Add flake8 bugbear checks

tests/test_camera_buffer.py

@@ -147,13 +147,13 @@ def test_buffer_size_respected():
 
     images = []
     buffer_ids = []
-    for i in range(10):
+    for _i in range(10):
         image = random_image()
         buffer_id = mem_buf.add_image(image, buffer_max=5)
         images.append(image)
         buffer_ids.append(buffer_id)
 
-    for i, (image, buffer_id) in enumerate(zip(images, buffer_ids)):
+    for i, (image, buffer_id) in enumerate(zip(images, buffer_ids, strict=True)):
         if i < 5:
             with pytest.raises(NoImageInMemoryError):
                 mem_buf.get_image(buffer_id)
@@ -168,7 +168,7 @@ def test_clear_buffer():
 
     images = []
     buffer_ids = []
-    for i in range(10):
+    for _i in range(10):
         image = random_image()
         buffer_id = mem_buf.add_image(image, buffer_max=10)
         images.append(image)
@@ -178,7 +178,7 @@ def test_clear_buffer():
     mem_buf.clear()
 
     # They are now gone
-    for i, (image, buffer_id) in enumerate(zip(images, buffer_ids)):
+    for _i, (_image, buffer_id) in enumerate(zip(images, buffer_ids, strict=True)):
         with pytest.raises(NoImageInMemoryError):
             mem_buf.get_image(buffer_id)
 
@@ -190,7 +190,7 @@ def test_get_metadata_too():
     images = []
     metadatas = []
     buffer_ids = []
-    for i in range(10):
+    for _i in range(10):
         image = random_image()
         metadata = random_metadata()
         buffer_id = mem_buf.add_image(image, metadata, buffer_max=10)
@@ -199,10 +199,10 @@ def test_get_metadata_too():
         buffer_ids.append(buffer_id)
 
     # Preallocate zipped data, to avoid long confusing lines
-    zipped = zip(images, metadatas, buffer_ids)
+    zipped = zip(images, metadatas, buffer_ids, strict=True)
 
     # Check both image and metadata
-    for i, (image, metadata, buffer_id) in enumerate(zipped):
+    for _i, (image, metadata, buffer_id) in enumerate(zipped):
         returned_image, returned_metadata = mem_buf.get_image(buffer_id)
         assert image is returned_image
         assert metadata is returned_metadata

tests/test_dummy_server.py

@@ -97,11 +97,11 @@ def test_stage(client):
     move = {"x": 1, "y": 2, "z": 3}
     stage.move_relative(**move)
     pos = stage.position
-    for s, m, p in zip(start.values(), move.values(), pos.values()):
+    for s, m, p in zip(start.values(), move.values(), pos.values(), strict=True):
         assert s + m == p
     stage.move_relative(**{k: -v for k, v in move.items()})
     pos = stage.position
-    for s, p in zip(start.values(), pos.values()):
+    for s, p in zip(start.values(), pos.values(), strict=True):
         assert s == p
 
 

tests/test_scan_directories.py

@@ -245,7 +245,7 @@ def test_scan_info():
     scan_dir_manager = ScanDirectoryManager(BASE_SCAN_DIR)
     scan_dir = scan_dir_manager.new_scan_dir("fake_scan")
 
-    for i in range(17):
+    for _i in range(17):
         _add_fake_image(scan_dir)
     info = scan_dir.scan_info()
     now = time.time()
@@ -273,7 +273,7 @@ def test_get_final_stitch():
     scan_dir = scan_dir_manager.new_scan_dir("fake_scan")
 
     # Create some scan images files
-    for i in range(17):
+    for _i in range(17):
         _add_fake_image(scan_dir)
 
     # No scans, so None should be returned, from both manager and scan dir
@@ -365,7 +365,7 @@ def test_zipping_scan_data():
         scan_dir = scan_dir_manager.new_scan_dir("fake_scan")
 
         # Create 21 fake scan images, a fake stitch, and a fake zip
-        for i in range(21):
+        for _i in range(21):
             _add_fake_image(scan_dir)
         _add_fake_file(scan_dir, "fake_scan_0001_stitched.jpg", in_im_dir=True)
         _add_fake_file(scan_dir, "zipfile.zip")
@@ -502,7 +502,7 @@ def test_extracting_files():
     assert scan_dir._extract_dzi_files(scan_files) == []
 
     # Add a number of images
-    for i in range(2321):
+    for _i in range(2321):
         _add_fake_image(scan_dir)
 
     scan_files = scan_dir.get_scan_files()

tests/test_stage.py

@@ -212,7 +212,7 @@ def _test_move_relative(dummy_stage, axis_inverted, path):
         dummy_stage.move_relative(
             cancel=cancel, x=movement[0], y=movement[1], z=movement[2]
         )
-        position = [pos + move for pos, move in zip(position, movement)]
+        position = [pos + move for pos, move in zip(position, movement, strict=True)]
         stage_pos = dummy_stage.get_xyz_position()
         hw_pos = dummy_stage._hardware_position
         assert position[0] == stage_pos[0] == hw_pos["x"] * x_dir
@@ -244,7 +244,7 @@ def test_move_relative(stage_client, dummy_stage, path):
     axis_names = stage_client.axis_names
     # Create every combination of True/False for x,y,z.
     inversion_combinations = [
-        dict(zip(axis_names, inverted))
+        dict(zip(axis_names, inverted, strict=True))
         for inverted in itertools.product([True, False], repeat=len(axis_names))
     ]
     print(path)
@@ -305,7 +305,7 @@ def test_move_absolute(stage_client, dummy_stage, path):
     axis_names = stage_client.axis_names
     # Create every combination of True/False for x,y,z.
     inversion_combinations = [
-        dict(zip(axis_names, inverted))
+        dict(zip(axis_names, inverted, strict=True))
         for inverted in itertools.product([True, False], repeat=len(axis_names))
     ]
     for axis_inverted in inversion_combinations:

tests/utilities/scan_test_helpers.py

@@ -60,8 +60,8 @@ def visualise_scan(sample: FakeSample, planner: scan_planners.ScanPlanner) -> Fi
     """For a given sample and scanner object return a matplotlib figure of the scan."""
     fig, ax = plt.subplots(figsize=(8, 8))
     ax.add_artist(sample.patch)
-    xh, yh = zip(*planner._path_history)
-    xi, yi, _ = zip(*planner._imaged_locations)
+    xh, yh = zip(*planner._path_history, strict=True)
+    xi, yi, _ = zip(*planner._imaged_locations, strict=True)
 
     # convert history to numpy array so can calculate quiver arrows
     xh = np.array(xh)
@@ -91,7 +91,7 @@ def interp_closed_path(xy_points: list[tuple[int, int]], n_points: int) -> MatPa
     https://stackoverflow.com/questions/33962717/interpolating-a-closed-curve-using-scipy
     """
     # Use zip to separate x and y points into tuples
-    x, y = zip(*xy_points)
+    x, y = zip(*xy_points, strict=True)
 
     # Append first point and convert to array
     x = np.array(x + (x[0],))
@@ -105,7 +105,7 @@ def interp_closed_path(xy_points: list[tuple[int, int]], n_points: int) -> MatPa
     xi, yi = interpolate.splev(np.linspace(0, 1, n_points), spline_data)
 
     # Convert to a matplotlib closed path
-    path_points = [[xp, yp] for xp, yp in (zip(xi, yi))]
+    path_points = [[xp, yp] for xp, yp in (zip(xi, yi, strict=True))]
     return MatPath(path_points, closed=True)