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Scan planner finds lowest neighbour, not most recent

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jaknapper 2025-06-18 14:36:50 +01:00
parent 1d4bb7363f
commit c0677858a1
1 changed files with 15 additions and 7 deletions
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22
src/openflexure_microscope_server/scan_planners.py
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@ -174,8 +174,8 @@ class ScanPlanner:
next_location = self._remaining_locations[0] next_location = self._remaining_locations[0]
# If focussed locations exist return closest location, favouring most recent # If focussed locations exist, return the neighbour with the lowest z position
closest_pos = self.closest_focus_site(next_location) closest_pos = self.select_nearby_focus_site(next_location)
if closest_pos is None: if closest_pos is None:
z = None z = None
else: else:
@ -183,7 +183,7 @@ class ScanPlanner:
return next_location, z return next_location, z
def closest_focus_site(self, xy_pos: XYPos) -> Optional[XYZPos]: def select_nearby_focus_site(self, xy_pos: XYPos) -> Optional[XYZPos]:
""" """
Return the xyz position of the closest site where focus was achieved Return the xyz position of the closest site where focus was achieved
to the input xy_position, with the most recently taken image returned in to the input xy_position, with the most recently taken image returned in
@ -202,12 +202,20 @@ class ScanPlanner:
# Note linalg.norm always uses float64 # Note linalg.norm always uses float64
dists = np.linalg.norm((path_pos - current_pos), axis=1) dists = np.linalg.norm((path_pos - current_pos), axis=1)
# Get indices of all minima. # Get indices of all focused sites within 1.6x the minimum distance.
# 1.6x chosen as it includes offsets in x and y on the Picamera2 aspect ratio
# Note np.where always returns a tuple of arrays, hence the trailing [0] # Note np.where always returns a tuple of arrays, hence the trailing [0]
indices = np.where(dists == np.min(dists))[0] indices = np.where(dists <= 1.6 * np.min(dists))[0]
# The last index is most recent # Turning into an array allows slicing based on a list
return self._focused_locations[indices[-1]] focused_locations_array = np.array(self._focused_locations)
# Choose the lowest (smallest z) of the neighbouring sites. Smart stack works best
# if started too low, so the lowest z will perform best
chosen_focused_site = min(focused_locations_array[indices], key=lambda x: x[-1])
# Convert back into list so values are of type int instead of np.int32
return chosen_focused_site.tolist()
def mark_location_visited( def mark_location_visited(
self, xyz_pos: XYZPos, imaged: bool, focused: bool self, xyz_pos: XYZPos, imaged: bool, focused: bool