Fix linting issues

.github/workflows/linters.yml

@@ -28,5 +28,6 @@ jobs:
     - name: Run linters
       run: |
         flake8 superdsm
+        flake8 tests
         isort superdsm --check-only --diff
         isort tests --check-only --diff

tests/regression/batch-validate.py

@@ -3,11 +3,11 @@
 import pathlib
 import socket
 
-
 parser = argparse.ArgumentParser(prog='Regression testing (batch)')
 
 parser.add_argument('actual_csv', help='Root directory where the actual CSV will be stored.')
-parser.add_argument('--taskdirs', help='Directory for which the /seg subdirectory will be validated.', nargs='+', default=list())
+parser.add_argument('--taskdirs', help='Directory for which the /seg subdirectory will be validated.', nargs='+',
+                    default=list())
 parser.add_argument('--update-expected', help='Update the expected CSV.', action='store_true')
 
 args = parser.parse_args()
@@ -22,4 +22,7 @@
 
     actual_csv = pathlib.Path(args.actual_csv) / taskdir
     actual_csv.mkdir(parents=True, exist_ok=True)
-    os.system(f'python tests/regression/validate.py "examples/{taskdir}/seg" "{str(actual_csv)}" "tests/regression/expected/{hostname}/{taskdir}" {options}')
+    os.system(
+        f'python tests/regression/validate.py '
+        f'"examples/{taskdir}/seg" "{str(actual_csv)}" "tests/regression/expected/{hostname}/{taskdir}" {options}'
+    )

tests/regression/validate.py

@@ -1,12 +1,12 @@
-import sys
 import argparse
+import csv
 import glob
 import pathlib
-import skimage.io
-import csv
-import scipy.ndimage as ndi
 import shutil
+import sys
 
+import scipy.ndimage as ndi
+import skimage.io
 
 parser = argparse.ArgumentParser(prog='Regression testing')
 
@@ -30,14 +30,14 @@
 
     img = skimage.io.imread(str(actual_seg_filepath))
     actual_csv_rows = list()
-    for l in frozenset(img.flatten()) - {0}:
+    for l in frozenset(img.flatten()) - {0}:  # noqa: E741
         cc = (img == l)
         cc_center_rc = ndi.center_of_mass(cc)
         actual_csv_rows.append((str(cc.sum()), str(round(cc_center_rc[1], 1)), str(round(cc_center_rc[0], 1))))
 
-    actual_csv_rows.sort(key = lambda row: row[1:3])
+    actual_csv_rows.sort(key=lambda row: row[1:3])
     with actual_csv_filepath.open('w') as fp:
-        writer = csv.writer(fp, delimiter = ',', quoting = csv.QUOTE_ALL)
+        writer = csv.writer(fp, delimiter=',', quoting=csv.QUOTE_ALL)
         writer.writerows([['Object size', 'Center X', 'Center Y']] + actual_csv_rows)
 
     actual_csv_rows_by_filename[actual_seg_filepath.name] = frozenset(actual_csv_rows)
@@ -67,24 +67,28 @@
 
     expected_csv_rows = list()
     with expected_csv_filepath.open('r') as fp:
-        reader = csv.reader(fp, delimiter = ',', quoting = csv.QUOTE_ALL)
+        reader = csv.reader(fp, delimiter=',', quoting=csv.QUOTE_ALL)
         for ridx, row in enumerate(reader):
-            if ridx == 0: continue  ## skip header
+            if ridx == 0:
+                continue  # skip header
             expected_csv_rows.append(tuple(row))
 
     expected_csv_rows = frozenset(expected_csv_rows)
     missing_rows  = expected_csv_rows - actual_csv_rows
     spurious_rows = actual_csv_rows - expected_csv_rows
 
     if len(spurious_rows) > 0 or len(missing_rows) > 0:
-        errors.append(f'{actual_seg_filename}: {len(spurious_rows)} spurious object(s) and {len(missing_rows)} missing object(s) cannot be matched')
+        errors.append(
+            f'{actual_seg_filename}: {len(spurious_rows)} spurious object(s) '
+            f'and {len(missing_rows)} missing object(s) cannot be matched'
+        )
 
 
 for remaining_filename in actual_csv_rows_by_filename.keys():
     errors.append(f'Spurious label map: "{remaining_filename}"')
 
 
-if 'img' not in locals():  ## an additional error check to prevent passing tests due to wrongs paths
+if 'img' not in locals():  # an additional error check to prevent passing tests due to wrongs paths
     errors.append('No label maps found')
 
 if len(errors) == 0:

tests/test_atoms.py

@@ -1,5 +1,7 @@
 import unittest
+
 import numpy as np
+
 import superdsm.atoms