Changeset 721

Timestamp:

03/03/10 10:41:30 (2 years ago)

Author:

pierre

Message:

Speed up the DistanceDependentProbabilityConnector? in parallel in the new implementation, by computing only distances that will be used by the nodes. Spot also a bug, not fixed yet, with the positions in parallel. They are not correctly assigned by defaults

Location:

trunk/src

Files:

• connectors.py (modified) (2 diffs)
• space.py (modified) (2 diffs)

trunk/src/connectors.py

@@ -134 +134 @@
                 create = create[:projection.post.local_cells.size]
             targets = projection.post.local_cells[create].tolist()
-            
             weights = self.get_weights(N, local)[create]
             weights = common.check_weight(weights, projection.synapse_type, is_conductance)
@@ -541 +540 @@
         p = {}
         for src in projection.pre.all():
-            d = self.space.distances(src.position, projection.post.positions)
+            local = projection.post._mask_local.flatten()
+            d   = self.space.distances(src.position, projection.post.positions, post_mask=local)
             p[src] = eval(self.d_expression).flatten()
             if p[src].dtype == 'bool':

trunk/src/space.py

@@ -53 +53 @@
         self.offset = offset
         
-    def distances(self, A, B):
+    def distances(self, A, B, post_mask=None):
         """
         Calculate the distance matrix between two sets of coordinates, given
@@ -66 +66 @@
         d = numpy.zeros((A.shape[1], B.shape[1]), dtype=A.dtype)
         for axis in self.axes:
-            diff2 = A[axis,:,None] - B[axis,:]
+            diff2 = A[axis,:,None] - B[axis, post_mask]
             if self.periodic_boundaries is not None:
                 boundaries = self.periodic_boundaries[axis]
                 if boundaries is not None:
                     range = boundaries[1]-boundaries[0]
-                    ad2 = abs(diff2)
+                    ad2   = abs(diff2)
                     diff2 = numpy.minimum(ad2, range-ad2)
             diff2 **= 2
-            d += diff2
-        numpy.sqrt(d, d)
+            d[:,post_mask] += diff2
+        numpy.sqrt(d[:,post_mask], d[:,post_mask])
         return d