make integer array in fixture

tests/conftest.py

@@ -1,13 +1,23 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING
 from pathlib import Path
 
 import numpy as np
 from iohub import open_ome_zarr
 from pytest import TempPathFactory, fixture
 
+if TYPE_CHECKING:
+    from numpy.typing import DTypeLike
+
 channel_names = ["Phase", "Retardance", "GFP", "DAPI"]
 
 
-def _build_hcs(path: Path, channel_names: list[str], zyx_shape: tuple[int, int, int]):
+def _build_hcs(
+    path: Path,
+    channel_names: list[str],
+    zyx_shape: tuple[int, int, int],
+    dtype: DTypeLike,
+):
     dataset = open_ome_zarr(
         path,
         layout="hcs",
@@ -20,17 +30,15 @@ def _build_hcs(path: Path, channel_names: list[str], zyx_shape: tuple[int, int,
                 pos = dataset.create_position(row, col, fov)
                 pos.create_image(
                     "0",
-                    np.random.rand(2, len(channel_names), *zyx_shape).astype(
-                        np.float32
-                    ),
+                    np.random.rand(2, len(channel_names), *zyx_shape).astype(dtype),
                 )
 
 
 @fixture(scope="session")
 def preprocessed_hcs_dataset(tmp_path_factory: TempPathFactory) -> Path:
     """Provides a preprocessed HCS OME-Zarr dataset."""
     dataset_path = tmp_path_factory.mktemp("preprocessed.zarr")
-    _build_hcs(dataset_path, channel_names, (12, 256, 256))
+    _build_hcs(dataset_path, channel_names, (12, 256, 256), np.float32)
     # U[0, 1)
     expected = {"mean": 0.5, "std": 1 / np.sqrt(12), "median": 0.5, "iqr": 0.5}
     norm_meta = {channel: {"dataset_statistics": expected} for channel in channel_names}
@@ -45,5 +53,5 @@ def preprocessed_hcs_dataset(tmp_path_factory: TempPathFactory) -> Path:
 def small_hcs_dataset(tmp_path_factory: TempPathFactory) -> Path:
     """Provides a small, not preprocessed HCS OME-Zarr dataset."""
     dataset_path = tmp_path_factory.mktemp("small.zarr")
-    _build_hcs(dataset_path, channel_names, (12, 64, 64))
+    _build_hcs(dataset_path, channel_names, (12, 64, 64), np.uint16)
     return dataset_path