Fix a few "errors" reported by ruff

Author: jdavid

caterva2/__init__.py

@@ -17,9 +17,9 @@
 __all__ = [
     "BasicAuth",
     "Client",
-    "Root",
-    "File",
     "Dataset",
+    "File",
+    "Root",
     "sub_urlbase_default",
 ]
 """List of symbols exported by the Caterva2 package."""

caterva2/tests/test_api.py

@@ -297,7 +297,7 @@ def test_append(auth_client, fields, fill_auth, examples_dir):
     if not auth_client:
         return pytest.skip("authentication support needed")
 
-    fnames, mypublic = fill_auth
+    _, mypublic = fill_auth
     myshared = auth_client.get("@shared")
     fname = "ds-1d.b2nd" if not fields else "ds-1d-fields.b2nd"
     # Copy a 1d dataset to the shared area

caterva2/tests/test_hdf5_proxy.py

@@ -313,7 +313,7 @@ def test_expression(expression, examples_dir, tmp_path, auth_client):
     root = pathlib.Path("@shared")
     remote_root = auth_client.get(root)
     with contextlib.chdir(tmp_path):
-        hdf5_path, remote_dir, file_list = create_and_unfold_hdf5(tmp_path, remote_root)
+        hdf5_path, remote_dir, _ = create_and_unfold_hdf5(tmp_path, remote_root)
         h5f = h5py.File(hdf5_path, "r")
         remote_a = remote_dir / (ds_a + ".b2nd")
         assert remote_a in remote_root

test-images/encode-grok-numbers.py

@@ -11,37 +11,37 @@
 Script for exporting a series of images into a Blosc2 HDF5 file using the Grok codec.
 """
 
+import os
+
+import blosc2
 import blosc2_grok
 import h5py
 import hdf5plugin
 import numpy as np
 from PIL import Image
-import os
-
-import blosc2
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # Define the compression and decompression parameters. Disable the filters and the
     # splitmode, because these don't work with the codec.
     cparams = {
-        'codec': blosc2.Codec.GROK,
-        'nthreads': 1,
-        'filters': [],
-        'splitmode': blosc2.SplitMode.NEVER_SPLIT,
+        "codec": blosc2.Codec.GROK,
+        "nthreads": 1,
+        "filters": [],
+        "splitmode": blosc2.SplitMode.NEVER_SPLIT,
     }
     # Set the parameters that will be used by grok
     kwargs = {
-        'cod_format': blosc2_grok.GrkFileFmt.GRK_FMT_JP2,
-        'num_threads': 1,    # this does not have any effect (grok should work in multithreading mode)
-        'quality_mode': "rates",
+        "cod_format": blosc2_grok.GrkFileFmt.GRK_FMT_JP2,
+        "num_threads": 1,  # this does not have any effect (grok should work in multithreading mode)
+        "quality_mode": "rates",
     }
 
     # Compression params for identifying the codec. In the future, one should be able to
     # specify the grok plugin (and its parameters) here.
     b2params = hdf5plugin.Blosc2()
 
     # Open the PNG datasets
-    dir_path = 'numbers'
+    dir_path = "numbers"
     # List of PNG files (in order)
     png_files = [
         "zero.png",
@@ -62,30 +62,31 @@
         img_color = Image.open(os.path.join(dir_path, png_file))
         images_color.append(np.array(img_color))
         # Convert the image to grayscale
-        img_gray = img_color.convert('L')
+        img_gray = img_color.convert("L")
         images_gray.append(np.array(img_gray))
     dset_color = np.array(images_color)
     dset_gray = np.array(images_gray)
 
     # Open the output file for color and gray images
-    fname = f'numbers-jpeg2000.h5'
+    fname = "numbers-jpeg2000.h5"
     print(f"output file: {fname}")
-    fout = h5py.File(fname, 'w')
+    fout = h5py.File(fname, "w")
 
     for cratio in (2, 5, 10, 20):
         print(f"Compressing with cratio={cratio}x ...")
-        kwargs['quality_layers'] = np.array([cratio], dtype=np.float64)
+        kwargs["quality_layers"] = np.array([cratio], dtype=np.float64)
         blosc2_grok.set_params_defaults(**kwargs)
-        group = fout.create_group(f'/cratio_{cratio}x')
+        group = fout.create_group(f"/cratio_{cratio}x")
         # Store the color and grey images
         for dset in [dset_color, dset_gray]:
             chunks = (1,) + dset.shape[1:]
-            name = 'numbers_color' if dset is dset_color else 'numbers_gray'
-            disk_dset = group.create_dataset(name, shape=dset.shape, dtype=dset.dtype,
-                                             chunks=chunks, **b2params)
-            disk_dset.attrs['contenttype'] = 'tomography'
+            name = "numbers_color" if dset is dset_color else "numbers_gray"
+            disk_dset = group.create_dataset(
+                name, shape=dset.shape, dtype=dset.dtype, chunks=chunks, **b2params
+            )
+            disk_dset.attrs["contenttype"] = "tomography"
             for i in range(dset.shape[0]):
-                im = dset[i:i+1]
+                im = dset[i : i + 1]
                 b2im = blosc2.asarray(im, chunks=im.shape, blocks=im.shape, cparams=cparams)
                 # Write to disk.
                 # Due to leading 1 dim, color images have 4 dimensions, gray images have 3 dimensions