From 7b8174c430ff45b977df030f1c365be0d72085e3 Mon Sep 17 00:00:00 2001
From: Andreas Kloeckner <inform@tiker.net>
Date: Tue, 14 Nov 2023 09:57:35 -0600
Subject: [PATCH] TensorProductBasis: drop orth_weight argument

---
 modepy/modes.py | 36 ++++++++----------------------------
 1 file changed, 8 insertions(+), 28 deletions(-)

diff --git a/modepy/modes.py b/modepy/modes.py
index 55cd1c4..b755dc8 100644
--- a/modepy/modes.py
+++ b/modepy/modes.py
@@ -126,8 +126,6 @@ def _cse(expr, prefix):
     else:
         return expr
 
-    return expr
-
 
 def _where(op_a, comp, op_b, then, else_):
     from pymbolic.primitives import Comparison, Expression, If
@@ -940,6 +938,9 @@ def orthonormality_weight(self) -> float:
         """
         :raises: :exc:`BasisNotOrthonormal` if the basis does not have
             a weight, i.e. it is not orthogonal.
+            For now, only scalar orthonormality weights are supported.
+            In the future, this may become a symbolic expression involving
+            symbols ``"rstuvw"``.
         """
 
     @property
@@ -1115,28 +1116,23 @@ class TensorProductBasis(Basis):
 
     def __init__(self,
             bases: Sequence[Basis],
-            orth_weight: Optional[float],
             dims_per_basis: Optional[Tuple[int, ...]] = None) -> None:
         """
         :param bases: a sequence of 1D bases used to construct the tensor
             product approximation basis.
-        :param orth_weight: if *bases* forms an orthogonal basis, this should
-            be the normalizing weight. If *None*, then the basis is assumed to
-            not be orthogonal (this is not checked).
         """
 
         if dims_per_basis is None:
             dims_per_basis = (1,) * len(bases)
 
         self._bases = tuple(bases)
-        self._orth_weight = orth_weight
         self._dims_per_basis = tuple(dims_per_basis)
 
-    def orthonormality_weight(self):
-        if self._orth_weight is None:
-            raise BasisNotOrthonormal
-        else:
-            return self._orth_weight
+    def orthonormality_weight(self) -> float:
+        orth_weight: float = 1.0
+        for b in self._bases:
+            orth_weight *= b.orthonormality_weight()
+        return orth_weight
 
     @property
     def bases(self) -> Sequence[Basis]:
@@ -1216,19 +1212,6 @@ def gradients(self):
                 for mid in self._mode_index_tuples)
 
 
-def _get_orth_weight(bases: Sequence[Basis]) -> Optional[float]:
-    orth_weight: Optional[float] = 1.0
-    for b in bases:
-        try:
-            assert orth_weight is not None
-            orth_weight *= b.orthonormality_weight()
-        except BasisNotOrthonormal:
-            orth_weight = None
-            break
-
-    return orth_weight
-
-
 @orthonormal_basis_for_space.register(TensorProductSpace)
 def _orthonormal_basis_for_tp(
         space: TensorProductSpace,
@@ -1244,7 +1227,6 @@ def _orthonormal_basis_for_tp(
 
     return TensorProductBasis(
             bases,
-            orth_weight=_get_orth_weight(bases),
             dims_per_basis=tuple([b.spatial_dim for b in space.bases]))
 
 
@@ -1259,7 +1241,6 @@ def _basis_for_tp(space: TensorProductSpace, shape: TensorProductShape):
     bases = [basis_for_space(b, s) for b, s in zip(space.bases, shape.bases)]
     return TensorProductBasis(
             bases,
-            orth_weight=_get_orth_weight(bases),
             dims_per_basis=tuple([b.spatial_dim for b in space.bases]))
 
 
@@ -1274,7 +1255,6 @@ def _monomial_basis_for_tp(space: TensorProductSpace, shape: TensorProductShape)
 
     return TensorProductBasis(
             bases,
-            orth_weight=None,
             dims_per_basis=tuple([b.spatial_dim for b in space.bases]))
 
 # }}}