Apply spelling corrections and variable renaming from code review of branch improve-scan-planning

Co-authored-by: Joe Knapper <joe.knapper@glasgow.ac.uk>
This commit is contained in:
Julian Stirling 2025-10-20 17:13:22 +00:00
parent dbb0a4ac16
commit ff606ff10d
2 changed files with 5 additions and 5 deletions

View file

@ -435,9 +435,9 @@ class SmartSpiral(ScanPlanner):
# Get the VisitedScanLocation object if already visited
visited = self.get_visited_location(new_pos)
if visited.imaged:
# If this adjacent position was imaged succsfully already then skip.
# If this adjacent position was imaged successfully already then skip.
continue
# If it wasn't imaged add an intimediate location between the
# If it wasn't imaged add an intermediate location between the
# last imaged position and this one.
i_pos = self._intermediate_position(xy_pos, new_pos)
# Set primary=False surrounding images are not added once imaged.
@ -459,7 +459,7 @@ class SmartSpiral(ScanPlanner):
This is called after an image is recorded that was background. Intermediate
locations are added between any adjacent locations that were successfully
imaged.
imaged due to being labelled as containing sample.
Note that in the case that an imaged location has an adjacent background image
then adding the intermediate image will be handled by
@ -474,7 +474,7 @@ class SmartSpiral(ScanPlanner):
if not visited.imaged:
# If it wasn't imaged then skip this position
continue
# If surrpounding location was imaged add an intimediate location
# If surrounding location was imaged add an intermediate location
# between the most recent position and this imaged position.
i_pos = self._intermediate_position(xy_pos, surr_pos)
# Set primary=False surrounding images are not added once imaged.

View file

@ -101,7 +101,7 @@ def interp_closed_path(xy_points: list[tuple[int, int]], n_points: int) -> MatPa
# fit splines to x=f(u) and y=g(u), treating both as periodic. also note that s=0
# is needed in order to force the spline fit to pass through all the input points.
spline_data, *_extra = interpolate.splprep([x, y], s=0, per=True)
spline_data, *_unused = interpolate.splprep([x, y], s=0, per=True)
# evaluate the spline
xi, yi = interpolate.splev(np.linspace(0, 1, n_points), spline_data)