Functions to QC histopathology images

pyproject.toml

@@ -94,7 +94,6 @@ test = [
     "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",

src/squidpy/_utils.py

@@ -11,12 +11,13 @@
 from multiprocessing import Manager, cpu_count
 from queue import Queue
 from threading import Thread
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal
 
 import joblib as jl
 import numba
 import numpy as np
 import spatialdata as sd
+import xarray as xr
 from spatialdata.models import Image2DModel, Labels2DModel
 
 __all__ = ["singledispatchmethod", "Signal", "SigQueue", "NDArray", "NDArrayA"]
@@ -373,3 +374,17 @@ def _yx_from_shape(shape: tuple[int, ...]) -> tuple[int, int]:
         return shape[1], shape[2]
 
     raise ValueError(f"Unsupported shape {shape}. Expected (y, x) or (c, y, x).")
+
+
+def _ensure_dim_order(img_da: xr.DataArray, order: Literal["cyx", "yxc"] = "yxc") -> xr.DataArray:
+    """
+    Ensure dims are in the requested order and that a 'c' dim exists.
+    Only supports images with dims subset of {'y','x','c'}.
+    """
+    dims = list(img_da.dims)
+    if "y" not in dims or "x" not in dims:
+        raise ValueError(f'Expected dims to include "y" and "x". Found dims={dims}')
+    if "c" not in dims:
+        img_da = img_da.expand_dims({"c": [0]})
+    # After possible expand, just transpose to target
+    return img_da.transpose(*tuple(order))

src/squidpy/datasets/_utils.py

@@ -185,7 +185,11 @@ def _extension(self) -> str:
 def _get_zipped_dataset(folderpath: Path, dataset_name: str, figshare_id: str) -> sd.SpatialData:
     """Returns a specific dataset as SpatialData object. If the file is not present on disk, it will be downloaded and extracted."""
 
-    if not folderpath.is_dir():
+    # Create directory if it doesn't exist
+    if not folderpath.exists():
+        logg.info(f"Creating directory `{folderpath}`")
+        folderpath.mkdir(parents=True, exist_ok=True)
+    elif not folderpath.is_dir():
         raise ValueError(f"Expected a directory path for `folderpath`, found: {folderpath}")
 
     download_zip = folderpath / f"{dataset_name}.zip"

src/squidpy/experimental/__init__.py

@@ -6,7 +6,6 @@
 
 from __future__ import annotations
 
-from . import im
-from .im._detect_tissue import detect_tissue
+from . import im, pl
 
-__all__ = ["detect_tissue", "im"]
+__all__ = ["im", "pl"]

src/squidpy/experimental/im/__init__.py

@@ -5,5 +5,13 @@
     FelzenszwalbParams,
     detect_tissue,
 )
+from ._qc_sharpness import qc_sharpness
+from ._sharpness_metrics import SharpnessMetric
 
-__all__ = ["detect_tissue", "BackgroundDetectionParams", "FelzenszwalbParams"]
+__all__ = [
+    "qc_sharpness",
+    "detect_tissue",
+    "SharpnessMetric",
+    "BackgroundDetectionParams",
+    "FelzenszwalbParams",
+]

src/squidpy/experimental/im/_detect_tissue.py

@@ -17,9 +17,9 @@
 from spatialdata.models import Labels2DModel
 from spatialdata.transformations import get_transformation
 
-from squidpy._utils import _get_scale_factors, _yx_from_shape
+from squidpy._utils import _ensure_dim_order, _get_scale_factors, _yx_from_shape
 
-from ._utils import _flatten_channels, _get_image_data
+from ._utils import _flatten_channels, _get_element_data
 
 
 class DETECT_TISSUE_METHOD(enum.Enum):
@@ -170,7 +170,9 @@ def detect_tissue(
     manual_scale = scale.lower() != "auto"
 
     # Load smallest available or explicit scale
-    img_src = _get_image_data(sdata, image_key, scale=scale if manual_scale else "auto")
+    img_node = sdata.images[image_key]
+    img_da = _get_element_data(img_node, scale if manual_scale else "auto", "image", image_key)
+    img_src = _ensure_dim_order(img_da, "yxc")
     src_h, src_w = _yx_from_shape(img_src.shape)
     n_src_px = src_h * src_w