Use a worker size of 8 chunks.

Author: perlman

flyapp.py

@@ -178,6 +178,7 @@ def map_points(dataset, scale, locs):
        Output: [n,5] numpy array representing n (new_x, new_y, new_z, new_dx, new_dy)
     """
     n5 = get_datasource(dataset, scale)
+    blocksize = np.asarray(n5.shape[:3]) * 2
     voxel_offset = n5.attrs['voxel_offset']
 
     query_points = np.empty_like(locs)
@@ -193,7 +194,8 @@ def map_points(dataset, scale, locs):
         field = np.full((query_points.shape[0], 2), np.NaN)
     else:
         field = process.get_multiple_ids(query_points, n5,
-                                        max_workers=config.MaxWorkers)
+                                        max_workers=config.MaxWorkers,
+                                        blocksize=blocksize)
 
     results = np.zeros(locs.shape[0], dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4'), ('dx', '<f4'), ('dy', '<f4')])
 

process.py

@@ -44,7 +44,7 @@ def _get_ids(vol, bl, co):
     return co_id
 
 
-def get_multiple_ids(x, vol, max_workers=mp.cpu_count() - 5):
+def get_multiple_ids(x, vol, max_workers=mp.cpu_count() - 5, blocksize=np.array([512, 512, 32])):
     """Return multiple segment IDs using cloudvolume.
 
     Parameters
@@ -62,9 +62,6 @@ def get_multiple_ids(x, vol, max_workers=mp.cpu_count() - 5):
     if not max_workers:
         max_workers = 1
 
-    # Hard coded block size
-    blocksize = np.array([512, 512, 32])
-
     # Make bins to fit with blocksize
     xbins = np.arange(0, np.nanmax(x) + blocksize[0] + 1, blocksize[0]).astype(int)
     ybins = np.arange(0, np.nanmax(x) + blocksize[1] + 1, blocksize[1]).astype(int)