + summary1 = simple_model.summary + summary2 = simple_model.summary + assert summary1 is summary2 # Same object + + def test_n_branches(self, simple_model): + """n_branches matches summary length.""" + assert simple_model.n_branches == len(simple_model.summary) + assert simple_model.n_branches > 0 + + def test_total_length(self, simple_model): + """total_length is sum of branch distances.""" + expected = simple_model.summary['branch_distance'].sum() + assert simple_model.total_length == pytest.approx(expected) + + def test_repr(self, simple_model): + """repr includes key information.""" + rep = repr(simple_model) + assert 'MorphologyModel' in rep + assert 'n_branches=' in rep + assert 'total_length=' in rep + + +class TestBranchComputation: + """Tests for branch analysis methods.""" + + def test_compute_branches(self, simple_model): + """compute_branches returns extended DataFrame.""" + branches = simple_model.compute_branches() + assert isinstance(branches, pd.DataFrame) + assert 'branch_distance' in branches.columns + + def test_compute_branches_cached(self, simple_model): + """compute_branches result is cached.""" + branches1 = simple_model.compute_branches() + branches2 = simple_model.compute_branches() + assert branches1 is branches2 + + +class TestShollCaching: + """Tests for Sholl result caching.""" + + def test_sholl_result_initially_none(self, simple_model): + """No Sholl result before computation.""" + result = simple_model.get_sholl_result('10.0,10.0') + assert result is None + + def test_add_and_get_sholl_result(self, simple_model): + """Can add and retrieve Sholl results.""" + from ndev_morphology.sholl import ShollResult + + result = ShollResult( + center=np.array([10.0, 10.0]), + radii=np.array([1.0, 2.0, 3.0]), + counts=np.array([0, 1, 2]), + ) + simple_model.add_sholl_result('10.0,10.0', result) + + retrieved = simple_model.get_sholl_result('10.0,10.0') + assert retrieved is result + + def test_sholl_results_property(self, simple_model): + """sholl_results returns copy of cache.""" + from ndev_morphology.sholl import ShollResult + + result = ShollResult( + center=np.array([10.0, 10.0]), + radii=np.array([1.0]), + counts=np.array([1]), + ) + simple_model.add_sholl_result('key1', result) + + results = simple_model.sholl_results + assert 'key1' in results + # Modifying returned dict doesn't affect cache + results['key2'] = result + assert 'key2' not in simple_model.sholl_results + + +class TestDirectedTreeCaching: + """Tests for directed tree caching.""" + + def test_directed_trees_initially_empty(self, simple_model): + """No trees before computation.""" + assert simple_model.directed_trees == [] + + def test_add_directed_trees(self, simple_model): + """Can add directed trees.""" + # Mock tree (just using a dict for testing) + mock_tree = {'root': 0, 'branches': 3} + simple_model.add_directed_trees([mock_tree]) + assert len(simple_model.directed_trees) == 1 + + def test_clear_directed_trees(self, simple_model): + """Can clear cached trees.""" + mock_tree = {'root': 0} + simple_model.add_directed_trees([mock_tree]) + simple_model.clear_directed_trees() + assert simple_model.directed_trees == [] + + +class TestCacheClearing: + """Tests for cache management.""" + + def test_clear_cache(self, simple_model): + """clear_cache resets all cached data.""" + # Populate caches + _ = simple_model.summary + _ = simple_model.compute_branches() + simple_model.add_directed_trees([{}]) + + simple_model.clear_cache() + + # All caches should be reset + assert simple_model._summary is None + assert simple_model._branch_summary is None + assert len(simple_model._directed_trees) == 0 + assert len(simple_model._sholl_results) == 0 + + +class TestLayerIntegration: + """Tests for napari layer integration.""" + + def test_get_model_from_layer_without_cache(self): + """Returns None when no model cached.""" + + class MockLayer: + metadata = {} + + result = get_model_from_layer(MockLayer()) + assert result is None + + def test_attach_model_to_layer(self, simple_model): + """Can attach model to layer metadata.""" + + class MockLayer: + metadata = {} + + layer = MockLayer() + attach_model_to_layer(layer, simple_model) + assert METADATA_KEY in layer.metadata + assert layer.metadata[METADATA_KEY] is simple_model + + def test_get_model_after_attach(self, simple_model): + """Can retrieve attached model.""" + + class MockLayer: + metadata = {} + + layer = MockLayer() + attach_model_to_layer(layer, simple_model) + retrieved = get_model_from_layer(layer) + assert retrieved is simple_model + + +class TestFromLayerMethod: + """Tests for MorphologyModel.from_layer().""" + + @pytest.fixture + def mock_labels_layer(self, simple_skeleton): + """Create a mock napari Labels layer.""" + + class MockLabelsLayer: + def __init__(self, data, scale, name): + self.data = data + self.scale = scale + self.name = name + self.metadata = {} + + return MockLabelsLayer( + data=simple_skeleton, + scale=(1.0, 1.0), + name='test_labels', + ) + + def test_from_layer_creates_model(self, mock_labels_layer): + """from_layer creates new model for uncached layer.""" + model = MorphologyModel.from_layer(mock_labels_layer) + assert isinstance(model, MorphologyModel) + assert model.source_layer_name == 'test_labels' + + def test_from_layer_caches_on_layer(self, mock_labels_layer): + """from_layer caches model in layer.metadata.""" + model = MorphologyModel.from_layer(mock_labels_layer) + assert METADATA_KEY in mock_labels_layer.metadata + assert mock_labels_layer.metadata[METADATA_KEY] is model + + def test_from_layer_returns_cached(self, mock_labels_layer): + """from_layer returns cached model on subsequent calls.""" + model1 = MorphologyModel.from_layer(mock_labels_layer) + model2 = MorphologyModel.from_layer(mock_labels_layer) + assert model1 is model2 + + def test_from_layer_force_recompute(self, mock_labels_layer): + """force_recompute creates new model.""" + model1 = MorphologyModel.from_layer(mock_labels_layer) + model2 = MorphologyModel.from_layer( + mock_labels_layer, force_recompute=True + ) + assert model1 is not model2 + + def test_from_layer_extracts_spacing(self, simple_skeleton): + """from_layer extracts spacing from layer scale.""" + + class MockLabelsLayer: + def __init__(self): + self.data = simple_skeleton + self.scale = (0.5, 0.5) # Non-default spacing + self.name = 'test' + self.metadata = {} + + layer = MockLabelsLayer() + model = MorphologyModel.from_layer(layer) + assert model.spacing == (0.5, 0.5) diff --git a/tests/test_napari_workflows_compat.py b/tests/test_napari_workflows_compat.py new file mode 100644 index 0000000..69c360c --- /dev/null +++ b/tests/test_napari_workflows_compat.py @@ -0,0 +1,415 @@ +""" +Tests for napari-workflows compatibility. + +These tests verify that ndev-morphology functions work with napari-workflows' +Workflow class for building composable image processing pipelines. + +napari-workflows compatibility requirements: +1. Functions should accept numpy arrays (or types that convert to numpy) +2. Type annotations help workflows track data flow +3. Functions should be pure (no side effects on inputs) +4. Return types should be numpy arrays or LayerDataTuples +""" + +import numpy as np +import pytest + +# Skip all tests if napari-workflows is not installed +pytest.importorskip('napari_workflows') + + +class TestWorkflowBasics: + """Test that core functions work with napari_workflows.Workflow.""" + + @pytest.fixture + def simple_labels(self): + """Create a simple labels image with two objects.""" + labels = np.zeros((50, 50), dtype=np.uint16) + # Object 1: horizontal bar + labels[20:22, 10:40] = 1 + # Object 2: vertical bar + labels[10:40, 35:37] = 2 + return labels + + @pytest.fixture + def simple_skeleton(self, simple_labels): + """Create a skeleton from the simple labels.""" + from ndev_morphology import skeletonize_labels + + return skeletonize_labels(simple_labels) + + def test_workflow_with_skeletonize_labels(self, simple_labels): + """Test that skeletonize_labels works in a Workflow.""" + from napari_workflows import Workflow + + from ndev_morphology import skeletonize_labels + + w = Workflow() + w.set('input', simple_labels) + w.set('skeleton', skeletonize_labels, 'input') + + result = w.get('skeleton') + + assert result is not None + assert isinstance(result, np.ndarray) + assert result.shape == simple_labels.shape + # Skeleton should have fewer non-zero pixels than original + assert np.count_nonzero(result) < np.count_nonzero(simple_labels) + # Label values should be preserved + assert set(np.unique(result)) <= set(np.unique(simple_labels)) + + def test_workflow_with_filter_labels(self, simple_labels): + """Test that filter_labels_by_size works in a Workflow.""" + from napari_workflows import Workflow + + from ndev_morphology import filter_labels_by_size + + w = Workflow() + w.set('input', simple_labels) + # Filter to keep only labels with at least 50 pixels + w.set('filtered', filter_labels_by_size, 'input', min_size=50) + + result = w.get('filtered') + + assert result is not None + assert isinstance(result, np.ndarray) + assert result.shape == simple_labels.shape + + def test_workflow_chain(self, simple_labels): + """Test a multi-step workflow: filter -> skeletonize.""" + from napari_workflows import Workflow + + from ndev_morphology import filter_labels_by_size, skeletonize_labels + + w = Workflow() + w.set('input', simple_labels) + w.set('filtered', filter_labels_by_size, 'input', min_size=10) + w.set('skeleton', skeletonize_labels, 'filtered') + + # Get the final result - all dependencies should auto-execute + result = w.get('skeleton') + + assert result is not None + assert isinstance(result, np.ndarray) + + def test_workflow_with_separate_touching(self, simple_labels): + """Test the separate_touching_skeleton_labels function.""" + from napari_workflows import Workflow + + from ndev_morphology import ( + separate_touching_skeleton_labels, + skeletonize_labels, + ) + + w = Workflow() + w.set('input', simple_labels) + w.set('skeleton', skeletonize_labels, 'input') + w.set('separated', separate_touching_skeleton_labels, 'skeleton') + + result = w.get('separated') + + assert result is not None + assert isinstance(result, np.ndarray) + + +class TestWorkflowWithSkan: + """Test workflow compatibility with skan-based functions.""" + + @pytest.fixture + def cross_skeleton(self): + """Create a cross-shaped skeleton for testing.""" + skeleton = np.zeros((50, 50), dtype=np.uint8) + skeleton[25, 10:40] = 1 # Horizontal line + skeleton[10:40, 25] = 1 # Vertical line + return skeleton + + def test_summarize_branches_workflow(self, cross_skeleton): + """Test that summarize_branches works in a workflow context. + + Note: summarize_branches takes a skan.Skeleton, not a numpy array. + This is acceptable for programmatic use but may need a wrapper + for workflow compatibility. + """ + import skan + + from ndev_morphology import summarize_branches + + # Create skan Skeleton directly (this is the expected API) + skeleton_obj = skan.Skeleton(cross_skeleton > 0) + branches = summarize_branches(skeleton=skeleton_obj) + + assert branches is not None + assert len(branches) > 0 + assert 'branch_distance' in branches.columns + assert 'tortuosity' in branches.columns + + +class TestWorkflowRoots: + """Test that workflow can track inputs and outputs correctly.""" + + def test_workflow_structure(self): + """Test that workflow correctly identifies roots and leafs.""" + from napari_workflows import Workflow + + from ndev_morphology import filter_labels_by_size, skeletonize_labels + + labels = np.zeros((30, 30), dtype=np.uint16) + labels[10:20, 10:20] = 1 + + w = Workflow() + w.set('input', labels) + w.set('filtered', filter_labels_by_size, 'input', min_size=5) + w.set('skeleton', skeletonize_labels, 'filtered') + + # Check workflow structure + roots = w.roots() + leafs = w.leafs() + + assert 'input' in roots + assert 'skeleton' in leafs + assert len(w.followers_of('input')) == 1 + assert len(w.sources_of('skeleton')) == 1 + + +class TestWorkflowSaveLoad: + """Test that workflows with ndev-morphology can be saved and loaded.""" + + def test_save_load_workflow(self, tmp_path): + """Test saving and loading a workflow.""" + from napari_workflows import Workflow + from napari_workflows._io_yaml_v1 import load_workflow, save_workflow + + from ndev_morphology import skeletonize_labels + + # Create a workflow + w = Workflow() + w.set('skeleton', skeletonize_labels, 'input') + + # Save it + filepath = tmp_path / 'test_workflow.yaml' + save_workflow(str(filepath), w) + + # Load it back + w2 = load_workflow(str(filepath)) + + # Check that the task was preserved + assert 'skeleton' in w2._tasks + task = w2.get_task('skeleton') + assert task[0] == skeletonize_labels + + def test_loaded_workflow_executable(self, tmp_path): + """Test that a loaded workflow can be executed.""" + from napari_workflows import Workflow + from napari_workflows._io_yaml_v1 import load_workflow, save_workflow + + from ndev_morphology import skeletonize_labels + + # Create and save workflow + w = Workflow() + w.set('skeleton', skeletonize_labels, 'input') + + filepath = tmp_path / 'test_workflow.yaml' + save_workflow(str(filepath), w) + + # Load and execute + w2 = load_workflow(str(filepath)) + + # Set input and get result + labels = np.zeros((20, 20), dtype=np.uint16) + labels[5:15, 8:12] = 1 + w2.set('input', labels) + + result = w2.get('skeleton') + + assert result is not None + assert isinstance(result, np.ndarray) + + +class TestWorkflowWithLabelsModule: + """Test labels module functions with napari-workflows.""" + + @pytest.fixture + def multi_object_labels(self): + """Create labels with multiple objects of different sizes.""" + labels = np.zeros((100, 100), dtype=np.uint16) + # Small object (100 pixels) + labels[10:20, 10:20] = 1 + # Medium object (400 pixels) + labels[30:50, 30:50] = 2 + # Large object (900 pixels) + labels[60:90, 60:90] = 3 + return labels + + def test_filter_labels_by_size_workflow(self, multi_object_labels): + """Test filter_labels_by_size in a workflow.""" + from napari_workflows import Workflow + + from ndev_morphology import filter_labels_by_size + + w = Workflow() + w.set('input', multi_object_labels) + w.set( + 'filtered', + filter_labels_by_size, + 'input', + min_size=200, + max_size=500, + ) + + result = w.get('filtered') + + # Only the medium object (400 pixels) should remain + unique_labels = np.unique(result) + assert 2 in unique_labels # Medium object kept + assert 1 not in unique_labels # Small removed + assert 3 not in unique_labels # Large removed + + def test_exclude_labels_on_edges_workflow(self): + """Test exclude_labels_on_edges in a workflow.""" + from napari_workflows import Workflow + + from ndev_morphology import exclude_labels_on_edges + + labels = np.zeros((50, 50), dtype=np.uint16) + labels[0:10, 20:30] = 1 # Touches top edge + labels[20:30, 20:30] = 2 # Interior, should be kept + + w = Workflow() + w.set('input', labels) + w.set('interior_only', exclude_labels_on_edges, 'input') + + result = w.get('interior_only') + + assert 1 not in result # Edge-touching removed + assert 2 in result # Interior kept + + def test_connect_breaks_between_labels_workflow(self): + """Test connect_breaks_between_labels in a workflow.""" + from napari_workflows import Workflow + + from ndev_morphology import connect_breaks_between_labels + + labels = np.zeros((50, 50), dtype=np.uint16) + labels[20:25, 10:15] = 1 + labels[20:25, 17:22] = 1 # Small gap of 2 pixels + + w = Workflow() + w.set('input', labels) + w.set( + 'connected', + connect_breaks_between_labels, + 'input', + connect_distance=5, + ) + + result = w.get('connected') + + assert result is not None + assert isinstance(result, np.ndarray) + + +class TestWorkflowWithSkeletonModule: + """Test skeleton module functions with napari-workflows.""" + + @pytest.fixture + def simple_labels(self): + """Create simple bar-shaped labels.""" + labels = np.zeros((50, 50), dtype=np.uint16) + labels[22:28, 10:40] = 1 + return labels + + def test_exclude_region_from_skeleton_workflow(self, simple_labels): + """Test exclude_region_from_skeleton in a workflow.""" + from napari_workflows import Workflow + + from ndev_morphology import ( + exclude_region_from_skeleton, + skeletonize_labels, + ) + + # Create a mask for the center region + mask = np.zeros((50, 50), dtype=bool) + mask[20:30, 20:30] = True + + w = Workflow() + w.set('labels', simple_labels) + w.set('skeleton', skeletonize_labels, 'labels') + w.set('excluded', exclude_region_from_skeleton, 'skeleton', mask) + + result = w.get('excluded') + + assert result is not None + # The center should be excluded + assert np.all(result[20:30, 20:30] == 0) + + def test_fill_skeleton_gaps_workflow(self, simple_labels): + """Test fill_skeleton_gaps in a workflow.""" + from napari_workflows import Workflow + + from ndev_morphology import fill_skeleton_gaps, skeletonize_labels + + w = Workflow() + w.set('labels', simple_labels) + w.set('skeleton', skeletonize_labels, 'labels') + w.set('filled', fill_skeleton_gaps, 'skeleton', dilation_size=3) + + result = w.get('filled') + + assert result is not None + assert isinstance(result, np.ndarray) + + +class TestComplexWorkflowPipeline: + """Test complex multi-step workflows typical in morphology analysis.""" + + def test_full_preprocessing_pipeline(self): + """Test a realistic preprocessing pipeline.""" + from napari_workflows import Workflow + + from ndev_morphology import ( + exclude_labels_on_edges, + filter_labels_by_size, + separate_touching_skeleton_labels, + skeletonize_labels, + ) + + # Create test data with edge-touching and small objects + labels = np.zeros((100, 100), dtype=np.uint16) + labels[0:20, 40:60] = 1 # Edge-touching (should be removed) + labels[30:35, 30:35] = 2 # Small (should be removed by size filter) + labels[50:80, 50:80] = 3 # Good object (should be kept) + + w = Workflow() + w.set('raw_labels', labels) + w.set('no_edges', exclude_labels_on_edges, 'raw_labels') + w.set('size_filtered', filter_labels_by_size, 'no_edges', min_size=100) + w.set('skeleton', skeletonize_labels, 'size_filtered') + w.set('final', separate_touching_skeleton_labels, 'skeleton') + + result = w.get('final') + + assert result is not None + # Only label 3 should remain (as skeleton) + unique = np.unique(result) + assert 1 not in unique + assert 2 not in unique + + def test_workflow_code_generation(self): + """Test that workflow can generate Python code.""" + from napari_workflows import Workflow + + from ndev_morphology import filter_labels_by_size, skeletonize_labels + + w = Workflow() + w.set('filtered', filter_labels_by_size, 'input', min_size=50) + w.set('skeleton', skeletonize_labels, 'filtered') + + # Workflow should have valid structure + assert len(w._tasks) == 2 + assert 'filtered' in w._tasks + assert 'skeleton' in w._tasks + + # Check task dependencies + skeleton_task = w.get_task('skeleton') + assert skeleton_task[0] == skeletonize_labels + assert 'filtered' in skeleton_task # Input reference diff --git a/tests/test_pruning.py b/tests/test_pruning.py new file mode 100644 index 0000000..6e5dca1 --- /dev/null +++ b/tests/test_pruning.py @@ -0,0 +1,181 @@ +"""Tests for skeleton pruning operations.""" + +from __future__ import annotations + +import numpy as np +import pytest +import skan + +from ndev_morphology.pruning import ( + BranchType, + prune_short_branches, + prune_skeleton_to_image, + remove_isolated_cycles, +) + + +@pytest.fixture +def skeleton_with_branches(): + """Create a skeleton with various branch types.""" + # Create a skeleton with main branch and short terminal branches + # Main horizontal line with short vertical spurs + skeleton = np.zeros((50, 100), dtype=bool) + # Main horizontal line + skeleton[25, 10:90] = True + # Short spur (should be prunable) + skeleton[22:25, 30] = True # 3 pixels up + # Longer spur (should survive) + skeleton[10:25, 60] = True # 15 pixels up + # Another short spur + skeleton[25:28, 50] = True # 3 pixels down + return skeleton + + +@pytest.fixture +def skeleton_with_cycle(): + """Create a skeleton with an isolated cycle.""" + skeleton = np.zeros((50, 50), dtype=bool) + # Main line + skeleton[25, 10:40] = True + # Small loop + skeleton[10:15, 25] = True # vertical + skeleton[10, 23:28] = True # top + skeleton[14, 23:28] = True # bottom + skeleton[10:15, 23] = True # left + return skeleton + + +class TestPruneShortBranches: + """Tests for prune_short_branches function.""" + + def test_prune_removes_short_branches(self, skeleton_with_branches): + """Short terminal branches should be removed.""" + skel = skan.Skeleton(skeleton_with_branches) + n_before = skel.n_paths + + pruned, summary = prune_short_branches( + skel, + min_branch_distance=5.0, + prune_junction_to_endpoint=True, + ) + + n_after = pruned.n_paths + assert n_after < n_before, 'Should have removed some branches' + + def test_prune_preserves_long_branches(self, skeleton_with_branches): + """Long branches should not be removed.""" + skel = skan.Skeleton(skeleton_with_branches) + + # Prune with very small threshold + pruned, summary = prune_short_branches( + skel, + min_branch_distance=1.0, + prune_junction_to_endpoint=True, + ) + + # All branches > 1 pixel should survive + assert np.all(summary['branch_distance'] >= 1.0) + + def test_prune_respects_branch_type(self, skeleton_with_branches): + """Only specified branch types should be pruned.""" + skel = skan.Skeleton(skeleton_with_branches) + _summary_before = skan.summarize(skel, separator='_') + + # Get branch counts by type before pruning + # All prune options disabled except connecting (type 2) + pruned, summary = prune_short_branches( + skel, + min_branch_distance=100.0, # Very large threshold + prune_junction_to_endpoint=False, + prune_endpoint_to_endpoint=False, + prune_junction_to_junction=False, # Nothing to prune + prune_isolated_cycles=False, + ) + + # Nothing should be pruned since all branch types are protected + assert pruned.n_paths == skel.n_paths, 'No branches should be removed' + + def test_prune_returns_valid_skeleton(self, skeleton_with_branches): + """Pruned result should be a valid skan Skeleton.""" + skel = skan.Skeleton(skeleton_with_branches) + + pruned, summary = prune_short_branches(skel, min_branch_distance=5.0) + + # Should be able to iterate paths + assert pruned.n_paths >= 0 + for i in range(pruned.n_paths): + coords = pruned.path_coordinates(i) + assert len(coords) >= 2 + + +class TestRemoveIsolatedCycles: + """Tests for remove_isolated_cycles function.""" + + def test_removes_cycles(self, skeleton_with_cycle): + """Isolated cycles should be removed.""" + skel = skan.Skeleton(skeleton_with_cycle) + summary_before = skan.summarize(skel, separator='_') + + # Check if there are any cycles before + has_cycles_before = ( + summary_before['branch_type'] == BranchType.ISOLATED_CYCLE + ).any() + + pruned, summary = remove_isolated_cycles(skel) + + # Check no cycles after + has_cycles_after = ( + summary['branch_type'] == BranchType.ISOLATED_CYCLE + ).any() + + if has_cycles_before: + assert not has_cycles_after, 'Cycles should be removed' + + def test_preserves_non_cycles(self): + """Non-cycle branches should be preserved.""" + # Simple line - no cycles + skeleton = np.zeros((10, 50), dtype=bool) + skeleton[5, 5:45] = True + + skel = skan.Skeleton(skeleton) + n_before = skel.n_paths + + pruned, summary = remove_isolated_cycles(skel) + + n_after = pruned.n_paths + assert n_after == n_before, 'No branches should be removed' + + +class TestPruneSkeletonToImage: + """Tests for prune_skeleton_to_image function.""" + + def test_returns_binary_image(self, skeleton_with_branches): + """Output should be a binary image.""" + skel = skan.Skeleton(skeleton_with_branches) + summary = skan.summarize(skel, separator='_') + short = summary['branch_distance'] < 5.0 + + result = prune_skeleton_to_image(skel, np.flatnonzero(short)) + + assert result.dtype == bool + assert result.shape == skeleton_with_branches.shape + + def test_empty_prune_list(self, skeleton_with_branches): + """Empty prune list should return full skeleton.""" + skel = skan.Skeleton(skeleton_with_branches) + + result = prune_skeleton_to_image(skel, []) + + # Should match original + assert result.sum() == skeleton_with_branches.sum() + + +class TestBranchType: + """Tests for BranchType constants.""" + + def test_constants_match_skan(self): + """BranchType values should match skan's conventions.""" + assert BranchType.ENDPOINT_TO_ENDPOINT == 0 + assert BranchType.JUNCTION_TO_ENDPOINT == 1 + assert BranchType.JUNCTION_TO_JUNCTION == 2 + assert BranchType.ISOLATED_CYCLE == 3 diff --git a/tests/test_sholl.py b/tests/test_sholl.py new file mode 100644 index 0000000..0895143 --- /dev/null +++ b/tests/test_sholl.py @@ -0,0 +1,108 @@ +"""Tests for Sholl analysis functions.""" + +import numpy as np + +from ndev_morphology.sholl import ShollResult, compute_sholl_profile + + +class TestShollResult: + """Tests for ShollResult dataclass.""" + + def test_dataclass_fields(self): + """Test ShollResult has expected fields and computed properties.""" + # Only pass the core fields; computed properties are derived + result = ShollResult( + center=(16, 16), + radii=np.array([1, 2, 3, 4, 5]), + counts=np.array([0, 2, 4, 3, 1]), + ) + + assert result.center == (16, 16) + assert len(result.radii) == 5 + assert len(result.counts) == 5 + # Computed properties + assert result.max_crossings == 4 # max of counts + assert result.critical_radius == 3 # radius at max crossings + assert result.enclosing_radius == 5 # last radius with nonzero count + assert result.mean_crossings == 2.0 # mean of counts + assert result.total_crossings == 10 # sum of counts + + +class TestComputeShollProfile: + """Tests for compute_sholl_profile function.""" + + def test_basic_sholl_analysis(self, skeleton_with_center): + """Test basic Sholl analysis.""" + skeleton, center = skeleton_with_center + + result = compute_sholl_profile(skeleton, center) + + assert isinstance(result, ShollResult) + np.testing.assert_array_equal(result.center, center) + assert len(result.radii) > 0 + assert len(result.counts) == len(result.radii) + + def test_radii_are_increasing(self, skeleton_with_center): + """Test that radii are monotonically increasing.""" + skeleton, center = skeleton_with_center + + result = compute_sholl_profile(skeleton, center) + + if len(result.radii) > 1: + diffs = np.diff(result.radii) + assert np.all(diffs > 0) + + def test_counts_are_nonnegative(self, skeleton_with_center): + """Test that counts are non-negative integers.""" + skeleton, center = skeleton_with_center + + result = compute_sholl_profile(skeleton, center) + + assert np.all(result.counts >= 0) + + def test_max_crossings_matches_counts(self, skeleton_with_center): + """Test that max_crossings equals max of counts.""" + skeleton, center = skeleton_with_center + + result = compute_sholl_profile(skeleton, center) + + if len(result.counts) > 0: + assert result.max_crossings == np.max(result.counts) + + def test_with_custom_radii(self, skeleton_with_center): + """Test with custom radii specification.""" + skeleton, center = skeleton_with_center + + custom_radii = np.arange(1, 20, 2) # 1, 3, 5, ..., 19 + result = compute_sholl_profile(skeleton, center, radii=custom_radii) + + # Should use the provided radii + np.testing.assert_array_equal(result.radii, custom_radii) + + def test_empty_skeleton_raises(self): + """Test that empty skeleton raises error from skan.""" + import pytest + import skan + + skeleton_arr = np.zeros((32, 32), dtype=float) + + # skan.Skeleton with empty array raises sparse matrix error + with pytest.raises(ValueError): + skan.Skeleton(skeleton_arr) + + def test_total_crossings(self, skeleton_with_center): + """Test that total_crossings is sum of counts.""" + skeleton, center = skeleton_with_center + + result = compute_sholl_profile(skeleton, center) + + assert result.total_crossings == np.sum(result.counts) + + def test_3d_sholl(self, simple_skeleton_3d): + """Test Sholl analysis on 3D skeleton.""" + center = (8, 16, 16) + + result = compute_sholl_profile(simple_skeleton_3d, center) + + assert isinstance(result, ShollResult) + assert len(result.center) == 3 diff --git a/tests/test_skeleton.py b/tests/test_skeleton.py new file mode 100644 index 0000000..bf06884 --- /dev/null +++ b/tests/test_skeleton.py @@ -0,0 +1,276 @@ +"""Tests for skeleton and label operations.""" + +import numpy as np + +from ndev_morphology.labels import ( + connect_breaks_between_labels, + exclude_labels_on_edges, + filter_labels_by_size, +) +from ndev_morphology.skeleton import ( + exclude_region_from_skeleton, + separate_touching_skeleton_labels, + skeletonize_labels, +) + + +class TestSkeletonizeLabels: + """Tests for skeletonize_labels function.""" + + def test_basic_skeletonization(self, simple_labels_2d): + """Test that skeletonization produces skeleton with label identities.""" + skeleton = skeletonize_labels(simple_labels_2d) + + # Skeleton should have same dtype + assert skeleton.dtype == simple_labels_2d.dtype + + # Skeleton should only have values from original labels (0, 1, 2) + unique_vals = np.unique(skeleton) + assert all(v in [0, 1, 2] for v in unique_vals) + + # Skeleton should have fewer pixels than original labels + assert np.count_nonzero(skeleton) < np.count_nonzero(simple_labels_2d) + + # Non-zero skeleton pixels should preserve label values + mask1 = simple_labels_2d == 1 + skel_in_label1 = skeleton[mask1] + assert all(v in [0, 1] for v in np.unique(skel_in_label1)) + + def test_empty_labels(self): + """Test with empty label image.""" + labels = np.zeros((32, 32), dtype=np.uint16) + skeleton = skeletonize_labels(labels) + + assert skeleton.shape == labels.shape + assert np.count_nonzero(skeleton) == 0 + + def test_single_pixel_label(self): + """Test with single-pixel labels.""" + labels = np.zeros((32, 32), dtype=np.uint16) + labels[16, 16] = 1 + + skeleton = skeletonize_labels(labels) + # Single pixel skeletonizes to single pixel + assert np.count_nonzero(skeleton) <= 1 + + +class TestExcludeRegionFromSkeleton: + """Tests for exclude_region_from_skeleton function.""" + + def test_basic_exclusion(self, simple_skeleton_2d, circular_mask): + """Test that masked region is excluded from skeleton.""" + result = exclude_region_from_skeleton( + simple_skeleton_2d, circular_mask + ) + + # Result should have same shape + assert result.shape == simple_skeleton_2d.shape + + # No skeleton pixels should remain in mask region + assert np.count_nonzero(result[circular_mask]) == 0 + + # Skeleton pixels outside mask should be preserved + outside_mask = ~circular_mask + original_outside = simple_skeleton_2d[outside_mask] + result_outside = result[outside_mask] + np.testing.assert_array_equal(original_outside, result_outside) + + def test_exclusion_with_dilation(self, simple_skeleton_2d, circular_mask): + """Test that dilation expands exclusion region.""" + result_no_dilation = exclude_region_from_skeleton( + simple_skeleton_2d, circular_mask, dilation_iterations=0 + ) + result_with_dilation = exclude_region_from_skeleton( + simple_skeleton_2d, circular_mask, dilation_iterations=5 + ) + + # Dilated exclusion should remove more pixels + assert np.count_nonzero(result_with_dilation) <= np.count_nonzero( + result_no_dilation + ) + + def test_no_overlap(self, simple_skeleton_2d): + """Test when mask doesn't overlap skeleton.""" + mask = np.zeros_like(simple_skeleton_2d, dtype=bool) + mask[0:5, 0:5] = True # Corner, no skeleton there + + result = exclude_region_from_skeleton(simple_skeleton_2d, mask) + np.testing.assert_array_equal(result, simple_skeleton_2d) + + +class TestFilterLabelsBySize: + """Tests for filter_labels_by_size function.""" + + def test_min_size_filter(self): + """Test filtering by minimum size.""" + # Create labels with clearly different sizes + labels = np.zeros((64, 64), dtype=np.uint16) + labels[5:10, 5:10] = 1 # 25 pixels (small) + labels[30:50, 30:50] = 2 # 400 pixels (large) + + # Filter out the small label (min_size > 25) + result = filter_labels_by_size(labels, min_size=100) + + assert np.count_nonzero(result == 1) == 0 # Small removed + assert np.count_nonzero(result == 2) > 0 # Large kept + + def test_max_size_filter(self): + """Test filtering by maximum size.""" + # Create labels with clearly different sizes + labels = np.zeros((64, 64), dtype=np.uint16) + labels[5:10, 5:10] = 1 # 25 pixels (small) + labels[30:50, 30:50] = 2 # 400 pixels (large) + + # Filter out the large label (max_size < 400) + result = filter_labels_by_size(labels, max_size=100) + + assert np.count_nonzero(result == 1) > 0 # Small kept + assert np.count_nonzero(result == 2) == 0 # Large removed + + def test_no_filter(self, simple_labels_2d): + """Test with no size constraints.""" + result = filter_labels_by_size(simple_labels_2d) + np.testing.assert_array_equal(result, simple_labels_2d) + + +class TestExcludeLabelsOnEdges: + """Tests for exclude_labels_on_edges function.""" + + def test_edge_exclusion(self): + """Test that edge-touching labels are removed.""" + labels = np.zeros((32, 32), dtype=np.uint16) + labels[0:10, 5:15] = 1 # Touches top edge + labels[15:25, 15:25] = 2 # Interior, no edge touching + + result = exclude_labels_on_edges(labels) + + assert np.count_nonzero(result == 1) == 0 # Edge label removed + assert np.count_nonzero(result == 2) > 0 # Interior label preserved + + +class TestConnectBreaksBetweenLabels: + """Tests for connect_breaks_between_labels function.""" + + def test_connects_nearby_labels(self): + """Test that nearby labels are connected.""" + labels = np.zeros((32, 32), dtype=np.uint16) + labels[10:15, 10:15] = 1 + labels[10:15, 17:22] = 2 # Gap of 2 pixels + + # Connect with distance > gap + result = connect_breaks_between_labels(labels, connect_distance=4.0) + + # Both regions should now have same label + unique_nonzero = np.unique(result[result > 0]) + assert len(unique_nonzero) == 1 # Single connected region + + def test_doesnt_connect_distant_labels(self): + """Test that distant labels remain separate.""" + labels = np.zeros((64, 64), dtype=np.uint16) + labels[10:15, 10:15] = 1 + labels[40:45, 40:45] = 2 # Far apart + + result = connect_breaks_between_labels(labels, connect_distance=3.0) + + # Should still have two separate regions + unique_nonzero = np.unique(result[result > 0]) + assert len(unique_nonzero) >= 1 # At least one region preserved + + +class TestSeparateTouchingSkeletonLabels: + """Tests for separate_touching_skeleton_labels function.""" + + def test_separate_skeleton_basics(self): + """Test that function separates touching skeleton pixels.""" + # Create two adjacent skeleton lines with different labels + skeleton = np.zeros((20, 20), dtype=np.uint16) + # Horizontal line with label 1 + skeleton[10, 5:10] = 1 + # Horizontal line with label 2 touching at column 10 + skeleton[10, 10:15] = 2 + + result = separate_touching_skeleton_labels(skeleton) + + # The touching pixels should be removed + assert result[10, 9] == 0 or result[10, 10] == 0 + # Non-touching parts should be preserved + assert result[10, 5] == 1 + assert result[10, 14] == 2 + + def test_no_touching_skeletons(self): + """Test that non-touching skeletons are unchanged.""" + skeleton = np.zeros((20, 20), dtype=np.uint16) + # Two separate horizontal lines + skeleton[5, 5:10] = 1 + skeleton[15, 5:10] = 2 + + result = separate_touching_skeleton_labels(skeleton) + + # Should be identical since nothing touches + np.testing.assert_array_equal(result, skeleton) + + def test_empty_skeleton(self): + """Test with empty skeleton.""" + skeleton = np.zeros((20, 20), dtype=np.uint16) + result = separate_touching_skeleton_labels(skeleton) + + np.testing.assert_array_equal(result, skeleton) + + def test_single_label_skeleton(self): + """Test that single-label skeleton is unchanged.""" + skeleton = np.zeros((20, 20), dtype=np.uint16) + skeleton[10, 5:15] = 1 + + result = separate_touching_skeleton_labels(skeleton) + + np.testing.assert_array_equal(result, skeleton) + + def test_preserves_dtype(self): + """Test that output preserves input dtype.""" + skeleton = np.zeros((20, 20), dtype=np.uint32) + skeleton[10, 5:10] = 1 + skeleton[10, 10:15] = 2 + + result = separate_touching_skeleton_labels(skeleton) + + assert result.dtype == skeleton.dtype + + def test_diagonal_not_touching_by_default(self): + """Test that diagonally adjacent pixels ARE considered touching. + + Since skan uses 8-connectivity, diagonally adjacent skeleton pixels + from different labels would be merged. The function must handle this. + """ + skeleton = np.zeros((20, 20), dtype=np.uint16) + # Two lines that touch diagonally + skeleton[9, 5:10] = 1 # Ends at (9, 9) + skeleton[10, 10:15] = 2 # Starts at (10, 10) - diagonal from (9, 9) + + result = separate_touching_skeleton_labels(skeleton) + + # Diagonals ARE considered touching, so pixels should be removed + assert np.count_nonzero(result) < np.count_nonzero(skeleton) + + def test_diagonal_separation_works(self): + """Test that diagonally touching skeletons become separate in skan.""" + import skan + + skeleton = np.zeros((20, 20), dtype=np.uint16) + # Two lines that touch diagonally + skeleton[9, 5:10] = 1 + skeleton[10, 10:15] = 2 + + # Before separation: skan sees them as ONE skeleton + skel_before = skan.Skeleton(skeleton) + summary_before = skan.summarize(skel_before, separator='_') + assert summary_before['skeleton_id'].nunique() == 1, ( + 'Setup: should be merged' + ) + + # After separation: skan sees them as TWO skeletons + result = separate_touching_skeleton_labels(skeleton) + skel_after = skan.Skeleton(result) + summary_after = skan.summarize(skel_after, separator='_') + assert summary_after['skeleton_id'].nunique() == 2, ( + 'Should be separated' + ) diff --git a/tests/test_soma.py b/tests/test_soma.py new file mode 100644 index 0000000..c2b668a --- /dev/null +++ b/tests/test_soma.py @@ -0,0 +1,174 @@ +"""Tests for soma module.""" + +import numpy as np +import pytest +import skan + +from ndev_morphology import ( + detect_soma_centroid, + find_soma_node, + get_label_centroid, +) + + +@pytest.fixture +def multi_region_labels(): + """Create label image with multiple regions of different sizes.""" + labels = np.zeros((100, 100), dtype=np.uint16) + # Small region + labels[10:15, 10:15] = 1 # 25 pixels + # Large region (soma-like) + labels[40:60, 40:60] = 2 # 400 pixels + # Medium region + labels[70:80, 70:85] = 3 # 150 pixels + return labels + + +@pytest.fixture +def circular_and_elongated_labels(): + """Create label image with circular and elongated regions.""" + labels = np.zeros((100, 100), dtype=np.uint16) + + # Elongated region (high eccentricity) + labels[40:45, 10:50] = 1 # 5x40 = 200 pixels + + # Circular region (low eccentricity) + center = (50, 70) + radius = 10 + y, x = np.ogrid[:100, :100] + mask = (y - center[0]) ** 2 + (x - center[1]) ** 2 <= radius**2 + labels[mask] = 2 # ~314 pixels + + return labels + + +@pytest.fixture +def intensity_image_with_peak(): + """Create intensity image with a distinct peak inside labeled region.""" + img = np.zeros((100, 100), dtype=np.float32) + # Low background + img += 10.0 + # Bright peak at (50, 50) - inside the large region (40:60, 40:60) + img[48:52, 48:52] = 200.0 + return img + + +@pytest.fixture +def simple_skeleton_for_soma(): + """Create skeleton radiating from a center point.""" + skeleton = np.zeros((100, 100), dtype=bool) + center = (50, 50) + + # Create rays from center + for angle in np.linspace(0, 2 * np.pi, 6, endpoint=False): + for r in range(5, 40): + y = int(center[0] + r * np.sin(angle)) + x = int(center[1] + r * np.cos(angle)) + if 0 <= y < 100 and 0 <= x < 100: + skeleton[y, x] = True + + return skan.Skeleton(skeleton.astype(float), spacing=(1.0, 1.0)) + + +class TestDetectSomaCentroid: + """Tests for detect_soma_centroid function.""" + + def test_largest_region_method(self, multi_region_labels): + """Finds centroid of largest region.""" + result = detect_soma_centroid( + multi_region_labels, method='largest_region' + ) + + # Largest region is label 2 at (40:60, 40:60), centroid ~(50, 50) + assert result[0] == pytest.approx(49.5, abs=1.0) + assert result[1] == pytest.approx(49.5, abs=1.0) + + def test_roundest_region_method(self, circular_and_elongated_labels): + """Finds centroid of most circular region.""" + result = detect_soma_centroid( + circular_and_elongated_labels, method='roundest_region' + ) + + # Circular region is label 2 centered at (50, 70) + assert result[0] == pytest.approx(50, abs=2.0) + assert result[1] == pytest.approx(70, abs=2.0) + + def test_intensity_peak_method( + self, multi_region_labels, intensity_image_with_peak + ): + """Finds location of peak intensity.""" + result = detect_soma_centroid( + multi_region_labels, + method='intensity_peak', + intensity_image=intensity_image_with_peak, + ) + + # Peak is at approximately (50, 50) inside label 2 region + assert result[0] == pytest.approx(50, abs=2.0) + assert result[1] == pytest.approx(50, abs=2.0) + + def test_intensity_peak_requires_image(self, multi_region_labels): + """Raises error when intensity_peak method used without image.""" + with pytest.raises(ValueError, match='intensity_image is required'): + detect_soma_centroid(multi_region_labels, method='intensity_peak') + + def test_label_id_filter(self, multi_region_labels): + """Can filter to specific label.""" + result = detect_soma_centroid( + multi_region_labels, method='largest_region', label_id=1 + ) + + # Label 1 is at (10:15, 10:15), centroid at (12, 12) + assert result[0] == pytest.approx(12, abs=1.0) + assert result[1] == pytest.approx(12, abs=1.0) + + def test_empty_labels_raises(self): + """Raises error for empty label image.""" + labels = np.zeros((50, 50), dtype=np.uint16) + + with pytest.raises(ValueError, match='No regions found'): + detect_soma_centroid(labels) + + +class TestGetLabelCentroid: + """Tests for get_label_centroid function.""" + + def test_returns_correct_centroid(self, multi_region_labels): + """Returns centroid of specified label.""" + result = get_label_centroid(multi_region_labels, label_id=2) + + # Label 2 is at (40:60, 40:60), centroid at (49.5, 49.5) + assert result[0] == pytest.approx(49.5, abs=0.1) + assert result[1] == pytest.approx(49.5, abs=0.1) + + def test_missing_label_raises(self, multi_region_labels): + """Raises error for non-existent label.""" + with pytest.raises(ValueError, match='Label 99 not found'): + get_label_centroid(multi_region_labels, label_id=99) + + +class TestFindSomaNode: + """Tests for find_soma_node function.""" + + def test_finds_closest_node(self, simple_skeleton_for_soma): + """Finds skeleton node closest to given centroid.""" + soma_centroid = np.array([50.0, 50.0]) + + result = find_soma_node(simple_skeleton_for_soma, soma_centroid) + + assert isinstance(result, int) + assert result >= 0 + assert result < len(simple_skeleton_for_soma.coordinates) + + # The closest node should be near the center + node_coords = simple_skeleton_for_soma.coordinates[result] + distance = np.linalg.norm(node_coords - soma_centroid) + assert distance < 10.0 # Should be close to center + + def test_returns_int(self, simple_skeleton_for_soma): + """Returns an integer node index.""" + soma_centroid = np.array([50.0, 50.0]) + + result = find_soma_node(simple_skeleton_for_soma, soma_centroid) + + assert isinstance(result, int) diff --git a/tests/test_tree.py b/tests/test_tree.py new file mode 100644 index 0000000..2789463 --- /dev/null +++ b/tests/test_tree.py @@ -0,0 +1,575 @@ +"""Tests for the tree module - directional tree analysis.""" + +import networkx as nx +import numpy as np +import pytest +import skan + +from ndev_morphology import ( + BranchOrder, + DirectedTree, + compute_branch_order, + compute_strahler_order, + create_directed_tree, + find_longest_path, + get_paths_to_tips, + summarize_directed_tree, +) +from ndev_morphology.tree import create_directed_trees_from_soma + +# ============================================================================= +# Fixtures +# ============================================================================= + + +@pytest.fixture +def simple_y_skeleton(): + """ + Create a Y-shaped skeleton for testing. + + Structure (approximately): + / + * <- junction + \\ + | + * <- root (soma) + + The soma is at the bottom, one junction, two tips at top. + """ + skeleton = np.zeros((50, 50), dtype=np.uint8) + # Vertical stem from soma (40, 25) to junction (20, 25) + skeleton[20:41, 25] = 1 + # Left branch from junction (20, 25) to tip (5, 10) + for i in range(16): + skeleton[20 - i, 25 - i] = 1 + # Right branch from junction (20, 25) to tip (5, 40) + for i in range(16): + skeleton[20 - i, 25 + i] = 1 + return skeleton + + +@pytest.fixture +def simple_y_skeleton_obj(simple_y_skeleton): + """Create skan.Skeleton from Y-shaped image.""" + return skan.Skeleton(simple_y_skeleton.astype(float), spacing=(1.0, 1.0)) + + +@pytest.fixture +def soma_coords_for_y(): + """Soma coordinates at bottom of Y skeleton.""" + return np.array([40, 25]) + + +@pytest.fixture +def linear_skeleton(): + """Create a simple linear skeleton (no junctions).""" + skeleton = np.zeros((50, 50), dtype=np.uint8) + skeleton[10:40, 25] = 1 # Vertical line + return skeleton + + +@pytest.fixture +def complex_skeleton(): + """ + Create a more complex skeleton with multiple junction levels. + + * * + \\ / + * <- junction 2 + | + *---*---* <- junction 1 + 2 tips + | + * <- soma + """ + skeleton = np.zeros((60, 60), dtype=np.uint8) + # Main stem: (50, 30) to (30, 30) + skeleton[30:51, 30] = 1 + # Horizontal bar at junction 1: (30, 10) to (30, 50) + skeleton[30, 10:51] = 1 + # Second stem up: (30, 30) to (15, 30) + skeleton[15:31, 30] = 1 + # Y branches from (15, 30) + for i in range(10): + skeleton[15 - i, 30 - i] = 1 # Left tip + skeleton[15 - i, 30 + i] = 1 # Right tip + return skeleton + + +# ============================================================================= +# Tests for create_directed_tree +# ============================================================================= + + +class TestCreateDirectedTree: + """Tests for create_directed_tree function.""" + + def test_creates_directed_tree_from_y_skeleton( + self, simple_y_skeleton, soma_coords_for_y + ): + """Test creating directed tree from Y-shaped skeleton.""" + skel = skan.Skeleton( + simple_y_skeleton.astype(float), spacing=(1.0, 1.0) + ) + summary = skan.summarize(skel, separator='_') + + tree = create_directed_tree(skel, soma_coords_for_y, summary=summary) + + assert isinstance(tree, DirectedTree) + assert isinstance(tree.graph, nx.DiGraph) + assert tree.root_node is not None + + def test_tree_has_correct_structure( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that the tree has expected number of nodes/edges.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + # Should have 3 or more nodes (soma, junction, tips) + assert tree.graph.number_of_nodes() >= 3 + + def test_tree_is_rooted_at_soma( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that tree is correctly rooted at soma position.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + # Root should have no incoming edges + assert tree.graph.in_degree(tree.root_node) == 0 + + def test_linear_skeleton_creates_simple_tree(self, linear_skeleton): + """Test that linear skeleton creates simple 2-node tree.""" + skel = skan.Skeleton(linear_skeleton.astype(float), spacing=(1.0, 1.0)) + summary = skan.summarize(skel, separator='_') + soma_coords = np.array([39, 25]) # Bottom of line + + tree = create_directed_tree(skel, soma_coords, summary=summary) + + assert tree.n_branches >= 1 + assert tree.n_junctions == 0 # No junctions in linear skeleton + + +# ============================================================================= +# Tests for DirectedTree properties +# ============================================================================= + + +class TestDirectedTreeProperties: + """Tests for DirectedTree class properties.""" + + def test_n_tips(self, simple_y_skeleton_obj, soma_coords_for_y): + """Test counting tip nodes.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + # Y-skeleton should have 2 tips + assert tree.n_tips == 2 + + def test_n_junctions(self, simple_y_skeleton_obj, soma_coords_for_y): + """Test counting junction nodes.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + # Y-skeleton should have 1 junction + assert tree.n_junctions == 1 + + def test_primary_branches(self, simple_y_skeleton_obj, soma_coords_for_y): + """Test identifying primary branches.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + primary = tree.primary_branches + assert isinstance(primary, list) + # Y-skeleton: primary is the stem from soma to junction + assert len(primary) >= 1 + + +# ============================================================================= +# Tests for compute_branch_order +# ============================================================================= + + +class TestComputeBranchOrder: + """Tests for compute_branch_order function.""" + + def test_branch_order_for_y_skeleton( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test branch order assignment for Y-shaped skeleton.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + orders = compute_branch_order(tree) + + assert isinstance(orders, dict) + # All edges should have an order assigned + assert len(orders) == tree.graph.number_of_edges() + + # Orders should be positive integers + for order in orders.values(): + assert isinstance(order, int) + assert order >= BranchOrder.PRIMARY + + def test_primary_secondary_order_distinction( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that primary and secondary branches are distinguished.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + orders = compute_branch_order(tree) + + # Should have at least primary (1) and secondary (2) orders in Y + order_values = set(orders.values()) + assert BranchOrder.PRIMARY in order_values + + +# ============================================================================= +# Tests for compute_strahler_order +# ============================================================================= + + +class TestComputeStrahlerOrder: + """Tests for compute_strahler_order function.""" + + def test_strahler_order_for_y_skeleton( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test Strahler order assignment for Y-shaped skeleton.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + orders = compute_strahler_order(tree) + + assert isinstance(orders, dict) + assert len(orders) == tree.graph.number_of_edges() + + def test_strahler_order_contains_ones( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that Strahler orders include order 1 for terminal branches.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + orders = compute_strahler_order(tree) + + # Strahler order 1 should exist for terminal branches + # or order 2 if the Y merges them (both are valid Strahler outcomes) + order_values = set(orders.values()) + # At minimum we should have some order >= 1 + assert all(v >= 1 for v in order_values) + + +# ============================================================================= +# Tests for find_longest_path +# ============================================================================= + + +class TestFindLongestPath: + """Tests for find_longest_path function.""" + + def test_finds_path_in_y_skeleton( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test finding longest path in Y-shaped skeleton.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + path, length = find_longest_path(tree) + + assert isinstance(path, list) + assert len(path) >= 2 # At least soma and tip + assert isinstance(length, float) + assert length > 0 + + def test_path_starts_at_root( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that longest path starts at root node.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + path, _ = find_longest_path(tree) + + assert path[0] == tree.root_node + + def test_linear_skeleton_path_length(self, linear_skeleton): + """Test path length for simple linear skeleton.""" + skel = skan.Skeleton(linear_skeleton.astype(float), spacing=(1.0, 1.0)) + summary = skan.summarize(skel, separator='_') + soma_coords = np.array([39, 25]) + + tree = create_directed_tree(skel, soma_coords, summary=summary) + path, length = find_longest_path(tree) + + # Linear skeleton is 30 pixels long (rows 10-39 inclusive) + assert length == pytest.approx(29.0, abs=5.0) + + +# ============================================================================= +# Tests for get_paths_to_tips +# ============================================================================= + + +class TestGetPathsToTips: + """Tests for get_paths_to_tips function.""" + + def test_returns_list_of_dicts( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that paths are returned as a list of dicts.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + paths = get_paths_to_tips(tree) + + assert isinstance(paths, list) + # Y-skeleton has 2 tips + assert len(paths) == 2 + # Each path should be a dict with expected keys + for path_info in paths: + assert isinstance(path_info, dict) + assert 'path' in path_info + assert 'length' in path_info + assert 'tip_node' in path_info + + def test_all_paths_start_at_root( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that all paths start at root node.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + paths = get_paths_to_tips(tree) + + for path_info in paths: + path = path_info['path'] + assert path[0] == tree.root_node + assert path[-1] == path_info['tip_node'] + + +# ============================================================================= +# Tests for summarize_directed_tree +# ============================================================================= + + +class TestSummarizeDirectedTree: + """Tests for summarize_directed_tree function.""" + + def test_returns_dataframe(self, simple_y_skeleton_obj, soma_coords_for_y): + """Test that function returns a pandas DataFrame.""" + import pandas as pd + + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + df = summarize_directed_tree(tree) + + assert isinstance(df, pd.DataFrame) + + def test_has_branch_order_column( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that summary includes branch_order column.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + df = summarize_directed_tree(tree) + + assert 'branch_order' in df.columns + + def test_has_strahler_order_column( + self, simple_y_skeleton_obj, soma_coords_for_y + ): + """Test that summary includes strahler_order column.""" + summary = skan.summarize(simple_y_skeleton_obj, separator='_') + tree = create_directed_tree( + simple_y_skeleton_obj, soma_coords_for_y, summary=summary + ) + + df = summarize_directed_tree(tree) + + assert 'strahler_order' in df.columns + + +# ============================================================================= +# Tests for BranchOrder constants +# ============================================================================= + + +class TestBranchOrder: + """Tests for BranchOrder constants.""" + + def test_order_values(self): + """Test that branch order constants have expected values.""" + assert BranchOrder.PRIMARY == 1 + assert BranchOrder.SECONDARY == 2 + assert BranchOrder.TERTIARY == 3 + assert BranchOrder.QUATERNARY == 4 + + +# ============================================================================= +# Tests for create_directed_trees_from_soma +# ============================================================================= + + +class TestCreateDirectedTreesFromSoma: + """Tests for create_directed_trees_from_soma function.""" + + def test_creates_multiple_trees_from_star_skeleton(self): + """Test that a star-shaped skeleton with central soma creates multiple trees.""" + # Create a star skeleton with a central soma + skeleton = np.zeros((60, 60), dtype=np.uint8) + # Central soma region (will be excluded) + soma_mask = np.zeros((60, 60), dtype=bool) + soma_mask[25:35, 25:35] = True + + # Four radiating branches from the soma edges + # Top branch + skeleton[5:26, 30] = 1 + # Bottom branch + skeleton[34:55, 30] = 1 + # Left branch + skeleton[30, 5:26] = 1 + # Right branch + skeleton[30, 34:55] = 1 + + soma_centroid = np.array([30.0, 30.0]) + + trees = create_directed_trees_from_soma( + skeleton_image=skeleton, + soma_mask=soma_mask, + soma_centroid=soma_centroid, + spacing=(1.0, 1.0), + dilation_iterations=0, + ) + + # Should have 4 separate trees (one per radiating branch) + assert len(trees) == 4 + + def test_each_tree_is_directed_tree(self): + """Test that each returned object is a DirectedTree.""" + skeleton = np.zeros((50, 50), dtype=np.uint8) + soma_mask = np.zeros((50, 50), dtype=bool) + soma_mask[20:30, 20:30] = True + + # Two branches + skeleton[5:21, 25] = 1 + skeleton[29:45, 25] = 1 + + soma_centroid = np.array([25.0, 25.0]) + + trees = create_directed_trees_from_soma( + skeleton_image=skeleton, + soma_mask=soma_mask, + soma_centroid=soma_centroid, + ) + + assert len(trees) == 2 + for tree in trees: + assert isinstance(tree, DirectedTree) + + def test_root_is_closest_to_soma(self): + """Test that tree roots are the nodes closest to soma centroid.""" + skeleton = np.zeros((50, 50), dtype=np.uint8) + soma_mask = np.zeros((50, 50), dtype=bool) + soma_mask[22:28, 22:28] = True + + # One branch going up from soma + skeleton[5:23, 25] = 1 + + soma_centroid = np.array([25.0, 25.0]) + + trees = create_directed_trees_from_soma( + skeleton_image=skeleton, + soma_mask=soma_mask, + soma_centroid=soma_centroid, + dilation_iterations=0, + ) + + assert len(trees) == 1 + tree = trees[0] + + # Root should be at the end closest to soma (around y=22) + root_coords = tree.skeleton.coordinates[tree.root_node] + # The root y-coordinate should be closer to soma (25) than the tip (5) + assert root_coords[0] > 10 # Closer to soma end, not tip end + + def test_empty_skeleton_returns_empty_list(self): + """Test that empty skeleton returns empty list.""" + skeleton = np.zeros((50, 50), dtype=np.uint8) + soma_mask = np.zeros((50, 50), dtype=bool) + soma_mask[20:30, 20:30] = True + soma_centroid = np.array([25.0, 25.0]) + + trees = create_directed_trees_from_soma( + skeleton_image=skeleton, + soma_mask=soma_mask, + soma_centroid=soma_centroid, + ) + + assert trees == [] + + def test_dilation_expands_exclusion_zone(self): + """Test that dilation_iterations expands the excluded region.""" + skeleton = np.zeros((50, 50), dtype=np.uint8) + soma_mask = np.zeros((50, 50), dtype=bool) + soma_mask[24:26, 24:26] = True # Small soma + + # Branch 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