Method to detect specimens in H&E images

pyproject.toml

@@ -37,16 +37,20 @@ authors = [
     {name = "Giovanni Palla"},
     {name = "Michal Klein"},
     {name = "Hannah Spitzer"},
+    {name = "Tim Treis"},
+    {name = "Laurens Lehner"},
+    {name = "Selman Ozleyen"},
 ]
 maintainers = [
-    {name = "Giovanni Palla", email = "[email protected]"},
-    {name = "Michal Klein", email = "[email protected]"},
-    {name = "Tim Treis", email = "[email protected]"}
+    {name = "Tim Treis", email = "[email protected]"},
+    {name = "Selman Ozleyen", email = "[email protected]"}
 ]
 
 dependencies = [
    "aiohttp>=3.8.1",
    "anndata>=0.9",
+   "spatialdata>=0.2.5",
+   "spatialdata-plot",
    "cycler>=0.11.0",
    "dask-image>=0.5.0",
    "dask[array]>=2021.02.0,<=2024.11.2",
@@ -61,7 +65,7 @@ dependencies = [
    "pandas>=2.1.0",
    "Pillow>=8.0.0",
    "scanpy>=1.9.3",
-   "scikit-image>=0.20",
+   "scikit-image>=0.25",
    # due to https://github.com/scikit-image/scikit-image/issues/6850 breaks rescale ufunc
    "scikit-learn>=0.24.0",
    "statsmodels>=0.12.0",
@@ -70,21 +74,27 @@ dependencies = [
    "tqdm>=4.50.2",
    "validators>=0.18.2",
    "xarray>=2024.10.0",
-   "zarr>=2.6.1,<3.0.0",
-   "spatialdata>=0.2.5",
+   "zarr>=2.6.1,<3.0.0", "imagecodecs>=2025.8.2,<2026",
 ]
 
 [project.optional-dependencies]
 dev = [
     "pre-commit>=3.0.0",
     "hatch>=1.9.0",
+    "jupyterlab",
+    "notebook",
+    "ipykernel",
+    "ipywidgets",
+    "jupytext",
+    "pytest",
+    "pytest-cov",
+    "ruff",
 ]
 test = [
     "scanpy[leiden]",
     "pytest>=7",
     "pytest-xdist>=3",
     "pytest-mock>=3.5.0",
-    # Just for VS Code
     "pytest-cov>=4",
     "coverage[toml]>=7",
     "pytest-timeout>=2.1.0",
@@ -281,4 +291,41 @@ exclude_lines = [
 show_missing = true
 precision = 2
 skip_empty = true
-sort = "Miss"
+sort = "Miss"
+
+[tool.pixi.workspace]
+channels = ["conda-forge"]
+platforms = ["osx-arm64", "linux-64"]
+
+[tool.pixi.dependencies]
+python = ">=3.11"
+
+[tool.pixi.pypi-dependencies]
+squidpy = { path = ".", editable = true }
+
+# for gh-actions
+[tool.pixi.feature.py311.dependencies]
+python = "3.11.*"
+
+[tool.pixi.feature.py313.dependencies]
+python = "3.13.*"
+
+[tool.pixi.environments]
+# 3.11 lane (for gh-actions)
+dev-py311  = { features = ["dev", "test", "py311"], solve-group = "py311" }
+docs-py311 = { features = ["docs", "py311"],        solve-group = "py311" }
+
+# 3.12 lane
+default    = { features = ["py313"],                 solve-group = "py313" }
+dev-py313  = { features = ["dev", "test", "py313"],  solve-group = "py313" }
+docs-py313 = { features = ["docs", "py313"],         solve-group = "py313" }
+test-py313 = { features = ["test", "py313"],         solve-group = "py313" }
+
+[tool.pixi.tasks]
+lab = "jupyter lab"
+kernel-install = "python -m ipykernel install --user --name pixi-dev --display-name \"sdata-plot (dev)\""
+test = "pytest -v --color=yes --tb=short --durations=10"
+lint = "ruff check ."
+format = "ruff format ."
+pre-commit-install = "pre-commit install"
+pre-commit = "pre-commit run"

src/squidpy/__init__.py

@@ -3,7 +3,7 @@
 from importlib import metadata
 from importlib.metadata import PackageMetadata
 
-from squidpy import datasets, gr, im, pl, read, tl
+from squidpy import datasets, exp, gr, im, pl, read, tl
 
 try:
     md: PackageMetadata = metadata.metadata(__name__)
@@ -14,3 +14,5 @@
     md = None  # type: ignore[assignment]
 
 del metadata, md
+
+__all__ = ["datasets", "exp", "gr", "im", "pl", "read", "tl"]

src/squidpy/_utils.py

@@ -16,6 +16,8 @@
 import joblib as jl
 import numba
 import numpy as np
+import spatialdata as sd
+from spatialdata.models import Image2DModel, Labels2DModel
 
 __all__ = ["singledispatchmethod", "Signal", "SigQueue", "NDArray", "NDArrayA"]
 
@@ -347,3 +349,27 @@ def new_func2(*args: Any, **kwargs: Any) -> Any:
 
     else:
         raise TypeError(repr(type(reason)))
+
+
+def _get_scale_factors(
+    element: Image2DModel | Labels2DModel,
+) -> list[float]:
+    """
+    Get the scale factors of an image or labels.
+    """
+    if not hasattr(element, "keys"):
+        return []  # element isn't a datatree -> single scale
+
+    shapes = [_yx_from_shape(element[scale].image.shape) for scale in element.keys()]
+
+    factors: list[float] = [(y0 / y1 + x0 / x1) / 2 for (y0, x0), (y1, x1) in zip(shapes, shapes[1:], strict=False)]
+    return [int(f) for f in factors]
+
+
+def _yx_from_shape(shape: tuple[int, ...]) -> tuple[int, int]:
+    if len(shape) == 2:  # (y, x)
+        return shape[0], shape[1]
+    if len(shape) == 3:  # (c, y, x)
+        return shape[1], shape[2]
+
+    raise ValueError(f"Unsupported shape {shape}. Expected (y, x) or (c, y, x).")

src/squidpy/exp/__init__.py

@@ -0,0 +1,11 @@
+"""Experimental module for Squidpy.
+
+This module contains experimental features that are still under development.
+These features may change or be removed in future releases.
+"""
+
+from __future__ import annotations
+
+from .im._detect_tissue import detect_tissue
+
+__all__ = ["detect_tissue"]

src/squidpy/exp/im/__init__.py

@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+from ._detect_tissue import detect_tissue
+
+__all__ = ["detect_tissue"]