CI: add single mpi sparse test

conftest.py

@@ -177,7 +177,7 @@ def parallel(item, m):
     args = ["-n", "1", pyversion, "-m", "pytest", "-s", "--runxfail", "-qq", testname]
     if nprocs > 1:
         args.extend([":", "-n", "%d" % (nprocs - 1), pyversion, "-m", "pytest",
-                     "-s", "--runxfail", "--tb=no", "-qq", "--no-summary", testname])
+                     "-s", "--runxfail", "--tb=no", "-qq", testname])
     # OpenMPI requires an explicit flag for oversubscription. We need it as some
     # of the MPI tests will spawn lots of processes
     if mpi_distro == 'OpenMPI':

devito/data/data.py

@@ -130,7 +130,8 @@ def __array_finalize__(self, obj):
                     decomposition.append(dec.reshape(i))
             self._decomposition = tuple(decomposition)
             self._allocator = obj._allocator
-            self._is_distributed = any(i is not None for i in self._decomposition)
+            decomp = any(i is not None for i in self._decomposition)
+            self._is_distributed = decomp and obj._is_distributed
         else:
             self._distributor = obj._distributor
             self._allocator = obj._allocator
@@ -142,7 +143,8 @@ def __array_finalize__(self, obj):
                 # E.g., from a reduction operation such as `np.mean` or `np.all`
                 self._modulo = tuple(False for i in range(self.ndim))
                 self._decomposition = (None,)*self.ndim
-            self._is_distributed = any(i is not None for i in self._decomposition)
+            decomp = any(i is not None for i in self._decomposition)
+            self._is_distributed = decomp and obj._is_distributed
 
     @property
     def _local(self):

tests/test_data.py

@@ -208,6 +208,17 @@ def test_indexing_into_sparse(self):
         sf.data[1:-1, 0] = np.arange(8)
         assert np.all(sf.data[1:-1, 0] == np.arange(8))
 
+    @pytest.mark.parallel(mode=1)
+    def test_indexing_into_sparse_subfunc_singlempi(self, mode):
+        grid = Grid(shape=(4, 4))
+        s = SparseFunction(name='sf', grid=grid, npoint=1)
+        coords = np.random.rand(*s.coordinates.data.shape)
+        s.coordinates.data[:] = coords
+
+        s.coordinates.data[-1, :] = s.coordinates.data[-1, :] / 2
+
+        assert np.allclose(s.coordinates.data[-1, :], coords[-1, :] / 2)
+
 
 class TestLocDataIDX:
     """