workaround for !24

aliby/tile/tiler.py

@@ -14,11 +14,11 @@ from aliby.tile.traps import segment_traps
 
 
 class Trap:
-    '''
+    """
     Stores a trap's location and size.
     Allows checks to see if the trap should be padded.
     Can export the trap either in OMERO or numpy formats.
-    '''
+    """
 
     def __init__(self, centre, parent, size, max_size):
         self.centre = centre
@@ -72,14 +72,15 @@ class Trap:
         x, y, w, h = self.as_tile(tp)
         return slice(x, x + w), slice(y, y + h)
 
+
 ###
 
 
 class TrapLocations:
-    '''
+    """
     Stores each trap as an instance of Trap.
     Traps can be iterated.
-    '''
+    """
 
     def __init__(
         self, initial_location, tile_size, max_size=1200, drifts=None
@@ -105,22 +106,22 @@ class TrapLocations:
 
     @property
     def shape(self):
-        '''
+        """
         Returns no of traps and no of drifts
-        '''
+        """
         return len(self.traps), len(self.drifts)
 
     def padding_required(self, tp):
-        '''
+        """
         Check if any traps need padding
-        '''
+        """
         return any([trap.padding_required(tp) for trap in self.traps])
 
     def to_dict(self, tp):
-        '''
+        """
         Export inital locations, tile_size, max_size, and drifts
         as a dictionary
-        '''
+        """
         res = dict()
         if tp == 0:
             res["trap_locations"] = self.initial_location
@@ -133,16 +134,16 @@ class TrapLocations:
 
     @classmethod
     def from_tiler_init(cls, initial_location, tile_size, max_size=1200):
-        '''
+        """
         Instantiate class from an instance of the Tiler class
-        '''
+        """
         return cls(initial_location, tile_size, max_size, drifts=[])
 
     @classmethod
     def read_hdf5(cls, file):
-        '''
+        """
         Instantiate class from a hdf5 file
-        '''
+        """
         with h5py.File(file, "r") as hfile:
             trap_info = hfile["trap_info"]
             initial_locations = trap_info["trap_locations"][()]
@@ -153,13 +154,12 @@ class TrapLocations:
         trap_locs.drifts = drifts
         return trap_locs
 
+
 ###
 
 
 class TilerParameters(ParametersABC):
-    _defaults = {"tile_size": 117,
-                 "ref_channel": "Brightfield",
-                 "ref_z": 0}
+    _defaults = {"tile_size": 117, "ref_channel": "Brightfield", "ref_z": 0}
 
 
 ####
@@ -227,8 +227,17 @@ class Tiler(ProcessABC):
 
     @lru_cache(maxsize=2)
     def get_tc(self, t, c):
-        # Get image
-        full = self.image[t, c].compute()  # FORCE THE CACHE
+        # Get image by forcing loading it into cache. Assumes TCZYX dimensional order.
+        # WORKADOUND around error (which arose on 2022/06/14) when fetching 3-D data.
+        full = np.stack(
+            [
+                self.image[t, c, z].compute()
+                for z in range(self.image.shape[2])
+            ],
+            axis=0,
+        )
+        # full = self.image[t, c].compute()
+
         return full
 
     ###
@@ -280,9 +289,7 @@ class Tiler(ProcessABC):
         # max_size is the minimal no of x or y pixels
         max_size = min(self.image.shape[-2:])
         # first time point, first channel, first z-position
-        initial_image = self.image[
-            0, self.ref_channel, self.ref_z
-        ]
+        initial_image = self.image[0, self.ref_channel, self.ref_z]
         # find the traps
         trap_locs = segment_traps(initial_image, tile_size)
         # keep only traps that are not near an edge
@@ -298,10 +305,10 @@ class Tiler(ProcessABC):
     ###
 
     def find_drift(self, tp):
-        '''
+        """
         Find any translational drifts between two images at consecutive
         time points using cross correlation
-        '''
+        """
         # TODO check that the drift doesn't move any tiles out of
         # the image, remove them from list if so
         prev_tp = max(0, tp - 1)
@@ -339,11 +346,11 @@ class Tiler(ProcessABC):
     ###
 
     def run_tp(self, tp):
-        '''
+        """
         Find traps if they have not yet been found.
         Determine any translational drift of the current image from the
         previous one.
-        '''
+        """
         # assert tp >= self.n_processed, "Time point already processed"
         # TODO check contiguity?
         if self.n_processed == 0 or not hasattr(self.trap_locs, "drifts"):
@@ -361,9 +368,9 @@ class Tiler(ProcessABC):
         return self.trap_locs.to_dict(tp)
 
     def run(self):
-        '''
+        """
         Tile all time points in an experiment at once.
-        '''
+        """
         raise NotImplementedError()
 
     ###