Merge pull request #2185 from devitocodes/custom-fd-v3

api: fix symbolic coeffs for cross derivatives

devito/finite_differences/coefficients.py

@@ -1,8 +1,8 @@
-import sympy
 import numpy as np
 from cached_property import cached_property
 
 from devito.finite_differences import generate_indices
+from devito.finite_differences.tools import numeric_weights, symbolic_weights
 from devito.tools import filter_ordered, as_tuple
 
 __all__ = ['Coefficient', 'Substitutions', 'default_rules']
@@ -245,15 +245,25 @@ def generate_subs(deriv_order, function, index):
         subs = {}
 
         mapper = {dim: index}
+        # Get full range of indices and weights
+        indices, x0 = generate_indices(function, dim,
+                                       fd_order, side=None, x0=mapper)
+        sweights = symbolic_weights(function, deriv_order, indices, x0)
+
+        # Actual FD used indices and weights
+        if deriv_order == 1 and fd_order == 2:
+            fd_order = 1
 
         indices, x0 = generate_indices(function, dim,
                                        fd_order, side=None, x0=mapper)
 
-        coeffs = sympy.finite_diff_weights(deriv_order, indices, x0)[-1][-1]
+        coeffs = numeric_weights(deriv_order, indices, x0)
+
+        for (c, i) in zip(coeffs, indices):
+            subs.update({function._coeff_symbol(i, deriv_order, function, index): c})
 
-        for j in range(len(coeffs)):
-            subs.update({function._coeff_symbol
-                        (indices[j], deriv_order, function, index): coeffs[j]})
+        # Set all unused weights to zero
+        subs.update({w: 0 for w in sweights if w not in subs})
 
         return subs
 

devito/finite_differences/differentiable.py

@@ -728,6 +728,9 @@ def _new_rawargs(self, *args, **kwargs):
         kwargs.pop('is_commutative', None)
         return self.func(*args, **kwargs)
 
+    def _coeff_symbol(self, *args, **kwargs):
+        return self.base._coeff_symbol(*args, **kwargs)
+
 
 class diffify(object):
 

devito/finite_differences/tools.py

@@ -60,7 +60,7 @@ def check_symbolic(func):
     def wrapper(expr, *args, **kwargs):
         if expr._uses_symbolic_coefficients:
             expr_dict = expr.as_coefficients_dict()
-            if any(len(expr_dict) > 1 for item in expr_dict):
+            if any(v > 1 for k, v in expr_dict.items()):
                 raise NotImplementedError("Applying the chain rule to functions "
                                           "with symbolic coefficients is not currently "
                                           "supported")
@@ -337,6 +337,7 @@ def generate_indices_staggered(expr, dim, order, side=None, x0=None):
         ind0 = expr.indices_ref[dim]
     except AttributeError:
         ind0 = start
+
     if start != ind0:
         if order < 2:
             indices = [start - diff/2, start + diff/2]

tests/test_symbolic_coefficients.py

@@ -32,9 +32,12 @@ def test_default_rules(self, order, stagger):
                           staggered=staggered)
         u1 = TimeFunction(name='u', grid=grid, time_order=order, space_order=order,
                           staggered=staggered, coefficients='symbolic')
-        eq0 = Eq(-u0.dx+u0.dt)
+
+        eq0 = Eq(u0.dt-u0.dx)
         eq1 = Eq(u1.dt-u1.dx)
-        assert(eq0.evalf(_PRECISION).__repr__() == eq1.evalf(_PRECISION).__repr__())
+
+        assert(eq0.evaluate.evalf(_PRECISION).__repr__() ==
+               eq1.evaluate.evalf(_PRECISION).__repr__())
 
     @pytest.mark.parametrize('expr, sorder, dorder, dim, weights, expected', [
         ('u.dx', 2, 1, 0, (-0.6, 0.1, 0.6),
@@ -374,3 +377,15 @@ def test_aggregate_w_custom_coeffs(self):
         assert aggregated.args[1] == q.dx2
 
         Operator([Eq(q.forward, expr)])(time_M=2)  # noqa
+
+    def test_cross_derivs(self):
+        grid = Grid(shape=(11, 11, 11))
+        q = TimeFunction(name='q', grid=grid, space_order=8, time_order=2,
+                         coefficients='symbolic')
+        q0 = TimeFunction(name='q', grid=grid, space_order=8, time_order=2)
+
+        eq0 = Eq(q0.forward, q0.dx.dy)
+        eq1 = Eq(q.forward, q.dx.dy)
+
+        assert(eq0.evaluate.evalf(_PRECISION).__repr__() ==
+               eq1.evaluate.evalf(_PRECISION).__repr__())