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"""Shared test fixtures."""
from __future__ import annotations
import tempfile
from pathlib import Path
from typing import TYPE_CHECKING
import nibabel as nib
import numpy as np
import pytest
from stroke_deepisles_demo.core.types import CaseFiles
if TYPE_CHECKING:
from collections.abc import Generator
@pytest.fixture
def temp_dir() -> Generator[Path, None, None]:
"""Create a temporary directory for test outputs."""
with tempfile.TemporaryDirectory() as td:
yield Path(td)
@pytest.fixture
def synthetic_nifti_3d(temp_dir: Path) -> Path:
"""Create a minimal synthetic 3D NIfTI file."""
data = np.random.rand(10, 10, 10).astype(np.float32)
img = nib.Nifti1Image(data, affine=np.eye(4)) # type: ignore
path = temp_dir / "synthetic.nii.gz"
nib.save(img, path) # type: ignore
return path
@pytest.fixture
def synthetic_case_files(temp_dir: Path) -> CaseFiles:
"""Create a complete set of synthetic case files."""
# Create DWI
dwi_data = np.random.rand(64, 64, 30).astype(np.float32)
dwi_img = nib.Nifti1Image(dwi_data, affine=np.eye(4)) # type: ignore
dwi_path = temp_dir / "dwi.nii.gz"
nib.save(dwi_img, dwi_path) # type: ignore
# Create ADC
adc_data = np.random.rand(64, 64, 30).astype(np.float32) * 2000
adc_img = nib.Nifti1Image(adc_data, affine=np.eye(4)) # type: ignore
adc_path = temp_dir / "adc.nii.gz"
nib.save(adc_img, adc_path) # type: ignore
# Create mask
mask_data = (np.random.rand(64, 64, 30) > 0.9).astype(np.uint8)
mask_img = nib.Nifti1Image(mask_data, affine=np.eye(4)) # type: ignore
mask_path = temp_dir / "mask.nii.gz"
nib.save(mask_img, mask_path) # type: ignore
return CaseFiles(
dwi=dwi_path,
adc=adc_path,
ground_truth=mask_path,
)
@pytest.fixture
def synthetic_probability_mask(temp_dir: Path) -> Path:
"""
Create a synthetic probability mask (float values 0.0-1.0).
This simulates model output that may contain probability values
rather than binary 0/1 masks. Used to test visualization handling
of probability-valued segmentation masks.
The mask has values ONLY at slice 5 to ensure get_slice_at_max_lesion selects it:
- Outer region with low probability (0.3) - below 0.5 threshold
- Inner region with high probability (0.8) - above 0.5 threshold
See: docs/specs/23-slice-comparison-overlay-bug.md
"""
mask_data = np.zeros((10, 10, 10), dtype=np.float32)
# Only populate slice 5 to ensure it's selected as max lesion slice
# Outer region: low confidence (below 0.5 threshold)
mask_data[2:8, 2:8, 5] = 0.3
# Inner region: high confidence (above 0.5 threshold) - this should be visible
mask_data[3:7, 3:7, 5] = 0.8
img = nib.Nifti1Image(mask_data, affine=np.eye(4)) # type: ignore
path = temp_dir / "probability_mask.nii.gz"
nib.save(img, path) # type: ignore
return path
@pytest.fixture
def synthetic_binary_mask(temp_dir: Path) -> Path:
"""Create a synthetic binary mask (0 or 1 values only)."""
mask_data = np.zeros((10, 10, 10), dtype=np.uint8)
mask_data[3:7, 3:7, 4:6] = 1 # Binary lesion region
img = nib.Nifti1Image(mask_data, affine=np.eye(4)) # type: ignore
path = temp_dir / "binary_mask.nii.gz"
nib.save(img, path) # type: ignore
return path
@pytest.fixture
def synthetic_isles_dir(temp_dir: Path) -> Path:
"""
Create synthetic ISLES24-like directory structure.
Structure:
temp_dir/
├── Images-DWI/
│ ├── sub-stroke0001_ses-02_dwi.nii.gz
│ └── sub-stroke0002_ses-02_dwi.nii.gz
├── Images-ADC/
│ ├── sub-stroke0001_ses-02_adc.nii.gz
│ └── sub-stroke0002_ses-02_adc.nii.gz
└── Masks/
├── sub-stroke0001_ses-02_lesion-msk.nii.gz
└── sub-stroke0002_ses-02_lesion-msk.nii.gz
"""
dwi_dir = temp_dir / "Images-DWI"
adc_dir = temp_dir / "Images-ADC"
mask_dir = temp_dir / "Masks"
dwi_dir.mkdir()
adc_dir.mkdir()
mask_dir.mkdir()
for subject_num in [1, 2]:
subject_id = f"sub-stroke{subject_num:04d}"
# Create DWI
dwi_data = np.random.rand(10, 10, 5).astype(np.float32)
dwi_img = nib.Nifti1Image(dwi_data, affine=np.eye(4)) # type: ignore
nib.save(dwi_img, dwi_dir / f"{subject_id}_ses-02_dwi.nii.gz") # type: ignore
# Create ADC
adc_data = np.random.rand(10, 10, 5).astype(np.float32) * 2000
adc_img = nib.Nifti1Image(adc_data, affine=np.eye(4)) # type: ignore
nib.save(adc_img, adc_dir / f"{subject_id}_ses-02_adc.nii.gz") # type: ignore
# Create Mask
mask_data = (np.random.rand(10, 10, 5) > 0.9).astype(np.uint8)
mask_img = nib.Nifti1Image(mask_data, affine=np.eye(4)) # type: ignore
nib.save(mask_img, mask_dir / f"{subject_id}_ses-02_lesion-msk.nii.gz") # type: ignore
return temp_dir
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