only supply relevant kwargs to OneFormAssembler

firedrake/assemble.py

@@ -143,18 +143,24 @@ def get_assembler(form, *args, **kwargs):
 
     """
     is_base_form_preprocessed = kwargs.pop('is_base_form_preprocessed', False)
+    fc_params = kwargs.get('form_compiler_parameters', None)
     if isinstance(form, ufl.form.BaseForm) and not is_base_form_preprocessed:
         mat_type = kwargs.get('mat_type', None)
-        fc_params = kwargs.get('form_compiler_parameters', None)
         # Preprocess the DAG and restructure the DAG
         # Only pre-process `form` once beforehand to avoid pre-processing for each assembly call
         form = BaseFormAssembler.preprocess_base_form(form, mat_type=mat_type, form_compiler_parameters=fc_params)
     if isinstance(form, (ufl.form.Form, slate.TensorBase)) and not BaseFormAssembler.base_form_operands(form):
         diagonal = kwargs.pop('diagonal', False)
         if len(form.arguments()) == 0:
-            return ZeroFormAssembler(form, **kwargs)
+            return ZeroFormAssembler(form, form_compiler_parameters=fc_params)
         elif len(form.arguments()) == 1 or diagonal:
-            return OneFormAssembler(form, *args, diagonal=diagonal, **kwargs)
+            return OneFormAssembler(form, *args,
+                                    bcs=kwargs.get("bcs", None),
+                                    form_compiler_parameters=fc_params,
+                                    needs_zeroing=kwargs.get("needs_zeroing", True),
+                                    zero_bc_nodes=kwargs.get("zero_bc_nodes", True),
+                                    diagonal=diagonal,
+                                    weight=kwargs.get("weight", 1.0))
         elif len(form.arguments()) == 2:
             return TwoFormAssembler(form, *args, **kwargs)
         else:
@@ -1192,7 +1198,7 @@ def _apply_dirichlet_bc(self, tensor, bc):
         if self._diagonal:
             bc.set(tensor, self._weight)
         elif not self._zero_bc_nodes:
-            # NOTE this will only work if tensor is a Function and not a Cofunction
+            # NOTE this only works if tensor is a Function and not a Cofunction
             bc.apply(tensor)
         else:
             bc.zero(tensor)