Refactor

.github/workflows/docs.yml

@@ -0,0 +1,25 @@
+name: Docs
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install package and docs dependencies
+        run: |
+          pip install -e .
+          pip install mkdocs mkdocs-material "mkdocstrings[python]" mkdocs-gen-files mkdocs-literate-nav
+
+      - name: Build docs
+        run: mkdocs build

.readthedocs.yml

@@ -0,0 +1,17 @@
+version: 2
+
+build:
+  os: ubuntu-24.04
+  tools:
+    python: "3.12"
+  jobs:
+    post_install:
+      - pip install "mkdocs>=1.6" "mkdocs-material>=9.5" "mkdocstrings[python]>=0.25"
+
+mkdocs:
+  configuration: mkdocs.yml
+
+python:
+  install:
+    - method: pip
+      path: .

CLAUDE.md

@@ -0,0 +1,95 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+TPTBox is a Python package for processing BIDS-compliant medical imaging datasets (CT, MRI), with a focus on torso/spine analysis. It provides NIfTI I/O, reorientation/resampling, Points of Interest (POI) computation for vertebrae, 2D/3D snapshots, registration, and segmentation integrations (SPINEPS, nnU-Net).
+
+## Commands
+
+### Installation
+```bash
+pip install poetry
+poetry install --with dev
+```
+
+### Running Tests
+```bash
+# All tests
+pytest unit_tests/
+
+# Single test file
+pytest unit_tests/test_nii.py
+
+# Single test function
+pytest unit_tests/test_nii.py::test_function_name
+
+# With coverage
+coverage run --source=TPTBox -m pytest unit_tests/
+```
+
+### Linting & Formatting
+```bash
+# Lint (auto-fix where possible)
+ruff check . --fix
+
+# Format
+ruff format .
+
+# Both (mirrors pre-commit behavior)
+pre-commit run --all-files
+```
+
+### CI Linting (as run in GitHub Actions)
+```bash
+flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+```
+
+## Architecture
+
+### Core Abstractions
+
+**`NII`** (`TPTBox/core/nii_wrapper.py`) — Central class wrapping nibabel NIfTI images. Handles reorientation, resampling, affine transforms, boolean masking, and mathematical operations. Nearly all image operations in the package go through `NII`. Math operations live in `nii_wrapper_math.py` as mixins.
+
+**`POI`** (`TPTBox/core/poi.py`) — Points of Interest container mapping `(vertebra_id, subregion_id) → 3D coordinate`. Coordinates can be in voxel or world space. POI computation strategies live in `TPTBox/core/poi_fun/`.
+
+**`BIDS_FILE` / `BIDS_Global_info`** (`TPTBox/core/bids_files.py`) — BIDS dataset navigator. `BIDS_Global_info` scans a dataset root, while `BIDS_FILE` represents a single file parsed according to BIDS naming entities. Constants for BIDS key/value vocabulary are in `bids_constants.py`.
+
+**`Location` / `Vertebra_Instance`** (`TPTBox/core/vert_constants.py`) — Enum-like constants for vertebral anatomy (vertebra IDs, subregion labels). Used as keys into `POI` objects throughout the codebase.
+
+### Package Layout
+
+```
+TPTBox/
+├── core/           # NII, POI, BIDS parsing, numpy utilities, vertebra constants
+│   └── poi_fun/    # POI calculation strategies and serialization
+├── spine/          # Spine-specific: 2D snapshots, statistics (distances, angles)
+├── segmentation/   # SPINEPS integration, nnU-Net inference, defacing
+├── registration/   # ANTs rigid/deformable, DeepALI deep learning registration
+├── mesh3D/         # 3D mesh generation and rendering from segmentations
+├── stitching/      # Multi-station image stitching
+└── logger/         # Logging infrastructure (Logger, Print_Logger, No_Logger)
+```
+
+Public API is re-exported from `TPTBox/__init__.py`. All major classes and utility functions are importable directly from `TPTBox`.
+
+### Key Relationships
+
+- `NII` + `POI` are used together constantly: compute POIs from segmentation `NII`s, then use POIs to guide further processing.
+- `BIDS_Global_info` iterates subjects/sessions; each subject has a `Subject_Container` with `BIDS_FILE` entries; `BIDS_FILE.get_nii()` returns a `NII`.
+- `vert_constants.py` defines the shared label space (`Location` enum) that ties segmentation labels to POI keys to snapshot rendering.
+- External tool integrations (SPINEPS, nnU-Net, ANTs, DeepALI) are optional; their imports are guarded so the core works without them.
+
+### Test Layout
+
+Tests live in `unit_tests/` (not `TPTBox/tests/`). `TPTBox/tests/` contains test utilities and sample data (CT/MRI NIfTIs) used by the unit tests. Some generated test files are very large (>20K LOC) — they are autogenerated and should not be edited by hand.
+
+## Code Style
+
+- **Line length**: 140 characters
+- **Formatter**: Ruff (Black-compatible, double quotes)
+- **Target Python**: 3.10+ syntax, but the package supports 3.9–3.14
+- **Naming**: Ruff N-rules are largely ignored — mixed-case class/method names are acceptable in this codebase (medical domain conventions)
+- **Complexity**: McCabe max=20; research code legitimately has deep branching
+- `from __future__ import annotations` is used widely for forward references

TPTBox/core/README.md

@@ -0,0 +1,97 @@
+# TPTBox Core
+
+The `core` subpackage is the foundation of TPTBox. It provides the three primary abstractions —
+`NII`, `POI`, and `BIDS_FILE` — along with helper utilities for array operations and anatomical constants.
+
+## Key Classes and Functions
+
+### `nii_wrapper.py` — NIfTI image wrapper
+
+| Symbol | Description |
+|---|---|
+| `NII` | Wraps `nibabel.Nifti1Image`; the central image type throughout TPTBox |
+| `NII.load(path, seg)` | Load a NIfTI file from disk (classmethod) |
+| `NII.from_numpy(arr, affine, seg)` | Construct from a numpy array and affine matrix |
+| `NII.reorient(axcodes_to)` | Reorient to a canonical axis code (e.g. `("R","A","S")`) |
+| `NII.rescale(voxel_spacing)` | Resample to new voxel spacing in mm |
+| `NII.resample_from_to(other)` | Resample to match the grid of another `NII` |
+| `NII.apply_mask(mask)` | Zero-out voxels outside a binary/label mask |
+| `NII.map_labels(label_map)` | Remap integer labels |
+| `NII.save(path)` | Save to disk as `.nii` or `.nii.gz` |
+| `NII.get_array()` | Return a copy of the underlying numpy array |
+| `NII.get_seg_array()` | Same as `get_array()` but asserts `seg=True` |
+| `Image_Reference` | Type alias: `BIDS_FILE | Nifti1Image | Path | str | NII` |
+
+### `bids_files.py` — BIDS dataset navigation
+
+| Symbol | Description |
+|---|---|
+| `BIDS_Global_info` | Scans a dataset root and indexes all BIDS files |
+| `BIDS_Global_info.enumerate_subjects()` | Iterate over subjects as `(subject_id, Subject_Container)` |
+| `Subject_Container` | Per-subject file index; entry point for queries |
+| `Subject_Container.new_query()` | Returns a `Searchquery` for this subject |
+| `BIDS_FILE` | One file parsed into BIDS entities (sub, ses, format, …) |
+| `BIDS_FILE.open_nii()` | Load this file's NIfTI |
+| `BIDS_FILE.get_changed_path(...)` | Derive a new path with changed BIDS entities |
+| `Searchquery` | Fluent query builder: `.filter()`, `.loop_dict()`, `.first()` |
+| `BIDS_Family` | `dict[str, list[BIDS_FILE]]` grouping files by format |
+
+### `poi.py` — Points of Interest
+
+| Symbol | Description |
+|---|---|
+| `POI` | Maps `(vertebra_id, subregion_id) → (x, y, z)` |
+| `calc_centroids(seg_nii)` | Compute centroids for every label in a segmentation |
+| `calc_poi_from_subreg_vert(vert, subreg)` | Compute POIs from paired vertebra + subregion segmentations |
+| `POI.save(path)` | Serialise to JSON |
+| `POI.load(path)` | Deserialise from JSON |
+| `POI.to_global(ref)` | Convert from voxel to world (mm) coordinates |
+| `POI.to_local(ref)` | Convert from world to voxel coordinates |
+
+### `np_utils.py` — NumPy utilities
+
+| Symbol | Description |
+|---|---|
+| `np_extract_label(arr, label)` | Extract a single label as a binary mask |
+| `np_center_of_mass(arr)` | Per-label centre-of-mass |
+| `np_volume(arr)` | Per-label voxel count |
+| `np_bbox_binary(mask)` | Bounding-box slice tuple for a binary array |
+| `np_dilate_msk(arr, mm, zoom)` | Morphological dilation by `mm` millimetres |
+| `np_erode_msk(arr, mm, zoom)` | Morphological erosion |
+| `np_fill_holes(arr)` | Fill holes per label |
+| `np_connected_components(arr)` | Label connected components |
+| `np_map_labels(arr, label_map)` | Remap label integers via a dict |
+| `np_unique(arr)` | Unique values (faster than `np.unique` for uint arrays) |
+
+### `vert_constants.py` — Anatomical constants
+
+| Symbol | Description |
+|---|---|
+| `Location` | `IntEnum` of anatomical subregion IDs (used as POI keys) |
+| `Vertebra_Instance` | Maps integer IDs → anatomical names (C1–S1) |
+| `v_name2idx` | Dict: `"L1" → 20`, etc. |
+| `v_idx2name` | Dict: `20 → "L1"`, etc. |
+| `v_idx_order` | Canonical sort order for vertebra IDs |
+| `ZOOMS` | Type alias: `tuple[float, float, float]` |
+| `AX_CODES` | Type alias: `tuple[str, str, str]` |
+| `AFFINE` | Type alias: `np.ndarray` (4×4) |
+
+## Quick Example
+
+```python
+from TPTBox import NII, BIDS_Global_info, calc_centroids
+
+# Load and resample a CT
+ct = NII.load("sub-001_ct.nii.gz", seg=False)
+ct_ras = ct.reorient(("R", "A", "S")).rescale((1.0, 1.0, 1.0))
+
+# Compute centroids from a segmentation
+seg = NII.load("sub-001_seg.nii.gz", seg=True)
+poi = calc_centroids(seg)
+print(poi)
+
+# Scan a BIDS dataset
+bids = BIDS_Global_info(["dataset/"], parents=["rawdata"])
+for subj, container in bids.enumerate_subjects():
+    t2 = container.new_query().filter("format", "T2w").first()
+```

TPTBox/core/bids_constants.py

@@ -311,6 +311,7 @@
     "Hemisphere": "hemi",  # [L,R]
     "Sample": "sample",  # such as tissue, primary cell or cell-free sample.
     # Sub recordings - Use when necessary.
+    "Split": "split",  # Never use, legacy. Is applied inconsistently by other datasets.
     ## Contrast
     "Contrast enhancement phase": "ce",
     "Tracer": "trc",  # use ce before this one.
@@ -340,7 +341,6 @@
     # Single class segmentation
     "Label": "label",
     # Others (never used)
-    "Split": "split",  # Never use, legacy. Is applied inconsistently by other datasets.
     "Density": "den",
     "version": "version",
     "Description": "desc",