tests: Enhanced tests for side

Author: EdCaunt

devito/finite_differences/differentiable.py

@@ -303,14 +303,14 @@ def shift(self, dim, shift):
         return self._subs(dim, dim + shift)
 
     @property
-    def laplace(self, **kwargs):
+    def laplace(self):
         """
         Generates a symbolic expression for the Laplacian, the second
         derivative w.r.t all spatial Dimensions.
         """
-        return self.laplacian(**kwargs)
+        return self.laplacian()
 
-    def laplacian(self, shift=None, order=None, method='FD', **kwargs):
+    def laplacian(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Laplacian of the Differentiable with shifted derivatives and custom
         FD order.
@@ -335,7 +335,6 @@ def laplacian(self, shift=None, order=None, method='FD', **kwargs):
         weights/w: list, tuple, or dict, optional, default=None
             Custom weights for the finite difference coefficients.
         """
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         order = order or self.space_order
         space_dims = self.root_dimensions
@@ -345,7 +344,7 @@ def laplacian(self, shift=None, order=None, method='FD', **kwargs):
                                       method=method, fd_order=order, side=side, w=w)
                      for i, d in enumerate(derivs)])
 
-    def div(self, shift=None, order=None, method='FD', **kwargs):
+    def div(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Divergence of the input Function.
 
@@ -367,7 +366,6 @@ def div(self, shift=None, order=None, method='FD', **kwargs):
         weights/w: list, tuple, or dict, optional, default=None
             Custom weights for the finite difference coefficients.
         """
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         space_dims = self.root_dimensions
         shift_x0 = make_shift_x0(shift, (len(space_dims),))
@@ -377,7 +375,7 @@ def div(self, shift=None, order=None, method='FD', **kwargs):
                                                  w=w)
                      for i, d in enumerate(space_dims)])
 
-    def grad(self, shift=None, order=None, method='FD', **kwargs):
+    def grad(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Gradient of the input Function.
 
@@ -404,7 +402,6 @@ def grad(self, shift=None, order=None, method='FD', **kwargs):
         shift_x0 = make_shift_x0(shift, (len(space_dims),))
         order = order or self.space_order
 
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         comps = [getattr(self, f'd{d.name}')(x0=shift_x0(shift, d, None, i),
                                              fd_order=order, method=method, side=side,

devito/types/tensor.py

@@ -220,7 +220,7 @@ def values(self):
         else:
             return super().values()
 
-    def div(self, shift=None, order=None, method='FD', **kwargs):
+    def div(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Divergence of the TensorFunction (is a VectorFunction).
 
@@ -240,7 +240,6 @@ def div(self, shift=None, order=None, method='FD', **kwargs):
         weights/w: list, tuple, or dict, optional, default=None
             Custom weights for the finite differences.
         """
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         comps = []
         func = vec_func(self)
@@ -262,7 +261,7 @@ def laplace(self):
         """
         return self.laplacian()
 
-    def laplacian(self, shift=None, order=None, method='FD', **kwargs):
+    def laplacian(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Laplacian of the TensorFunction with shifted derivatives and custom
         FD order.
@@ -287,7 +286,6 @@ def laplacian(self, shift=None, order=None, method='FD', **kwargs):
         weights/w: list, tuple, or dict, optional, default=None
             Custom weights for the finite
         """
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         comps = []
         func = vec_func(self)
@@ -366,7 +364,7 @@ def __str__(self):
 
     __repr__ = __str__
 
-    def div(self, shift=None, order=None, method='FD', **kwargs):
+    def div(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Divergence of the VectorFunction, creates the divergence Function.
 
@@ -386,7 +384,6 @@ def div(self, shift=None, order=None, method='FD', **kwargs):
         weights/w: list, tuple, or dict, optional, default=None
             Custom weights for the finite difference coefficients.
         """
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         shift_x0 = make_shift_x0(shift, (len(self.space_dimensions),))
         order = order or self.space_order
@@ -403,7 +400,7 @@ def laplace(self):
         """
         return self.laplacian()
 
-    def laplacian(self, shift=None, order=None, method='FD', **kwargs):
+    def laplacian(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Laplacian of the VectorFunction, creates the Laplacian VectorFunction.
 
@@ -423,7 +420,6 @@ def laplacian(self, shift=None, order=None, method='FD', **kwargs):
         weights/w: list, tuple, or dict, optional, default=None
             Custom weights for the finite
         """
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         func = vec_func(self)
         shift_x0 = make_shift_x0(shift, (len(self.space_dimensions),))
@@ -436,7 +432,7 @@ def laplacian(self, shift=None, order=None, method='FD', **kwargs):
                  for s in self]
         return func._new(comps)
 
-    def curl(self, shift=None, order=None, method='FD', **kwargs):
+    def curl(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Gradient of the (3D) VectorFunction, creates the curl VectorFunction.
 
@@ -459,7 +455,6 @@ def curl(self, shift=None, order=None, method='FD', **kwargs):
         if len(self.space_dimensions) != 3:
             raise AttributeError("Curl only supported for 3D VectorFunction")
         # The curl of a VectorFunction is a VectorFunction
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         dims = self.root_dimensions
         derivs = ['d%s' % d.name for d in dims]
@@ -486,7 +481,7 @@ def curl(self, shift=None, order=None, method='FD', **kwargs):
         func = vec_func(self)
         return func._new(3, 1, [comp1, comp2, comp3])
 
-    def grad(self, shift=None, order=None, method='FD', **kwargs):
+    def grad(self, shift=None, order=None, method='FD', side=None, **kwargs):
         """
         Gradient of the VectorFunction, creates the gradient TensorFunction.
 
@@ -506,7 +501,6 @@ def grad(self, shift=None, order=None, method='FD', **kwargs):
         weights/w: list, tuple, or dict, optional, default=None
             Custom weights for the finite difference coefficients.
         """
-        side = kwargs.get("side")
         w = kwargs.get('weights', kwargs.get('w'))
         func = tens_func(self)
         ndim = len(self.space_dimensions)

tests/test_derivatives.py

@@ -625,13 +625,13 @@ def test_shifted_grad(self, shift, ndim):
                 gk = getattr(f, 'd%s' % d.name)(x0=x0, fd_order=order).evaluate
                 assert gi == gk
 
-    # TODO: Repeat all these with vector and tensor functions where defined
     @pytest.mark.parametrize('side', [left, right, centered])
     def test_grad_w_side(self, side):
         grid = Grid(shape=(11, 11))
         f = Function(name='f', grid=grid, space_order=2)
 
-        # Want to check that it's the same as constructing by hand
+        # Want to check that it's the same as constructing by hand. Note that
+        # all the subsequent tests of this flavor work in the same way.
         comps = (f.dx(side=side), f.dy(side=side))
         expr1 = VectorFunction(name=f"{f.name}_vec", space_order=f.space_order,
                                components=comps, grid=grid).evaluate
@@ -663,6 +663,21 @@ def test_div_w_side(self, side):
         assert expr1 == f.div(side=side).evaluate
         assert expr1 == div(f, side=side).evaluate
 
+    @pytest.mark.parametrize('side', [left, right, centered])
+    def test_tensor_div_w_side(self, side):
+        grid = Grid(shape=(11, 11))
+        f = TensorFunction(name='f', grid=grid, space_order=2,
+                           staggered=((None, None), (None, None)))
+
+        comps = (f[0, 0].dx(side=side) + f[0, 1].dy(side=side),
+                 f[0, 1].dx(side=side) + f[1, 1].dy(side=side))
+
+        expr1 = VectorFunction(name=f"{f.name}_vec", space_order=f.space_order,
+                               components=comps, grid=grid).evaluate
+
+        assert expr1 == f.div(side=side).evaluate
+        assert expr1 == div(f, side=side).evaluate
+
     @pytest.mark.parametrize('side', [left, right, centered])
     def test_curl_w_side(self, side):
         grid = Grid(shape=(11, 11, 11))
@@ -689,6 +704,35 @@ def test_laplace_w_side(self, side):
         assert expr1 == f.laplacian(side=side).evaluate
         assert expr1 == laplace(f, side=side).evaluate
 
+    @pytest.mark.parametrize('side', [left, right, centered])
+    def test_vector_laplace_w_side(self, side):
+        grid = Grid(shape=(11, 11))
+        f = VectorFunction(name='f', grid=grid, space_order=2, staggered=(None, None))
+
+        comps = (f[0].dx2(side=side) + f[0].dy2(side=side),
+                 f[1].dx2(side=side) + f[1].dy2(side=side))
+
+        expr1 = VectorFunction(name=f"{f.name}_vec", space_order=f.space_order,
+                               components=comps, grid=grid).evaluate
+
+        assert expr1 == f.laplacian(side=side).evaluate
+        assert expr1 == laplace(f, side=side).evaluate
+
+    @pytest.mark.parametrize('side', [centered])
+    def test_tensor_laplace_w_side(self, side):
+        grid = Grid(shape=(11, 11))
+        f = TensorFunction(name='f', grid=grid, space_order=2,
+                           staggered=((None, None), (None, None)))
+
+        comps = (f[0, 0].dx2(side=side) + f[0, 1].dy2(side=side),
+                 f[0, 1].dx2(side=side) + f[1, 1].dy2(side=side))
+
+        expr1 = VectorFunction(name=f"{f.name}_vec", space_order=f.space_order,
+                               components=comps, grid=grid).evaluate
+
+        assert expr1 == f.laplacian(side=side).evaluate
+        assert expr1 == laplace(f, side=side).evaluate
+
     def test_substitution(self):
         grid = Grid((11, 11))
         f = Function(name="f", grid=grid, space_order=4)