- Make bioformats optional because jpype can cause problems

- Ruff format

tests/test_open.py

@@ -7,7 +7,7 @@ import pytest
 from ndbioimage import Imread, ReaderNotFoundError
 
 
-@pytest.mark.parametrize('file', (Path(__file__).parent / 'files').iterdir())
+@pytest.mark.parametrize("file", (Path(__file__).parent / "files").iterdir())
 def test_open(file):
     try:
         with Imread(file) as im:
@@ -21,7 +21,7 @@ def test_open(file):
             w = pickle.loads(b)
             assert w[dict(c=0, z=0, t=0)].mean() == mean
     except ReaderNotFoundError:
-        assert len(Imread.__subclasses__()), 'No subclasses for Imread found.'
+        assert len(Imread.__subclasses__()), "No subclasses for Imread found."
 
     for child in active_children():
         child.kill()

tests/test_slicing.py

@@ -11,8 +11,9 @@ im = Imread(r)
 a = np.array(im)
 
 
-@pytest.mark.parametrize('s', combinations_with_replacement(
-    (0, -1, 1, slice(None), slice(0, 1), slice(-1, 0), slice(1, 1)), 5))
+@pytest.mark.parametrize(
+    "s", combinations_with_replacement((0, -1, 1, slice(None), slice(0, 1), slice(-1, 0), slice(1, 1)), 5)
+)
 def test_slicing(s):
     s_im, s_a = im[s], a[s]
     if isinstance(s_a, Number):

tests/test_ufuncs.py

@@ -10,10 +10,30 @@ im = Imread(r)
 a = np.array(im)
 
 
-@pytest.mark.parametrize('fun_and_axis', product(
-    (np.sum, np.nansum, np.min, np.nanmin, np.max, np.nanmax, np.argmin, np.argmax,
-     np.mean, np.nanmean, np.var, np.nanvar, np.std, np.nanstd), (None, 0, 1, 2, 3, 4)))
+@pytest.mark.parametrize(
+    "fun_and_axis",
+    product(
+        (
+            np.sum,
+            np.nansum,
+            np.min,
+            np.nanmin,
+            np.max,
+            np.nanmax,
+            np.argmin,
+            np.argmax,
+            np.mean,
+            np.nanmean,
+            np.var,
+            np.nanvar,
+            np.std,
+            np.nanstd,
+        ),
+        (None, 0, 1, 2, 3, 4),
+    ),
+)
 def test_ufuncs(fun_and_axis):
     fun, axis = fun_and_axis
-    assert np.all(np.isclose(fun(im, axis), fun(a, axis))), \
-        f'function {fun.__name__} over axis {axis} does not give the correct result'
+    assert np.all(np.isclose(fun(im, axis), fun(a, axis))), (
+        f"function {fun.__name__} over axis {axis} does not give the correct result"
+    )