Fix communication for directions without PBC

Signed-off-by: Rafael Ravedutti <rafaelravedutti@gmail.com>

src/pairs/sim/domain_partitioning.py

@@ -11,7 +11,7 @@ from pairs.sim.lowerable import Lowerable
 
 class DimensionRanges:
     def __init__(self, sim):
-        self.sim = sim
+        self.sim            = sim
         self.neighbor_ranks = sim.add_static_array('neighbor_ranks', [sim.ndims() * 2], Types.Int32)
         self.pbc            = sim.add_static_array('pbc', [sim.ndims() * 2], Types.Int32)
         self.subdom         = sim.add_static_array('subdom', [sim.ndims() * 2], Types.Real)
@@ -23,20 +23,37 @@ class DimensionRanges:
         return [step * 2 + 0, step * 2 + 1]
 
     def ghost_particles(self, step, position, offset=0.0):
-        if self.sim._pbc[step] is False:
-            return
-
         # Particles with one of the following flags are ignored
         flags_to_exclude = (Flags.Infinite | Flags.Global)
 
-        for i in For(self.sim, 0, self.sim.nlocal + self.sim.nghost):
-            for _ in Filter(self.sim, ScalarOp.cmp(self.sim.particle_flags[i] & flags_to_exclude, 0)):
-                j = step * 2 + 0
-                for _ in Filter(self.sim, position[i][step] < self.subdom[j] + offset):
-                    yield i, j, self.neighbor_ranks[j], [0 if d != step else self.pbc[j] for d in range(self.sim.ndims())]
-
-        for i in For(self.sim, 0, self.sim.nlocal + self.sim.nghost):
-            for _ in Filter(self.sim, ScalarOp.cmp(self.sim.particle_flags[i] & flags_to_exclude, 0)):
-                j = step * 2 + 1
-                for _ in Filter(self.sim, position[i][step] > self.subdom[j] - offset):
-                    yield i, j, self.neighbor_ranks[j], [0 if d != step else self.pbc[j] for d in range(self.sim.ndims())]
+        def next_neighbor(self, j, step, position, offset, flags_to_exclude):
+            particle_flags = self.sim.particle_flags
+            for i in For(self.sim, 0, self.sim.nlocal + self.sim.nghost):
+                for _ in Filter(self.sim, ScalarOp.cmp(particle_flags[i] & flags_to_exclude, 0)):
+                    for _ in Filter(self.sim, position[i][step] < self.subdom[j] + offset):
+                        pbc_shifts = [0 if d != step else self.pbc[j] for d in range(self.sim.ndims())]
+                        yield i, j, self.neighbor_ranks[j], pbc_shifts
+
+
+
+        def prev_neighbor(self, j, step, position, offset, flags_to_exclude):
+            particle_flags = self.sim.particle_flags
+            j = step * 2 + 1
+            for i in For(self.sim, 0, self.sim.nlocal + self.sim.nghost):
+                for _ in Filter(self.sim, ScalarOp.cmp(particle_flags[i] & flags_to_exclude, 0)):
+                    for _ in Filter(self.sim, position[i][step] > self.subdom[j] - offset):
+                        pbc_shifts = [0 if d != step else self.pbc[j] for d in range(self.sim.ndims())]
+                        yield i, j, self.neighbor_ranks[j], pbc_shifts
+
+        if self.sim._pbc[step]:
+            yield from next_neighbor(self, step * 2 + 0, step, position, offset, flags_to_exclude)
+            yield from prev_neighbor(self, step * 2 + 1, step, position, offset, flags_to_exclude)
+
+        else:
+            j = step * 2 + 0
+            for _ in Filter(self.sim, ScalarOp.inline(ScalarOp.cmp(self.pbc[j], 0))):
+                yield from next_neighbor(self, j, step, position, offset, flags_to_exclude)
+
+            j = step * 2 + 1
+            for _ in Filter(self.sim, ScalarOp.inline(ScalarOp.cmp(self.pbc[j], 0))):
+                yield from prev_neighbor(self, j, step, position, offset, flags_to_exclude)