feat: ScalarFunction ± Number adds the constant function - #18
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A scalar added to a function means "add the constant function of that value", projected onto the basis: f + c == f + dg_function(dg, fill(c, n)). Only functions absorb a scalar this way — the other tensors have no canonical constant element, so +/- against a Number stays a MethodError there, matching upstream. Flips the `function plus scalar` @test_broken in the ported Python suite. Closes #11
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Closes #11.
Python supports
f + 5,5 + f,f - 5,5 - fon aFunction, meaning "add the constant function". Julia defined*and/against aNumberbut not+/-, so all four raisedMethodError.What changed
Four methods in
src/tensors/base_tensor/base_tensor.jl, plus a private_constant_function(f, c)helper that buildsdg_function(dg, fill(c, npoints(dg)))— the semantics the issue pins. Subtraction reuses the existing tensor arithmetic (f - cisf + (-c),c - fis(-f) + c), and batching falls out for free: tensor+already right-aligns and broadcasts the unbatched constant across batch rows.The methods dispatch on
ScalarFunction, notAbstractTensor, soVectorField/Form/Tensor02plus a scalar still raiseMethodError— the behaviour upstream deliberately keeps. The ported suite now asserts that in both operand orders.Verification
Checked against a real geometry before the suite: the coefficient difference lands entirely in φ₀ at
c * sqrt(sum(measure(dg)))(residual ~1e-14 elsewhere), and pointwise values shift by exactlyc.The
function plus scalar@test_brokenintest/test_pyclasses.jlflips to real assertions — value checks against the explicit constant function, the upstream φ₀ check, and all four operand orders in the batched case. Full suite passes at 2325 tests; the 4 remaining@test_brokenare the separate per-batch-vector-scaling gap, untouched here.