mpi: fix typo in comm size

Author: mloubout

devito/data/data.py

@@ -176,11 +176,10 @@ def _check_idx(func):
         @wraps(func)
         def wrapper(data, *args, **kwargs):
             glb_idx = args[0]
-            is_gather = isinstance(kwargs.get('gather_rank', None), int)
+            is_gather = isinstance(kwargs.get('gather_rank'), int)
             if is_gather and all(i == slice(None, None, 1) for i in glb_idx):
                 comm_type = gather
-            elif len(args) > 1 and isinstance(args[1], Data) \
-                    and args[1]._is_decomposed:
+            elif len(args) > 1 and isinstance(args[1], Data) and args[1]._is_decomposed:
                 comm_type = index_by_index
             elif data._is_decomposed:
                 for i in as_tuple(glb_idx):
@@ -197,10 +196,8 @@ def wrapper(data, *args, **kwargs):
 
     @property
     def _is_decomposed(self):
-        is_mpi = self._is_distributed and configuration['mpi']
-        if is_mpi:
-            is_mpi = is_mpi and self._distributor.comm.size > 2
-        return is_mpi
+        return self._is_distributed and configuration['mpi'] and \
+            self._distributor.comm.size > 1
 
     def __repr__(self):
         return super(Data, self._local).__repr__()

examples/seismic/self_adjoint/sa_03_iso_correctness.ipynb

@@ -128,12 +128,12 @@ def test_linearization_F(self, shape, dtype, so):
         ns = 2 * size + 1
         if len(shape) == 2:
             nx2, nz2 = shape[0]//2, shape[1]//2
-            dm.data[nx2-size:nx2+size, nz2-size:nz2+size] = \
+            dm.data[nx2-size:nx2+size+1, nz2-size:nz2+size+1] = \
                 -1 + 2 * np.random.rand(ns, ns)
         else:
             nx2, ny2, nz2 = shape[0]//2, shape[1]//2, shape[2]//2
             nx, ny, nz = shape
-            dm.data[nx2-size:nx2+size, ny2-size:ny2+size, nz2-size:nz2+size] = \
+            dm.data[nx2-size:nx2+size+1, ny2-size:ny2+size+1, nz2-size:nz2+size+1] = \
                 -1 + 2 * np.random.rand(ns, ns, ns)
 
         # Compute F(m + dm)
@@ -194,12 +194,12 @@ def test_linearity_forward_J(self, shape, dtype, so):
         ns = 2 * size + 1
         if len(shape) == 2:
             nx2, nz2 = shape[0]//2, shape[1]//2
-            m1.data[nx2-size:nx2+size, nz2-size:nz2+size] = \
+            m1.data[nx2-size:nx2+size+1, nz2-size:nz2+size+1] = \
                 -1 + 2 * np.random.rand(ns, ns)
         else:
             nx2, ny2, nz2 = shape[0]//2, shape[1]//2, shape[2]//2
             nx, ny, nz = shape
-            m1.data[nx2-size:nx2+size, ny2-size:ny2+size, nz2-size:nz2+size] = \
+            m1.data[nx2-size:nx2+size+1, ny2-size:ny2+size+1, nz2-size:nz2+size+1] = \
                 -1 + 2 * np.random.rand(ns, ns, ns)
 
         a = np.random.rand()
@@ -242,12 +242,12 @@ def test_linearity_adjoint_J(self, shape, dtype, so):
         ns = 2 * size + 1
         if len(shape) == 2:
             nx2, nz2 = shape[0]//2, shape[1]//2
-            m1.data[nx2-size:nx2+size, nz2-size:nz2+size] = \
+            m1.data[nx2-size:nx2+size+1, nz2-size:nz2+size+1] = \
                 -1 + 2 * np.random.rand(ns, ns)
         else:
             nx2, ny2, nz2 = shape[0]//2, shape[1]//2, shape[2]//2
             nx, ny, nz = shape
-            m1.data[nx2-size:nx2+size, ny2-size:ny2+size, nz2-size:nz2+size] = \
+            m1.data[nx2-size:nx2+size+1, ny2-size:ny2+size+1, nz2-size:nz2+size+1] = \
                 -1 + 2 * np.random.rand(ns, ns, ns)
 
         a = np.random.rand()
@@ -289,12 +289,12 @@ def test_adjoint_J(self, shape, dtype, so):
         ns = 2 * size + 1
         if len(shape) == 2:
             nx2, nz2 = shape[0]//2, shape[1]//2
-            dm1.data[nx2-size:nx2+size, nz2-size:nz2+size] = \
+            dm1.data[nx2-size:nx2+size+1, nz2-size:nz2+size+1] = \
                 -1 + 2 * np.random.rand(ns, ns)
         else:
             nx2, ny2, nz2 = shape[0]//2, shape[1]//2, shape[2]//2
             nx, ny, nz = shape
-            dm1.data[nx2-size:nx2+size, ny2-size:ny2+size, nz2-size:nz2+size] = \
+            dm1.data[nx2-size:nx2+size+1, ny2-size:ny2+size+1, nz2-size:nz2+size+1] = \
                 -1 + 2 * np.random.rand(ns, ns, ns)
 
         # Data perturbation