CI: add large radius precomputed interp test

devito/operations/interpolators.py

@@ -330,7 +330,7 @@ class LinearInterpolator(WeightedInterpolator):
     def _weights(self):
         # (1 - p) * (1 - rd) + rd * p
         # simplified for better arithmetic
-        c = [1 - p + rd * (2*p - 1)
+        c = [1.0 - p + rd * (2.0*p - 1.0)
              for (p, d, rd) in zip(self._point_symbols, self._gdim, self._rdim)]
         return prod(c)
 

devito/passes/iet/mpi.py

@@ -47,9 +47,9 @@ def _drop_halospots(iet):
 
     # If a HaloSpot is outside any iteration it is not needed
     for iters, halo_spots in MapNodes(Iteration, HaloSpot, 'groupby').visit(iet).items():
-        if not iters and halo_spots:
-            for hs in halo_spots:
-                for f in hs.fmapper:
+        for hs in halo_spots:
+            for f, v in hs.fmapper.items():
+                if not iters and v.loc_indices:
                     mapper[hs].add(f)
 
     # Transform the IET introducing the "reduced" HaloSpots

tests/test_dse.py

@@ -2606,7 +2606,7 @@ def test_premature_evalderiv_lowering(self):
 
     def test_dtype_aliases(self):
         a = np.arange(64).reshape((8, 8))
-        grid = Grid(shape=a.shape, extent=(8, 8))
+        grid = Grid(shape=a.shape, extent=(7, 7))
 
         so = 2
         f = Function(name='f', grid=grid, space_order=so, dtype=np.int32)
@@ -2617,7 +2617,7 @@ def test_dtype_aliases(self):
         op.apply()
 
         assert FindNodes(Expression).visit(op)[0].dtype == np.float32
-        assert np.all(fo.data[:-1, :-1] == 6)
+        assert np.all(fo.data[:-1, :-1] == 8)
 
 
 class TestIsoAcoustic(object):

tests/test_interpolation.py

@@ -84,25 +84,31 @@ def custom_points(grid, ranges, npoints, name='points'):
     return points
 
 
-def precompute_linear_interpolation(points, grid, origin):
-    """ Sample precompute function that, given point and grid information
-        precomputes gridpoints and interpolation coefficients according to a linear
-        scheme to be used in PrecomputedSparseFunction.
+def precompute_linear_interpolation(points, grid, origin, r=2):
+    """
+    Sample precompute function that, given point and grid information
+    precomputes gridpoints and interpolation coefficients according to a linear
+    scheme to be used in PrecomputedSparseFunction.
+
+    Allow larger radius with zero weights for testing.
     """
     gridpoints = [tuple(floor((point[i]-origin[i])/grid.spacing[i])
                         for i in range(len(point))) for point in points]
 
-    interpolation_coeffs = np.zeros((len(points), 2, 2))
+    interpolation_coeffs = np.zeros((len(points), grid.dim, r))
+    rs = r // 2 - 1
     for i, point in enumerate(points):
         for d in range(grid.dim):
-            interpolation_coeffs[i, d, 0] = ((gridpoints[i][d] + 1)*grid.spacing[d] -
-                                             point[d])/grid.spacing[d]
-            interpolation_coeffs[i, d, 1] = (point[d]-gridpoints[i][d]*grid.spacing[d])\
+            gd = gridpoints[i][d]
+            interpolation_coeffs[i, d, rs] = ((gd + 1)*grid.spacing[d] -
+                                              point[d])/grid.spacing[d]
+            interpolation_coeffs[i, d, rs+1] = (point[d]-gd*grid.spacing[d])\
                 / grid.spacing[d]
     return gridpoints, interpolation_coeffs
 
 
-def test_precomputed_interpolation():
+@pytest.mark.parametrize('r', [2, 4, 6])
+def test_precomputed_interpolation(r):
     """ Test interpolation with PrecomputedSparseFunction which accepts
         precomputed values for interpolation coefficients
     """
@@ -123,7 +129,8 @@ def init(data):
     m = Function(name='m', grid=grid, initializer=init, space_order=0)
 
     gridpoints, interpolation_coeffs = precompute_linear_interpolation(points,
-                                                                       grid, origin)
+                                                                       grid, origin,
+                                                                       r=r)
 
     sf = PrecomputedSparseFunction(name='s', grid=grid, r=r, npoint=len(points),
                                    gridpoints=gridpoints,
@@ -136,7 +143,8 @@ def init(data):
     assert(all(np.isclose(sf.data, expected_values, rtol=1e-6)))
 
 
-def test_precomputed_interpolation_time():
+@pytest.mark.parametrize('r', [2, 4, 6])
+def test_precomputed_interpolation_time(r):
     """ Test interpolation with PrecomputedSparseFunction which accepts
         precomputed values for interpolation coefficients, but this time
         with a TimeFunction
@@ -154,7 +162,8 @@ def test_precomputed_interpolation_time():
         u.data[it, :] = it
 
     gridpoints, interpolation_coeffs = precompute_linear_interpolation(points,
-                                                                       grid, origin)
+                                                                       grid, origin,
+                                                                       r=r)
 
     sf = PrecomputedSparseTimeFunction(name='s', grid=grid, r=r, npoint=len(points),
                                        nt=5, gridpoints=gridpoints,
@@ -171,7 +180,8 @@ def test_precomputed_interpolation_time():
         assert np.allclose(sf.data[it, :], it)
 
 
-def test_precomputed_injection():
+@pytest.mark.parametrize('r', [2, 4, 6])
+def test_precomputed_injection(r):
     """Test injection with PrecomputedSparseFunction which accepts
        precomputed values for interpolation coefficients
     """
@@ -188,7 +198,8 @@ def test_precomputed_injection():
     m.data[:] = 0.
 
     gridpoints, interpolation_coeffs = precompute_linear_interpolation(coords,
-                                                                       m.grid, origin)
+                                                                       m.grid, origin,
+                                                                       r=r)
 
     sf = PrecomputedSparseFunction(name='s', grid=m.grid, r=r, npoint=len(coords),
                                    gridpoints=gridpoints,
@@ -206,7 +217,8 @@ def test_precomputed_injection():
     assert np.allclose(m.data[indices], result, rtol=1.e-5)
 
 
-def test_precomputed_injection_time():
+@pytest.mark.parametrize('r', [2, 4, 6])
+def test_precomputed_injection_time(r):
     """Test injection with PrecomputedSparseFunction which accepts
        precomputed values for interpolation coefficients
     """
@@ -224,7 +236,8 @@ def test_precomputed_injection_time():
     m.data[:] = 0.
 
     gridpoints, interpolation_coeffs = precompute_linear_interpolation(coords,
-                                                                       m.grid, origin)
+                                                                       m.grid, origin,
+                                                                       r=r)
 
     sf = PrecomputedSparseTimeFunction(name='s', grid=m.grid, r=r, npoint=len(coords),
                                        gridpoints=gridpoints, nt=nt,