Apply suggestions from code review of branch scanning-stability

Co-authored-by: Joe Knapper <joe.knapper@glasgow.ac.uk>

src/openflexure_microscope_server/background_detect.py

@@ -264,7 +264,7 @@ class ColourChannelDetectLUV(BackgroundDetectAlgorithm):
         std = np.std(image_luv, axis=(0, 1))
 
         if np.any(std == 0):
-            raise ChannelBlankError("Some LUV channels have no standard devaition.")
+            raise ChannelBlankError("Some LUV channels have no standard deviation.")
         std = np.maximum(std, self.min_stds)
 
         self.background_data = ChannelDistributions(
@@ -298,7 +298,7 @@ class ChannelDeviationLUV(BackgroundDetectAlgorithm):
         deviations of an 8x8 grid of sub-images to the median standard deviation
         from a background image.
 
-        :returns: A value (between 0 and 100) is the percentage of the image that is
+        :returns: A value (between 0 and 100) that is the percentage of the image that is
             sample.
         """
         if not self.background_data:

src/openflexure_microscope_server/things/autofocus.py

@@ -865,7 +865,7 @@ class NoFocusFoundError(RuntimeError):
 
 
 def _get_peak_turning_point(sharpnesses: np.ndarray) -> float:
-    """Get the turing point for a sharpnesses in a z-stack.
+    """Get the turning point for a sharpnesses in a z-stack.
 
     :param sharpnesses: A numpy array of sharpnesses
     :return: The x value of the turning point where x-axis is 0 to N-1 for the N

tests/test_autofocus.py

@@ -12,7 +12,7 @@ from openflexure_microscope_server.things.autofocus import (
 )
 
 
-def fake_sharpeness_data(
+def fake_sharpness_data(
     dz: int, start_z: int, max_loc: int, length: int = 41
 ) -> tuple[list[float], np.ndarray, np.ndarray]:
     """Create some fake data for the shapeness.
@@ -23,7 +23,7 @@ def fake_sharpeness_data(
     times = [i / 10 + 100000 for i in range(length)]
     img_dz = dz / (length - 1)
     heights = [round(start_z + i * img_dz) for i in range(length)]
-    # Shapeness is falls off linearly in this model.
+    # Sharpnesses fall off linearly in this model.
     sharpnesses = [10 * dz - abs(max_loc - h) for h in heights]
     return times, np.array(heights), np.array(sharpnesses)
 
@@ -31,7 +31,7 @@ def fake_sharpeness_data(
 @pytest.mark.parametrize(
     ("start_z", "max_loc", "centre", "attempts_expected", "passes"),
     [
-        # To complete the max must be in the central 1200, so -600 to 600 when looping
+        # To complete, the max must be in the central 1200, so -600 to 600 when looping
         # from -1000 to 1000
         (0, 550, True, 1, True),  # Found in loop1 from -1000 to 1000
         (0, 650, True, 2, True),  # Just outside the limit in loop1
@@ -67,15 +67,15 @@ def test_looping_autofocus(start_z, max_loc, centre, attempts_expected, passes,
     sharpness_monitor = mocker.MagicMock()
     sharpness_monitor.focus_rel.return_value = (0, 0)
 
-    def return_shapness(*_args) -> tuple[list[float], np.ndarray, np.ndarray]:
-        """Generate shapenesses based on parameterised input, and mock stage position."""
-        return fake_sharpeness_data(
+    def return_sharpness(*_args) -> tuple[list[float], np.ndarray, np.ndarray]:
+        """Generate sharpnesses based on parameterised input, and mock stage position."""
+        return fake_sharpness_data(
             dz=dz,
             start_z=stage.position["z"],
             max_loc=max_loc,
         )
 
-    sharpness_monitor.move_data.side_effect = return_shapness
+    sharpness_monitor.move_data.side_effect = return_sharpness
 
     autofocus_thing = AutofocusThing()
     if passes:

tests/test_background_detectors.py

@@ -291,7 +291,7 @@ def test_channel_deviation_luv_get_sample_coverage(background_image, mocker):
     """Check _get_sample_coverage returns the values expected."""
     cd_luv = ChannelDeviationLUV()
 
-    # Create fake chunked STD data where each channel os the numbers 0 -> 31.5 in 0.5
+    # Create fake chunked STD data where each channel is the numbers 0 -> 31.5 in 0.5
     # steps
     grid = np.arange(0, 32, 0.5).reshape(8, 8)
     fake_stds = np.stack([grid, grid, grid], axis=-1)
@@ -313,7 +313,7 @@ def test_channel_deviation_luv_get_sample_coverage(background_image, mocker):
         means=[0, 0, 0], standard_deviations=[1.6, 1.6, 1.1]
     )
     assert cd_luv.get_sample_coverage(background_image) == 75
-    # This is but increases if any channels has a lower background value.
+    # But coverage increases if any channels has a lower background value.
     cd_luv.background_data = ChannelDistributions(
         means=[0, 0, 0], standard_deviations=[1.6, 0.6, 1.1]
     )

tests/test_stack.py

@@ -425,8 +425,8 @@ def setup_and_run_z_stack(check_returns, check_turning_points, autofocus_thing,
     """Set up a z_stack, run it, and return the result.
 
     :param check_returns: The return values from check_stack_result. Note that if this
-        is a list it will be set as a side effect (and should be  a list of tuples of
-        results. If it a tuple (or anything else) it is set as a return value.
+        is a list, it will be set as a side effect (and should be a list of tuples of
+        results). If it a tuple (or anything else), it is set as a return value.
     """
     stack_params = autofocus_thing.create_stack_params(
         autofocus_dz=2000,
@@ -578,7 +578,7 @@ def _run_check_stack_with_good_peak(autofocus_thing, count_turnings=False):
     result, cap_id = autofocus_thing.check_stack_result(
         captures, check_turning_points=count_turnings
     )
-    # Nothing a mocked function does should change which is the sharpedt image.
+    # Nothing a mocked function does should change which is the sharpest image.
     assert cap_id == 4
     return result