gam_terms/basis/

duchon_thinplate.rs

use super::*;

use super::invariant_tie_break::resolve_sorted_profile_tie;
use gam_linalg::lanczos::{SymmetricExtremeLanczosOptions, symmetric_extreme_lanczos_eigenpairs};

/// Cross-disease Duchon basis cache.
///
/// The biobank workload fits many models (e.g. 17 diseases) over the SAME base
/// cohort: identical individuals, identical predictor columns (PC1..PC15, sex,
/// ages, geography); only the response/PRS column changes per fit. The Duchon
/// spatial basis — center/knot selection, the thin-plate kernel evaluation, the
/// kernel-constraint nullspace reparameterisation, the identifiability
/// transform, and the penalty Grams — is a PURE FUNCTION of `(data, spec)`: it
/// never reads the response. Its dense-versus-lazy REPRESENTATION additionally
/// depends on the workspace's storage-routing policy — but that is a property
/// of how the same basis is carried, so it is checked against the cached entry
/// on lookup rather than folded into the key (see [`route_matches_policy`]).
/// Thus diseases sharing the same columns can reuse the complete
/// [`BasisBuildResult`] without crossing a caller's materialization boundary.
///
/// This is a content-addressed, size-bounded, recomputable memo mirroring the
/// FFI cross-disease column-encode cache (`encoded_column_cache` in
/// `crates/gam-pyffi/src/manifold_and_posterior_ffi.rs`): the key is a 128-bit
/// fingerprint of the data matrix CONTENT (shape + every element bit-pattern),
/// the basis spec, and the caller's declared storage MODE. A different cohort
/// or spec therefore MISSES; matching diseases HIT. A hit clones the cached `BasisBuildResult` (cheap
/// `Arc`/ndarray clones vs. the kernel build + RRQR audit), so results are
/// bit-identical to the miss path.
/// Eviction (LRU under a byte budget) only ever forfeits the perf benefit, never
/// correctness, since every value is exactly recomputable from its key.
type DuchonBasisCacheKey = (u64, u64);

#[derive(Clone)]
struct CachedDuchonBasis {
    result: BasisBuildResult,
    /// The storage route the *building* policy selected for this realized
    /// shape — `true` for the streamed/operator design, `false` for the
    /// materialized one.
    ///
    /// This is how the memory policy participates in the memo WITHOUT
    /// participating in the memo's KEY. The key is `(data, spec)`: it names
    /// WHICH basis this is, and that is a question memory has no vote in. The
    /// route names HOW that basis is carried, and a hit is served only when the
    /// asking policy would pick the same route for the same shape
    /// ([`route_matches_policy`]). Hashing the cap into the key instead — which
    /// is what shipped before #2684 — spelled a routing preference as a
    /// difference of identity, so two processes that would have built the very
    /// same basis missed each other over a byte count neither of them chose.
    route_lazy: bool,
}

impl gam_runtime::resource::ResidentBytes for CachedDuchonBasis {
    fn resident_bytes(&self) -> usize {
        // Coarse charge: the dominant resident cost is the dense design columns
        // and the penalty Grams. An estimate suffices — the byte budget only
        // bounds the cache, it never affects correctness.
        let design_bytes = self
            .result
            .design
            .nrows()
            .saturating_mul(self.result.design.ncols())
            .saturating_mul(std::mem::size_of::<f64>());
        let penalty_bytes: usize = self
            .result
            .active_penalties
            .iter()
            .map(|penalty| {
                penalty
                    .matrix
                    .len()
                    .saturating_mul(std::mem::size_of::<f64>())
            })
            .sum();
        design_bytes
            .saturating_add(penalty_bytes)
            .saturating_add(4096)
    }
}

/// Process-wide Duchon basis memo. 1 GiB matches the established large-scale
/// densification ceiling used elsewhere; with ~17 diseases over one cohort the
/// working set is a single `BasisBuildResult`, so even a modest budget retains
/// the shared basis across the whole sweep.
fn duchon_basis_cache()
-> &'static gam_runtime::resource::ByteLruCache<DuchonBasisCacheKey, CachedDuchonBasis> {
    static CACHE: std::sync::OnceLock<
        gam_runtime::resource::ByteLruCache<DuchonBasisCacheKey, CachedDuchonBasis>,
    > = std::sync::OnceLock::new();
    CACHE.get_or_init(|| gam_runtime::resource::ByteLruCache::new(1 << 30))
}

/// 128-bit content fingerprint of `(data, spec)`. Two independent hashers (one
/// unseeded, one seeded with a fixed golden-ratio constant) widen the key to
/// 128 bits so accidental collisions across a batch are negligible. The data
/// contribution hashes the shape plus EVERY element's IEEE-754 bit pattern, so
/// any change of rows, columns, or values — i.e. a different cohort / subsample
/// — produces a different key and misses. The spec is hashed via its serialized
/// form (the spec carries `serde` derives), capturing center strategy, power,
/// length scale, nullspace order, anisotropy, identifiability, and operator
/// penalty dials. The storage MODE is hashed because it is an explicit caller
/// choice — a caller that has committed to operator-only math is asking for a
/// different artifact, not for a different copy of the same one.
///
/// What is deliberately NOT hashed is the materialization CAP (#2684). A byte
/// ceiling is a statement about the machine, not about the model: two processes
/// handed the same `(data, spec)` must agree on which basis that is, whatever
/// their ceilings say. Before this change the cap was hashed here, so the
/// artifact's very identity moved with a number the caller never chose — and
/// while that cap was read from FREE memory, four processes on one node
/// computed four different fingerprints for identical inputs. The cap now
/// enters at the only place it has standing: whether a cached result's storage
/// route is the route this policy would pick ([`route_matches_policy`]).
fn duchon_basis_fingerprint(
    data: ArrayView2<'_, f64>,
    spec: &DuchonBasisSpec,
    policy: &gam_runtime::resource::ResourcePolicy,
) -> Option<DuchonBasisCacheKey> {
    let spec_bytes = serde_json::to_vec(spec).ok()?;
    let mut lo = DefaultHasher::new();
    let mut hi = DefaultHasher::new();
    // Seed `hi` so its stream is statistically independent of `lo`.
    0x9E37_79B9_7F4A_7C15u64.hash(&mut hi);

    let (nrows, ncols) = data.dim();
    for h in [&mut lo, &mut hi] {
        nrows.hash(h);
        ncols.hash(h);
    }
    // Hash element bit-patterns in a fixed (row-major) order, independent of the
    // view's underlying memory layout, so two views over the same logical matrix
    // fingerprint identically.
    for row in data.rows() {
        for &v in row {
            let bits = v.to_bits();
            bits.hash(&mut lo);
            bits.hash(&mut hi);
        }
    }
    for h in [&mut lo, &mut hi] {
        spec_bytes.len().hash(h);
        spec_bytes.hash(h);
        let storage_mode = match policy.derivative_storage_mode {
            gam_runtime::resource::DerivativeStorageMode::AnalyticOperatorRequired => 0_u8,
            gam_runtime::resource::DerivativeStorageMode::MaterializeIfSmall => 1_u8,
            gam_runtime::resource::DerivativeStorageMode::DiagnosticsOnly => 2_u8,
        };
        storage_mode.hash(h);
    }
    Some((lo.finish(), hi.finish()))
}

/// The storage route `policy` selects for a design of this realized shape.
///
/// Evaluated on the FINAL design rather than on the pre-identifiability width
/// the builder routed on, and evaluated by the same function at insert and at
/// lookup. That is what makes the comparison in [`route_matches_policy`] exact
/// and idempotent: whatever a build produces, storing `f(shape, building
/// policy)` means a later lookup under that same policy recomputes the same
/// answer and hits. A predicate that could disagree with itself on its own
/// output would turn every lookup in the disagreement band into a silent
/// permanent cache miss.
fn realized_route_is_lazy(
    result: &BasisBuildResult,
    policy: &gam_runtime::resource::ResourcePolicy,
) -> bool {
    should_use_lazy_spatial_design(result.design.nrows(), result.design.ncols(), policy)
}

/// Whether a cached basis may be served to a caller holding `policy`.
///
/// The question is NOT "is this policy as permissive as the one that built it"
/// — that would make the answer depend on arrival order, so a permissive caller
/// would get a dense or a streamed design according to who ran first. It is the
/// symmetric one: do the two policies route this shape the same way? If they
/// do, the cached artifact is the artifact this caller would have built. If
/// they do not, the caller wanted a differently-carried copy of the same basis
/// and gets one built for it.
fn route_matches_policy(
    cached: &CachedDuchonBasis,
    policy: &gam_runtime::resource::ResourcePolicy,
) -> bool {
    realized_route_is_lazy(&cached.result, policy) == cached.route_lazy
}

pub fn build_duchon_basiswithworkspace(
    data: ArrayView2<'_, f64>,
    spec: &DuchonBasisSpec,
    workspace: &mut BasisWorkspace,
) -> Result<BasisBuildResult, BasisError> {
    if let Some(key) = duchon_basis_fingerprint(data, spec, workspace.policy()) {
        if let Some(hit) = duchon_basis_cache().get(&key) {
            if route_matches_policy(&hit, workspace.policy()) {
                return Ok(hit.result);
            }
        }
        let result = build_duchon_basis_uncached(data, spec, workspace)?;
        let route_lazy = realized_route_is_lazy(&result, workspace.policy());
        duchon_basis_cache().insert(
            key,
            CachedDuchonBasis {
                result: result.clone(),
                route_lazy,
            },
        );
        return Ok(result);
    }
    build_duchon_basis_uncached(data, spec, workspace)
}

/// Build a Duchon design whose COLUMN SPACE is a property of the spec alone
/// (gam#237).
///
/// [`build_duchon_basis`] adopts a *data-metric* radial chart when none is
/// frozen: it forms `G_c = (K·Z)ᵀ(K·Z)` from the realized design and keeps only
/// the `G_c` eigen-directions above a numerical floor — "design columns with no
/// realized data support", as the whitening step's own comment puts it. `G_c`
/// has rank at most `n`, so the surviving width is `min(K−p, n) + p`. For a FIT
/// that is a deliberate rank reduction and it is safe, because the fit freezes
/// the chart into basis metadata and replays it at predict time. For a
/// basis-evaluation primitive, with no fit and nothing to freeze, it means the
/// design's width is a function of the frame it is handed. Measured, one spec
/// (12 centers, `d=2`, `m=2`), varying only the evaluation row count:
///
/// ```text
///   before:  1 row -> 4 cols,  5 rows -> 8,  9 rows -> 12,  30 rows -> 12
///   after :  1 row -> 12 cols, 5 rows -> 12, 9 rows -> 12,  30 rows -> 12
/// ```
///
/// A basis that changes dimension with the number of points you evaluate it at
/// cannot be applied twice consistently, and its `basis_size` is unknowable
/// without the data. This entry point instead derives the chart from the
/// CENTERS — the same `Ω_c` bending eigenbasis
/// `thin_plate_radial_reparam_data_metric` already falls back to when the
/// realized Gram is degenerate — and freezes it into the spec before building.
/// The width is then `K` exactly: measured across 63 configurations
/// (`d ∈ {2,3,4}` × `m ∈ {1,2,3}` × `K ∈ {6..12}`), 63 of 63 emit `cols == K`.
/// It is `K` rather than something smaller because `Ω_c` is the bending energy
/// on the ALREADY constrained kernel block, whose polynomial null space has been
/// projected out, so all `K−p` modes carry genuine curvature and none falls
/// below the `K·ε·λ_max` roundoff floor.
///
/// Callers that ARE fitting should keep using [`build_duchon_basis`]: the
/// data-metric chart is what removes the REML over-smoothing collapse (#1355),
/// and it is legitimate there precisely because a fit persists it.
pub fn build_duchon_basis_spec_chart(
    data: ArrayView2<'_, f64>,
    spec: &DuchonBasisSpec,
) -> Result<BasisBuildResult, BasisError> {
    // An explicitly frozen chart already makes the basis spec-determined, and
    // the periodic/cyclic builders never reach the data-metric branch at all,
    // so in both cases the ordinary path is already frame-independent.
    if center_strategy_spectral_basis(&spec.center_strategy).is_some()
        || spec.radial_reparam.is_some()
        || spec.periodic.is_some()
        || spec.boundary.period().is_some()
    {
        return build_duchon_basis(data, spec);
    }
    let mut workspace = BasisWorkspace::default();
    let centers = select_centers_by_strategy(data, &spec.center_strategy)?;
    let effective_nullspace_order =
        duchon_effective_nullspace_order(centers.view(), spec.nullspace_order);
    let aniso = auto_seed_aniso_contrasts(centers.view(), spec.aniso_log_scales.as_deref());
    let kernel_transform = kernel_constraint_nullspace(
        centers.view(),
        effective_nullspace_order,
        &mut workspace.cache,
    )?;
    if kernel_transform.ncols() == 0 {
        return build_duchon_basis(data, spec);
    }
    let omega_constrained = duchon_constrained_bending_penalty(
        centers.view(),
        spec.length_scale,
        spec.power,
        effective_nullspace_order,
        aniso.as_deref(),
        &kernel_transform,
    )?;
    let (v, _mu) = thin_plate_radial_reparam_from_constrained_penalty(&omega_constrained)?;
    if v.ncols() == 0 {
        // A degenerate chart would gut the basis; the unrotated design is a
        // better answer than an empty one, and it is still frame-independent
        // because nothing data-derived went into it.
        return build_duchon_basis(data, spec);
    }
    let mut spec_chart = spec.clone();
    spec_chart.radial_reparam = Some(v);
    build_duchon_basis(data, &spec_chart)
}

/// Dominant center-kernel eigenspace followed by the exact polynomial
/// side-condition projection used by Duchon regression splines.
///
/// The center count controls Nyström resolution; `rank` independently controls
/// the final spline width. This is the construction mgcv calls a low-rank
/// Duchon spline: retain the `rank` eigenpairs of largest magnitude, then remove
/// the polynomial component *inside that eigenspace*. Projecting the full
/// center space first and truncating afterward is a different approximation.
struct DuchonSpectralKernelChart {
    kernel_transform: Array2<f64>,
    bending_penalty: Array2<f64>,
}

fn duchon_spectral_kernel_chart(
    centers: ArrayView2<'_, f64>,
    length_scale: Option<f64>,
    power: f64,
    nullspace_order: DuchonNullspaceOrder,
    aniso_log_scales: Option<&[f64]>,
    rank: usize,
) -> Result<DuchonSpectralKernelChart, BasisError> {
    let (center_kernel, kernel_amp) = duchon_center_kernel_value_matrix(
        centers,
        length_scale,
        power,
        nullspace_order,
        aniso_log_scales,
    )?;
    let dim = center_kernel.nrows();

    // Match mgcv::slanczos' deliberately tiny deterministic LCG exactly. The
    // start vector selects a finite-precision Krylov chart, so using merely
    // another deterministic random sequence needlessly rotates the truncated
    // approximation away from the reference even when knots and rank match.
    let mut state = 1_u64;
    let mut start = vec![0.0_f64; dim];
    for value in &mut start {
        state = (state * 106 + 1283) % 6075;
        *value = state as f64 / 6075.0 - 0.5;
    }
    let check_every = (rank / 2).max(10).min((dim / 10).max(1));

    let pairs = symmetric_extreme_lanczos_eigenpairs(
        dim,
        &start,
        SymmetricExtremeLanczosOptions {
            target_rank: rank,
            max_steps: 128,
            check_every,
            relative_residual_tol: f64::EPSILON.sqrt(),
            breakdown_tol: 1e-14,
        },
        |q, image| gam_linalg::faer_ndarray::symmetric_matvec_into(&center_kernel, q, image),
    )
    .map_err(BasisError::InvalidInput)?;
    let selected = pairs.eigenvectors;

    let mut centers_centered = centers.to_owned();
    for axis in 0..centers.ncols() {
        let mean = centers.column(axis).sum() / centers.nrows() as f64;
        centers_centered
            .column_mut(axis)
            .mapv_inplace(|value| value - mean);
    }
    let polynomial = polynomial_block_from_order(centers_centered.view(), nullspace_order);
    let polynomial_in_eigenspace = fast_atb(&selected, &polynomial);
    let spectral_constraint =
        kernel_constraint_nullspace_from_matrix(polynomial_in_eigenspace.view())?;
    let kernel_transform = fast_ab(&selected, &spectral_constraint);
    let mut diagonal = Array2::<f64>::zeros((rank, rank));
    for (index, &eigenvalue) in pairs.eigenvalues.iter().enumerate() {
        diagonal[[index, index]] = eigenvalue;
    }
    let reduced = fast_ab(
        &fast_atb(&spectral_constraint, &diagonal),
        &spectral_constraint,
    )
    .mapv(|value| value * kernel_amp * kernel_amp);
    Ok(DuchonSpectralKernelChart {
        kernel_transform,
        bending_penalty: symmetrize_penalty(&reduced),
    })
}

#[cfg(test)]
mod duchon_spectral_basis_tests {
    use super::*;

    fn asymmetric_centers() -> Array2<f64> {
        Array2::from_shape_fn((24, 4), |(row, axis)| {
            let x = (row + 1) as f64;
            let a = (axis + 2) as f64;
            (x * a.sqrt()).sin() + (x / (a + 0.5)).cos() + 0.01 * x * a
        })
    }

    #[test]
    fn spectral_transform_has_requested_rank_is_deterministic_and_obeys_side_condition() {
        let centers = asymmetric_centers();
        let rank = 6;
        let chart = duchon_spectral_kernel_chart(
            centers.view(),
            None,
            1.5,
            DuchonNullspaceOrder::Zero,
            None,
            rank,
        )
        .expect("small asymmetric cloud has a certifiable spectral basis");
        let replay = duchon_spectral_kernel_chart(
            centers.view(),
            None,
            1.5,
            DuchonNullspaceOrder::Zero,
            None,
            rank,
        )
        .expect("deterministic replay");

        // rank includes the single constant null-space column.
        assert_eq!(chart.kernel_transform.dim(), (centers.nrows(), rank - 1));
        assert_eq!(chart.kernel_transform, replay.kernel_transform);
        assert_eq!(chart.bending_penalty, replay.bending_penalty);

        let gram = fast_atb(&chart.kernel_transform, &chart.kernel_transform);
        for i in 0..gram.nrows() {
            for j in 0..gram.ncols() {
                let target = if i == j { 1.0 } else { 0.0 };
                assert!(
                    (gram[[i, j]] - target).abs() <= 2e-10,
                    "spectral transform is not orthonormal at ({i}, {j}): {}",
                    gram[[i, j]]
                );
            }
        }
        for column in chart.kernel_transform.columns() {
            assert!(
                column.sum().abs() <= 2e-10,
                "constant polynomial side condition was not removed: sum={}",
                column.sum()
            );
        }
    }
}

fn build_duchon_basis_uncached(
    data: ArrayView2<'_, f64>,
    spec: &DuchonBasisSpec,
    workspace: &mut BasisWorkspace,
) -> Result<BasisBuildResult, BasisError> {
    if let Some((_start, _end, period)) = spec.boundary.period() {
        // A 1-D cyclic boundary is the formula-DSL spelling of periodicity.
        // Normalize it onto `spec.periodic` so ALL periodic 1-D Duchon terms
        // share the single Bernoulli Green's-function construction
        // (`build_periodic_duchon_basis_1d`): the exact periodic kernel of
        // `(d²/dx²)^m` with the exact RKHS Gram penalty `ω = zᵀK_centers z`.
        // The former wrapped-min-distance kernel + coefficient-difference
        // penalty path was both a SPEC 5 violation (penalty on coefficients,
        // not the function) and a live forward/derivative desync: every
        // derivative/jet consumer (`create_duchon_basis_1d_derivative_dense`,
        // the log-κ derivative builders, the pyffi periodic jet) reconstructs
        // the Bernoulli design, never the wrapped-distance one. Only the
        // period LENGTH matters — the periodic kernel depends on cyclic
        // distance mod period, so the boundary's absolute phase anchor is
        // immaterial (the wrap anchor is re-derived deterministically from
        // the frozen center set at fit and predict time alike).
        if data.ncols() != 1 {
            crate::bail_invalid_basis!(
                "cyclic-boundary Duchon smooths require exactly one covariate"
            );
        }
        let mut spec_periodic = spec.clone();
        spec_periodic.boundary = crate::basis::OneDimensionalBoundary::Open;
        spec_periodic.periodic = Some(vec![Some(period)]);
        let centers = select_centers_by_strategy(data, &spec_periodic.center_strategy)?;
        assert_spatial_centers_below_large_scale_cap(data.ncols(), centers.view())?;
        return build_periodic_duchon_basis_1d(data, &spec_periodic, centers, workspace);
    }
    let centers = select_centers_by_strategy(data, &spec.center_strategy)?;
    assert_spatial_centers_below_large_scale_cap(data.ncols(), centers.view())?;
    if let Some(periodic) = spec.periodic.as_ref() {
        if periodic.len() != data.ncols() {
            crate::bail_invalid_basis!(
                "periodic must have length d={}, got {}",
                data.ncols(),
                periodic.len()
            );
        }
        if data.ncols() > 1 && periodic.iter().any(Option::is_some) {
            let flags = periodic.iter().map(Option::is_some).collect::<Vec<_>>();
            let periods = periodic
                .iter()
                .map(|axis| axis.unwrap_or(1.0))
                .collect::<Vec<_>>();
            return build_duchon_basis_mixed_periodicity_auto(data, spec, &flags, Some(&periods));
        }
        return build_periodic_duchon_basis_1d(data, spec, centers, workspace);
    }
    // `spec.power` is the LITERAL Duchon spectral power `s` at the basis layer.
    // The kernel exponent is `2(p+s) − d`, so `power = 0` means `s = 0` — the
    // integer-order Duchon kernel `r^{2(p)−d}` (its `r²·log r` log case in even
    // `d`, which equals the thin-plate kernel) — and is honored verbatim, NOT
    // read as "apply a default". The magic cubic default (no explicit power ⇒
    // `s = (d−1)/2`, `φ(r)=r³`) is a REQUEST-LAYER choice the formula/CLI/pyffi
    // front-ends resolve via `duchon_cubic_default`; the builder uses whatever
    // `(nullspace_order, power)` it is handed, so both Duchon spectral powers —
    // `s = 0` (thin-plate kernel) and `s = (d−1)/2` (fractional cubic) — are
    // reachable through this one construction.
    //
    // Auto-degrade the requested null-space order to Zero when the selected
    // centers cannot span the requested polynomial block. Every downstream
    // consumer of `spec.nullspace_order` in this function MUST use the
    // effective order, otherwise the penalty/nullspace is built with a
    // different order than the basis.
    let effective_nullspace_order =
        duchon_effective_nullspace_order(centers.view(), spec.nullspace_order);
    let p_order = duchon_p_from_nullspace_order(effective_nullspace_order);
    // Initialize anisotropy contrasts from knot cloud geometry when the caller
    // enabled scale-dimensions but left η at the zero default. Duchon η is a
    // FIXED, geometry-derived basis parameter (never a REML hyper-axis), so the
    // all-zero auto-seed sentinel is the intended seeding mechanism here — unlike
    // the Matérn forward path, whose η is optimized and must be honored literally.
    let aniso = auto_seed_aniso_contrasts(centers.view(), spec.aniso_log_scales.as_deref());
    // The native reproducing-norm Gram penalty (`Primary`) is assembled from
    // kernel VALUES at the center pairs (K_CC), not from collocated D1/D2
    // derivative operators, so the build only requires the pointwise kernel to
    // EXIST (`2(p+s) > d`). The stricter operator-collocation orders
    // (`2(p+s) > d+1` / `> d+2`) are a property of the old triple-operator
    // penalties that this path no longer builds; enforcing them here would
    // spuriously reject valid kernels — e.g. the `s=0` thin-plate `r²·log r`
    // (`2(p+s)=d+2` in 2D), which the native Gram handles fine.
    //
    // Validate against the spectral power the kernel actually evaluates. The
    // scale-free native Gram (`length_scale=None`) uses the literal fractional
    // `spec.power`. The hybrid Matérn-blended kernel (`length_scale=Some`) is
    // built from the integer partial-fraction expansion of `(κ²+‖w‖²)^s` and
    // reads `s` back through `power_as_usize` (a fractional `spec.power` is
    // truncated to that integer). Validating the raw fractional power on the
    // hybrid path desyncs the `2(p+s) > d` well-posedness gate from the realized
    // kernel: e.g. the cubic default `s=(d-1)/2=1.5` at p=2, d=4 truncates to
    // s=0 where `2(p+s)=4=d` is NOT finite at the origin, yet `spec.power=1.5`
    // passes the gate — the resulting non-finite Gram crashes the constraint
    // eigendecomposition (gh#750). Gate on the truncated integer for hybrid so
    // that case is rejected here with a clear message while every valid hybrid
    // config (e.g. 1D, where `2(2+0)=4>1` stays finite) still builds.
    let validation_power = if spec.length_scale.is_some() {
        spec.power_as_usize() as f64
    } else {
        spec.power
    };
    validate_duchon_kernel_orders(spec.length_scale, p_order, validation_power, data.ncols())?;
    let poly_cols = polynomial_block_from_order(data, effective_nullspace_order).ncols();
    let spectral_basis = center_strategy_spectral_basis(&spec.center_strategy);
    if let Some(spectral) = spectral_basis {
        let rank = spectral.rank();
        if rank <= poly_cols || rank > centers.nrows() {
            crate::bail_invalid_basis!(
                "Duchon spectral rank must satisfy polynomial_columns < rank <= centers: \
                 polynomial_columns={poly_cols}, rank={rank}, centers={}",
                centers.nrows()
            );
        }
        if spec.radial_reparam.is_some() {
            crate::bail_invalid_basis!(
                "Duchon spectral basis and landmark data-metric radial reparameterization \
                 are mutually exclusive"
            );
        }
        if spec.length_scale.is_some() {
            crate::bail_invalid_basis!(
                "Duchon spectral reduction currently requires the scale-free kernel; \
                 a moving hybrid range would change the retained eigenspace"
            );
        }
    }
    let mut realized_spectral_basis = None;
    let mut spectral_bending_penalty = None;
    let mut kernel_transform = if let Some(spectral) = spectral_basis {
        let rank = spectral.rank();
        let chart = match (spectral.kernel_transform(), spectral.bending_penalty()) {
            (Some(frozen), Some(frozen_penalty)) => {
                if frozen.nrows() != centers.nrows()
                    || frozen.ncols() != rank.saturating_sub(poly_cols)
                {
                    crate::bail_dim_basis!(
                        "Duchon frozen spectral transform has shape {:?}; expected ({}, {})",
                        frozen.dim(),
                        centers.nrows(),
                        rank.saturating_sub(poly_cols)
                    );
                }
                if frozen_penalty.dim() != (frozen.ncols(), frozen.ncols()) {
                    crate::bail_dim_basis!(
                        "Duchon frozen spectral bending penalty has shape {:?}; expected ({}, {})",
                        frozen_penalty.dim(),
                        frozen.ncols(),
                        frozen.ncols()
                    );
                }
                DuchonSpectralKernelChart {
                    kernel_transform: frozen.clone(),
                    bending_penalty: frozen_penalty.clone(),
                }
            }
            (None, None) => duchon_spectral_kernel_chart(
                centers.view(),
                spec.length_scale,
                spec.power,
                effective_nullspace_order,
                aniso.as_deref(),
                rank,
            )?,
            _ => crate::bail_invalid_basis!(
                "Duchon frozen spectral state must contain both transform and bending penalty"
            ),
        };
        spectral_bending_penalty = Some(chart.bending_penalty.clone());
        realized_spectral_basis = Some(DuchonSpectralBasis::Frozen {
            rank,
            kernel_transform: chart.kernel_transform.clone(),
            bending_penalty: chart.bending_penalty,
        });
        chart.kernel_transform
    } else {
        kernel_constraint_nullspace(
            centers.view(),
            effective_nullspace_order,
            &mut workspace.cache,
        )?
    };
    let base_cols = kernel_transform.ncols() + poly_cols;
    let dense_bytes = dense_design_bytes(data.nrows(), base_cols);
    let use_lazy = should_use_lazy_spatial_design(data.nrows(), base_cols, workspace.policy());
    // #1355: data-metric radial reparameterization `V`, frozen into metadata so
    // predict / κ-trial rebuilds replay the exact fit-time rotated radial basis.
    // A FROZEN `V` (predict / κ-trial / replay) is folded into the constrained
    // kernel transform on EVERY path so the design stays consistent with the
    // frozen penalty. A FRESH `V` is computed on every cold path: the dense
    // builder obtains its realized Gram from the materialized kernel block,
    // while the lazy builder streams the same Gram through the chunked operator.
    let mut frozen_radial_reparam: Option<Array2<f64>> = None;
    if let Some(v) = spec.radial_reparam.as_ref() {
        if v.nrows() != kernel_transform.ncols() {
            crate::bail_dim_basis!(
                "Duchon frozen radial reparam shape {:?} does not match constrained kernel dimension {}",
                v.dim(),
                kernel_transform.ncols()
            );
        }
        kernel_transform = fast_ab(&kernel_transform, v);
        frozen_radial_reparam = Some(v.clone());
    }
    let (design, identifiability_transform) = if use_lazy {
        // log::info! — deliberate memory-saving choice, not an anomaly.
        log::info!(
            "Duchon basis switching to lazy chunked design: n={} p={} ({:.1} MiB dense)",
            data.nrows(),
            base_cols,
            dense_bytes as f64 / (1024.0 * 1024.0),
        );
        let d = data.ncols();
        let shared_data = shared_owned_data_matrix(data, &workspace.cache);
        let p_order = duchon_p_from_nullspace_order(effective_nullspace_order);
        let s_order: f64 = spec.power;
        let length_scale = spec.length_scale;
        let s_order_int = length_scale.map(|_| duchon_power_to_usize(s_order));
        let coeffs = length_scale.map(|ls| {
            // Hybrid Matérn (length_scale = Some) uses the integer
            // partial-fraction chain; assert at this boundary so the
            // scale-free path stays fractional-clean.
            duchon_partial_fraction_coeffs(
                p_order,
                s_order_int.expect("hybrid Duchon requires integer power"),
                1.0 / ls.max(1e-300),
            )
        });
        let pure_poly_coeff = if length_scale.is_none() {
            Some(PolyharmonicBlockCoeff::new(
                pure_duchon_block_order(p_order, s_order),
                d,
            ))
        } else {
            None
        };
        // Translation-invariant polynomial frame (#1375): build the explicit
        // poly null-space columns at coordinates centered by the center-cloud
        // per-axis mean, matching `build_duchon_basis_designwithworkspace` (dense
        // path) and the side-condition `Z` (centered inside
        // `kernel_constraint_nullspace`). The kernel block reads `data − centers`
        // differences, so it is already translation-invariant and stays raw.
        let center_mean: Vec<f64> = (0..d)
            .map(|c| centers.column(c).sum() / (centers.nrows().max(1) as f64))
            .collect();
        let mut data_centered = data.to_owned();
        for c in 0..d {
            let mu = center_mean[c];
            data_centered.column_mut(c).mapv_inplace(|v| v - mu);
        }
        let poly_block =
            polynomial_block_from_order(data_centered.view(), effective_nullspace_order);
        let kernel_amp = duchon_kernel_amplification(
            centers.view(),
            length_scale,
            p_order,
            duchon_power_to_usize(s_order),
            d,
            aniso.as_deref(),
            coeffs.as_ref(),
            pure_poly_coeff.as_ref(),
        );
        // Build the same kernel evaluator for the raw-Gram pass and the final
        // operator.  The evaluator owns its anisotropic metric weights, so the
        // two streamed passes share the exact function without sharing mutable
        // state or materialising the n×p design.
        let make_kernel = || {
            let coeffs = coeffs.clone();
            let pure_poly_coeff = pure_poly_coeff;
            let metric_weights = aniso.as_ref().map(|eta| {
                eta.iter()
                    .map(|&value| (2.0 * value).exp())
                    .collect::<Vec<_>>()
            });
            Arc::new(move |data_row: &[f64], center_row: &[f64]| -> f64 {
                let r = if let Some(weights) = metric_weights.as_ref() {
                    let mut squared_radius = 0.0_f64;
                    for axis in 0..data_row.len() {
                        let delta = data_row[axis] - center_row[axis];
                        squared_radius += weights[axis] * delta * delta;
                    }
                    squared_radius.sqrt()
                } else {
                    stable_euclidean_norm((0..d).map(|axis| data_row[axis] - center_row[axis]))
                };
                let raw = if let Some(ppc) = pure_poly_coeff {
                    ppc.eval(r)
                } else {
                    duchon_matern_kernel_general_from_distance(
                        r,
                        length_scale,
                        p_order,
                        s_order_int.expect("hybrid Duchon requires integer power"),
                        d,
                        coeffs.as_ref(),
                    )
                    .expect("validated Duchon inputs should not fail")
                };
                raw * kernel_amp
            }) as Arc<dyn crate::chunked_kernel_design::SpatialKernelEvaluator>
        };
        // The data-metric radial chart is a property of the represented kernel
        // function, not of the isotropic special case.  Stream the raw
        // constrained Gram for every cold lazy build, including anisotropic and
        // operator-penalty configurations, then solve the same generalized
        // eigenproblem as the dense path.  This keeps VᵀG_cV=I and
        // VᵀΩ_cV=diag(μ) without ever allocating n×p.
        if spectral_basis.is_none() && frozen_radial_reparam.is_none() {
            let raw_gauge = Arc::new(gam_problem::Gauge::from_block_transforms(&[
                kernel_transform.clone(),
            ]));
            let raw_op = ChunkedKernelDesignOperator::new(
                shared_data.clone(),
                Arc::new(centers.clone()),
                make_kernel(),
                Some(raw_gauge),
                Some(Arc::new(poly_block.clone())),
                workspace.policy().material_policy(),
            )
            .map_err(BasisError::InvalidInput)?;
            let ones = Array1::<f64>::ones(raw_op.nrows());
            let raw_gram = raw_op.diag_xtw_x(&ones).map_err(BasisError::InvalidInput)?;
            let kernel_cols = kernel_transform.ncols();
            let design_gram =
                symmetrize_penalty(&raw_gram.slice(s![..kernel_cols, ..kernel_cols]).to_owned());
            let omega_constrained = duchon_constrained_bending_penalty(
                centers.view(),
                spec.length_scale,
                spec.power,
                effective_nullspace_order,
                aniso.as_deref(),
                &kernel_transform,
            )?;
            let (v, _mu) = thin_plate_radial_reparam_data_metric(&omega_constrained, &design_gram)?;
            if v.ncols() > 0 {
                kernel_transform = fast_ab(&kernel_transform, &v);
                frozen_radial_reparam = Some(v);
            }
        }
        let kernel_gauge = Arc::new(gam_problem::Gauge::from_block_transforms(&[
            kernel_transform.clone(),
        ]));
        let base_op = ChunkedKernelDesignOperator::new(
            shared_data,
            Arc::new(centers.clone()),
            make_kernel(),
            Some(kernel_gauge),
            Some(Arc::new(poly_block)),
            workspace.policy().material_policy(),
        )
        .map_err(BasisError::InvalidInput)?;
        let base_design = DesignMatrix::Dense(gam_linalg::matrix::DenseDesignMatrix::from(
            Arc::new(base_op),
        ));
        let identifiability_transform = spatial_identifiability_transform_from_design_matrix(
            data,
            &base_design,
            &spec.identifiability,
            "Duchon",
        )?;
        let design = if let Some(transform) = identifiability_transform.as_ref() {
            wrap_dense_design_with_transform(base_design, transform, "Duchon")?
        } else {
            base_design
        };
        (design, identifiability_transform)
    } else {
        // #1355: dense path applies the data-metric radial reparameterization
        // `V` (mirroring the thin-plate Wood-TPRS reparam) so the native
        // penalty's cliff-less Mercer spectrum is replaced by the
        // curvature-per-unit-data-variance spectrum (mgcv's cliff), removing the
        // REML over-smoothing collapse to EDF = 1. `V` is frozen at the cold
        // build and replayed verbatim from `spec.radial_reparam` on the
        // predict / κ-trial paths.
        // A FRESH `V` is computed only when no frozen reparam was supplied
        // (`frozen_radial_reparam` already folded above on the replay paths). At
        // that point `kernel_transform` is still the raw `Z`.
        //
        // The reparam is adopted for EVERY configuration, including the default
        // all-on Hilbert scale (mass+tension active). The frozen `V` is threaded
        // into the operator collocation builder (`duchon_operator_penalty_candidates`
        // → `build_duchon_collocation_operator_matriceswithworkspace`) so the
        // mass/tension blocks are assembled directly in the same `K·Z·V` frame as
        // the design and the native `Primary` penalty — no design↔penalty desync.
        // Skipping the reparam whenever operators were active (the old gate) left
        // the default Duchon on the raw cliff-less Mercer spectrum, so REML
        // over-selected EDF (a single 2-D bump fit to EDF≈30/49), which in turn
        // made the fit a knife-edge unstable to ulp-level covariate rotation and
        // unable to collapse toward the null on an irrelevant covariate. Restoring
        // the cliff for the default is what makes those recoveries hold.
        // When the fresh data-metric reparam is computed, its `raw` (un-rotated)
        // design is built here from a full `n×k` kernel evaluation. That SAME
        // realized design is the base of the final basis — rotating it by the
        // adopted `V` gives the fit-time design without a second kernel pass —
        // so carry it forward instead of rebuilding it below (#1718). This
        // halves the cold-build kernel work for explicit native-only Duchon
        // configurations (`all_disabled()`, no frozen reparam), closing their
        // wall-time gap to `thinplate(x, z)` without changing default terms.
        // The chart resolution itself lives in `duchon_resolve_radial_chart`, so
        // the ψ-derivative context resolves the IDENTICAL frame from the same
        // `(data, spec)` instead of assuming the raw `Z` (#2638).
        let mut prebuilt_raw_basis: Option<Array2<f64>> = None;
        if spectral_basis.is_none()
            && frozen_radial_reparam.is_none()
            && kernel_transform.ncols() > 0
        {
            let resolved = duchon_resolve_radial_chart(
                data,
                centers.view(),
                spec,
                effective_nullspace_order,
                aniso.as_deref(),
                &kernel_transform,
                workspace,
            )?;
            if let Some(v) = resolved.reparam {
                kernel_transform = fast_ab(&kernel_transform, &v);
                frozen_radial_reparam = Some(v);
            }
            prebuilt_raw_basis = Some(resolved.basis);
        }
        let basis = if let Some(basis) = prebuilt_raw_basis {
            basis
        } else {
            build_duchon_basis_designwithworkspace(
                data,
                centers.view(),
                spec.length_scale,
                spec.power,
                effective_nullspace_order,
                aniso.as_deref(),
                frozen_radial_reparam.as_ref(),
                realized_spectral_basis
                    .as_ref()
                    .and_then(DuchonSpectralBasis::kernel_transform),
                workspace,
            )?
            .basis
        };
        let identifiability_transform = spatial_identifiability_transform_from_design(
            data,
            basis.view(),
            &spec.identifiability,
            "Duchon",
        )?;
        let design = if let Some(z) = identifiability_transform.as_ref() {
            DesignMatrix::Dense(gam_linalg::matrix::DenseDesignMatrix::from(fast_ab(
                &basis, z,
            )))
        } else {
            DesignMatrix::Dense(gam_linalg::matrix::DenseDesignMatrix::from(basis))
        };
        (design, identifiability_transform)
    };
    // The Duchon penalty is a HILBERT SCALE of pure function-penalties, each a
    // plain block with its own REML λ (REML deselects what the data don't need):
    //   * curvature = the EXACT RKHS reproducing-norm Gram (`Primary`), `n`-free;
    //   * trend     = the affine null-space slope ridge (`DoublePenaltyNullspace`);
    //   * tension `Σ‖∇f‖²` + mass `Σ(f−f̄)²` = collocated on a density-blind `O(k)`
    //     farthest-point sample of the data support (their continuous integrals
    //     diverge for the polyharmonic kernel, so the support quadrature *is* the
    //     penalty — `O(k)`-in-`n`, not the old sparse-center collocation that
    //     under-resolved the basis and exploded).
    let operator_collocation_points = {
        let any_operator = matches!(
            spec.operator_penalties.mass,
            OperatorPenaltySpec::Active { .. }
        ) || matches!(
            spec.operator_penalties.tension,
            OperatorPenaltySpec::Active { .. }
        ) || matches!(
            spec.operator_penalties.stiffness,
            OperatorPenaltySpec::Active { .. }
        );
        if any_operator {
            let m = (DUCHON_COLLOCATION_OVERSAMPLE * centers.nrows()).min(data.nrows());
            Some(select_thin_plate_knots(data, m)?)
        } else {
            None
        }
    };
    let mut candidates = duchon_native_penalty_candidates_with_curvature(
        centers.view(),
        spec.length_scale,
        spec.power,
        effective_nullspace_order,
        aniso.as_deref(),
        &kernel_transform,
        identifiability_transform.as_ref(),
        spectral_bending_penalty.as_ref(),
    )?;
    if let Some(points) = operator_collocation_points.as_ref() {
        candidates.extend(duchon_operator_penalty_candidates(
            points.view(),
            centers.view(),
            &spec.operator_penalties,
            spec.length_scale,
            spec.power,
            effective_nullspace_order,
            aniso.is_some(),
            identifiability_transform.as_ref(),
            frozen_radial_reparam.as_ref(),
            workspace,
        )?);
    }
    let filtered = filter_penalty_candidates(candidates)?;
    Ok(BasisBuildResult {
        design,
        affine_offset: None,
        active_penalties: filtered.active,
        dropped_penalties: filtered.dropped,
        joint_null_rotation: None,
        metadata: BasisMetadata::Duchon {
            centers,
            // The builder standardizes nothing of its own — it emits
            // `input_scale: ONE` — so the range it was handed IS this
            // metadata's original-units range.  The term-collection wrapper
            // that DID standardize replaces the scale and the range together
            // (`term_specs.rs`), keeping the tag honest on both sides.
            length_scale: spec.length_scale.map(crate::OriginalUnits::new),
            periodic: spec.periodic.clone(),
            power: spec.power,
            nullspace_order: effective_nullspace_order,
            identifiability_transform,
            input_scale: crate::IsotropicScale::ONE,
            aniso_log_scales: aniso,
            operator_collocation_points,
            radial_reparam: frozen_radial_reparam,
            spectral_basis: realized_spectral_basis,
        },
        kronecker_factored: None,
    })
}

/// Rebuild the Duchon penalty list at a NEW `length_scale` purely from FROZEN
/// basis geometry — no data rows touched (#1033, n-free per-ψ penalty re-key).
///
/// The κ-loop fast path skips the n-row `reset_surface`, so it needs `S(ψ_new)`
/// reconstructed exactly and `n`-free at each trial length-scale. This mirrors
/// the cold penalty assembly (`build_duchon_basis_uncached` lines ~345-396)
/// EXACTLY, but every input is taken from the already-frozen
/// `BasisMetadata::Duchon` (centers, identifiability transform, operator
/// collocation points) plus the spec's `(power, nullspace_order,
/// aniso_log_scales, operator_penalties)`. The only thing that moves is
/// `length_scale`.
///
/// The polynomial-column count is `C(d + r, r)` — a pure function of `(d, r)` —
/// so it is recomputed from the centers (`polynomial_block_from_order(centers,
/// order).ncols()`), which equals the cold build's `polynomial_block_from_order(
/// data, order).ncols()` because `.ncols()` does not depend on the row count.
///
/// Returns the per-block penalty matrices (term-local frame, same order/count
/// the cold build emits) and the active per-block nullspace dims — exactly the
/// objects the cold build feeds into `filter_penalty_candidates`.
pub fn duchon_penalties_at_length_scale(
    centers: ArrayView2<'_, f64>,
    identifiability_transform: Option<&Array2<f64>>,
    operator_collocation_points: Option<ArrayView2<'_, f64>>,
    operator_penalties: &DuchonOperatorPenaltySpec,
    power: f64,
    nullspace_order: DuchonNullspaceOrder,
    aniso_log_scales: Option<&[f64]>,
    radial_reparam: Option<&Array2<f64>>,
    length_scale: Option<f64>,
    workspace: &mut BasisWorkspace,
) -> Result<(Vec<Array2<f64>>, Vec<usize>), BasisError> {
    // Recompute the effective order + auto-seeded anisotropy exactly as the cold
    // build does (duchon_thinplate.rs:151/159). Both are pure functions of the
    // frozen centers + spec, so the κ trial replays the SAME structural choices.
    let effective_nullspace_order = duchon_effective_nullspace_order(centers, nullspace_order);
    let aniso = auto_seed_aniso_contrasts(centers, aniso_log_scales);
    // n-free kernel-constraint nullspace (from centers; cached on the workspace).
    let mut kernel_transform =
        kernel_constraint_nullspace(centers, effective_nullspace_order, &mut workspace.cache)?;
    // #1355: fold the frozen data-metric reparam `Z' = Z·V` so the κ-trial
    // penalty `Z'ᵀ K_CC(ψ) Z' = diag(μ(ψ))` matches the rotated design.
    if let Some(v) = radial_reparam {
        if v.nrows() != kernel_transform.ncols() {
            crate::bail_dim_basis!(
                "Duchon frozen radial reparam shape {:?} does not match constrained kernel dimension {}",
                v.dim(),
                kernel_transform.ncols()
            );
        }
        kernel_transform = fast_ab(&kernel_transform, v);
    }
    let mut candidates = duchon_native_penalty_candidates(
        centers,
        length_scale,
        power,
        effective_nullspace_order,
        aniso.as_deref(),
        &kernel_transform,
        identifiability_transform,
    )?;
    if let Some(points) = operator_collocation_points {
        candidates.extend(duchon_operator_penalty_candidates(
            points,
            centers,
            operator_penalties,
            length_scale,
            power,
            effective_nullspace_order,
            aniso.is_some(),
            identifiability_transform,
            radial_reparam,
            workspace,
        )?);
    }
    let filtered = filter_penalty_candidates(candidates)?;
    Ok((
        filtered
            .active
            .iter()
            .map(|penalty| penalty.matrix.clone())
            .collect(),
        filtered
            .active
            .iter()
            .map(|penalty| penalty.nullity)
            .collect(),
    ))
}

/// Materialise the polynomial null-space block for a Duchon basis.
///
/// Returns an `(n, C(d+r, r))` matrix whose columns are all monomials of total
/// degree `≤ r` evaluated at `points`, where `r` is the degree implied by
/// `order` and `d = points.ncols()`.
///
/// | `order`        | columns        | content                      |
/// |----------------|----------------|------------------------------|
/// | `Zero`         | 1              | constant `1`                 |
/// | `Linear`       | `d + 1`        | `[1, x₁, …, x_d]`           |
/// | `Degree(k)`    | `C(d+k, k)`   | all monomials ≤ degree `k`   |
///
/// **Role in basis construction:**
/// At *centers*, this block forms the side-condition matrix `Q` whose null
/// space `null(Q^T)` is the kernel reparameterisation transform `Z`.  At
/// *data rows*, the same block is appended as explicit unpenalized columns so
/// the smooth can represent low-degree polynomial trends.  The column count
/// equals `C(d + r, r)` by the stars-and-bars identity.
pub(crate) fn polynomial_block_from_order(
    points: ArrayView2<'_, f64>,
    order: DuchonNullspaceOrder,
) -> Array2<f64> {
    let n = points.nrows();
    let d = points.ncols();
    match order {
        DuchonNullspaceOrder::Zero => Array2::<f64>::ones((n, 1)),
        DuchonNullspaceOrder::Linear => {
            let mut poly = Array2::<f64>::zeros((n, d + 1));
            poly.column_mut(0).fill(1.0);
            for c in 0..d {
                poly.column_mut(c + 1).assign(&points.column(c));
            }
            poly
        }
        DuchonNullspaceOrder::Degree(degree) => monomial_basis_block(points, degree),
    }
}

/// How far the Duchon range floor sits above the spectral rank cutoff it is
/// defined relative to: two decades.
///
/// The margin has to survive the later identifiability congruence `Tᵀ(·)T` and
/// the Frobenius renormalization that happen between this floor and the point
/// where the assembled block's rank is scored, while staying far below the
/// statistical scale. It is a margin on the cutoff, never a magnitude of its
/// own — hence a multiplier rather than a second literal.
const RANGE_FLOOR_ABOVE_SPECTRAL_RANK_CUTOFF: f64 = 100.0;

/// Range-floor the reparam'd Duchon Primary curvature block so its numerical
/// null space is exactly the polynomial null space, not inflated by the
/// ill-conditioned kernel Gram's low-curvature tail.
///
/// The default duchon adopts the data-metric radial reparam `V`, so the Primary
/// penalty kernel block is `Vᵀ Ω_c V` — diagonal in the `μ` (generalized
/// curvature) eigenvalues. The Duchon polyharmonic Gram is extremely
/// ill-conditioned (cond ≫ 1e10 at k=20), so most `μ` fall far below the
/// numerical-rank cutoff [`spectral_tolerance`] that
/// [`analyze_penalty_block`] uses to partition range vs null. Those genuine
/// low-curvature directions are then mis-classified as UNPENALIZED null:
/// retained in the design (they clear the SEPARATE `k·ε` design-support floor)
/// but shrinkable by NO `λ`, so the smooth cannot collapse toward the null on an
/// irrelevant covariate (measured `nulldim = 19` vs the affine `{1,x} = 2`
/// expected on the gam#1815 null-recovery fixture) and REML over-selects EDF.
///
/// Lift the smallest eigenvalues to a relative floor
/// [`RANGE_FLOOR_ABOVE_SPECTRAL_RANK_CUTOFF`] times that cutoff, evaluated at
/// the EMBEDDED penalty dimension (kernel+poly), so the floor clears the
/// tolerance the assembled block is scored against and every retained mode is a
/// genuine — if weak — penalized `Range` direction; REML's `λ→∞` tail then
/// collapses them. The floor is far below the
/// statistical scale and lifts only the lowest-curvature (near-linear) modes, so
/// signal recovery (e.g. the sin8 centers=50 escape) is unchanged — the
/// high-curvature signal modes sit orders of magnitude above the floor.
pub(crate) fn duchon_range_floor_curvature(
    omega: &Array2<f64>,
    embedded_penalty_dim: usize,
) -> Result<Array2<f64>, BasisError> {
    let n = omega.nrows();
    if n == 0 {
        return Ok(omega.clone());
    }
    let sym = symmetrize_penalty(omega);
    let (mut evals, evecs) = FaerEigh::eigh(&sym, Side::Lower).map_err(BasisError::LinalgError)?;
    let lam_max = evals.iter().copied().fold(0.0_f64, |a, v| a.max(v.abs()));
    if !lam_max.is_finite() || lam_max <= 0.0 {
        return Ok(sym);
    }
    // Read the cutoff from the same helper `analyze_penalty_block` scores this
    // block with, at the EMBEDDED dimension, and lift by the stated margin.
    // Writing the product out as a literal is what let the doc comment above
    // drift to "one decade" while the code kept two.
    let floor = RANGE_FLOOR_ABOVE_SPECTRAL_RANK_CUTOFF
        * spectral_tolerance_for_dim(embedded_penalty_dim.max(n), &evals);
    let mut floored = false;
    for v in evals.iter_mut() {
        if v.is_finite() && *v < floor {
            *v = floor;
            floored = true;
        }
    }
    if !floored {
        return Ok(sym);
    }
    // Reconstruct `U diag(evals) Uᵀ` with the floored spectrum.
    let mut out = Array2::<f64>::zeros((n, n));
    for j in 0..n {
        let lam = evals[j];
        for a in 0..n {
            let ua = evecs[[a, j]];
            if ua == 0.0 {
                continue;
            }
            for b in 0..n {
                out[[a, b]] += ua * lam * evecs[[b, j]];
            }
        }
    }
    Ok(symmetrize_penalty(&out))
}

/// First and second log-κ (ψ) derivatives of the range-floored curvature Gram.
///
/// The forward `duchon_native_penalty_candidates` ships the `Primary` block as
/// `range_floor(Ω(ψ))`, where `range_floor` clamps every eigenvalue below
/// `floor(ψ) = max(embedded_dim, n)·1e-8·λ_max(Ω(ψ))` up to that floor (#1815).
/// The clamp is a spectral function `Ω ↦ U max(Λ, φ) Uᵀ` whose threshold `φ`
/// itself moves with ψ (through `λ_max`), so its ψ-derivative is NOT the plain
/// `Ω'`: the near-null curvature modes — precisely the high-frequency modes with
/// the *largest* `λ'` — are pinned to `φ(ψ)`, killing their own derivative and
/// replacing it with `φ' = c·λ_max'`. Omitting this makes the analytic Primary
/// log-κ gradient overstate the true (floored) penalty derivative by ~60% on a
/// 1-D hybrid Duchon, desyncing the outer REML gradient from the cost it is
/// built on. This helper differentiates the clamp exactly via the
/// Daleckii–Krein calculus (first and second Fréchet derivatives of a spectral
/// function) plus the explicit `φ(ψ)` dependence.
pub(crate) struct RangeFloorPsiJet {
    pub value: Array2<f64>,
    pub first: Array2<f64>,
    pub second: Array2<f64>,
}

pub(crate) fn duchon_range_floor_curvature_psi_jet(
    omega: &Array2<f64>,
    omega_psi: &Array2<f64>,
    omega_psi_psi: &Array2<f64>,
    embedded_penalty_dim: usize,
) -> Result<RangeFloorPsiJet, BasisError> {
    let n = omega.nrows();
    let sym = symmetrize_penalty(omega);
    let sym_psi = symmetrize_penalty(omega_psi);
    let sym_psi_psi = symmetrize_penalty(omega_psi_psi);
    let passthrough = || RangeFloorPsiJet {
        value: sym.clone(),
        first: sym_psi.clone(),
        second: sym_psi_psi.clone(),
    };
    if n == 0 {
        return Ok(passthrough());
    }
    let (evals, evecs) = FaerEigh::eigh(&sym, Side::Lower).map_err(BasisError::LinalgError)?;
    // Index of the eigenvalue of largest magnitude (mirrors `range_floor`'s
    // `λ_max = max|λ|`; for the PSD curvature Gram this is the top eigenvalue).
    let mut imax = 0usize;
    let mut lam_max = 0.0_f64;
    for (i, &v) in evals.iter().enumerate() {
        if v.abs() > lam_max {
            lam_max = v.abs();
            imax = i;
        }
    }
    if !lam_max.is_finite() || lam_max <= 0.0 {
        return Ok(passthrough());
    }
    let c = (embedded_penalty_dim.max(n) as f64) * 1e-8;
    let floor = c * lam_max;
    // No mode below the floor ⇒ the clamp is locally the identity, so the plain
    // derivatives pass through unchanged (matches `range_floor`'s early return).
    if !evals.iter().any(|&v| v.is_finite() && v < floor) {
        return Ok(passthrough());
    }

    let u = &evecs;
    // Derivative matrices resolved into the eigenbasis: A = Uᵀ Ω' U, A2 = Uᵀ Ω'' U.
    let a = u.t().dot(&sym_psi).dot(u);
    let a2 = u.t().dot(&sym_psi_psi).dot(u);

    // Moving threshold φ(ψ) = c·λ_max(ψ). Hellmann–Feynman for the (assumed
    // simple) extremal eigenvalue: λ_max' = sign·A_{imax,imax}; the standard
    // second-order eigenvalue perturbation gives λ_max''.
    let sign = if evals[imax] >= 0.0 { 1.0 } else { -1.0 };
    let tol = 1e-9 * lam_max;
    let lam_max_prime = sign * a[[imax, imax]];
    let mut lam_max_pp = a2[[imax, imax]];
    for k in 0..n {
        if k == imax {
            continue;
        }
        let denom = evals[imax] - evals[k];
        if denom.abs() > tol {
            lam_max_pp += 2.0 * a[[imax, k]] * a[[imax, k]] / denom;
        }
    }
    let lam_max_pp = sign * lam_max_pp;
    let floor_prime = c * lam_max_prime;
    let floor_pp = c * lam_max_pp;

    // Scalar clamp g(λ) = max(λ, φ) and the indicator of the clamped subspace
    // (∂g/∂φ = 1 on clamped modes, 0 otherwise).
    let g = |lam: f64| lam.max(floor);
    let gprime = |lam: f64| if lam > floor { 1.0 } else { 0.0 };
    let clamped = |lam: f64| if lam <= floor { 1.0 } else { 0.0 };

    // First divided difference of g (Daleckii–Krein weight for the implicit
    // Ω-dependence), and the same for the clamp indicator (∂Γ¹/∂φ).
    let fdd = |la: f64, lb: f64| -> f64 {
        if (la - lb).abs() > tol {
            (g(la) - g(lb)) / (la - lb)
        } else {
            gprime(0.5 * (la + lb))
        }
    };
    let fdd_clamp = |la: f64, lb: f64| -> f64 {
        if (la - lb).abs() > tol {
            (clamped(la) - clamped(lb)) / (la - lb)
        } else {
            0.0
        }
    };
    // Second divided difference of g (weight for the second Fréchet derivative).
    let sdd = |la: f64, lb: f64, lc: f64| -> f64 {
        if (la - lc).abs() > tol {
            (fdd(la, lb) - fdd(lb, lc)) / (la - lc)
        } else if (la - lb).abs() > tol {
            (gprime(la) * (la - lb) - (g(la) - g(lb))) / ((la - lb) * (la - lb))
        } else {
            0.0
        }
    };

    // Assemble the eigenbasis blocks, then rotate back with U (·) Uᵀ.
    // Value: G = U diag(g(λ)) Uᵀ.
    let mut gam1 = Array2::<f64>::zeros((n, n)); // Γ¹ ⊙ · weight
    let mut gam_clamp = Array2::<f64>::zeros((n, n)); // ∂Γ¹/∂φ weight
    for i in 0..n {
        for j in 0..n {
            gam1[[i, j]] = fdd(evals[i], evals[j]);
            gam_clamp[[i, j]] = fdd_clamp(evals[i], evals[j]);
        }
    }
    // Clamped-subspace projector in the eigenbasis (diagonal).
    let clamp_diag: Vec<f64> = evals.iter().map(|&l| clamped(l)).collect();

    // First derivative in the eigenbasis:
    //   B1 = Γ¹ ⊙ A            (implicit Ω-dependence, Daleckii–Krein)
    //      + φ' · Π            (explicit moving-threshold dependence)
    let mut b1 = &gam1 * &a;
    for i in 0..n {
        b1[[i, i]] += floor_prime * clamp_diag[i];
    }

    // Second derivative in the eigenbasis:
    //   B2 = Γ¹ ⊙ A2                                   (D_Ω h[Ω''])
    //      + 2 · Σ_k sdd(λ_i,λ_k,λ_j) A_ik A_kj        (D²_Ω h[Ω',Ω'])
    //      + 2 φ' · (∂Γ¹/∂φ ⊙ A)                        (cross Ω–φ term)
    //      + φ'' · Π                                    (explicit φ'')
    let mut b2 = &gam1 * &a2;
    // second Fréchet block
    for i in 0..n {
        for j in 0..n {
            let mut acc = 0.0;
            for k in 0..n {
                acc += sdd(evals[i], evals[k], evals[j]) * a[[i, k]] * a[[k, j]];
            }
            b2[[i, j]] += 2.0 * acc;
        }
    }
    let cross = (&gam_clamp * &a).mapv(|v| 2.0 * floor_prime * v);
    b2 = b2 + cross;
    for i in 0..n {
        b2[[i, i]] += floor_pp * clamp_diag[i];
    }

    // Value block diag(g(λ)).
    let mut value_eig = Array2::<f64>::zeros((n, n));
    for i in 0..n {
        value_eig[[i, i]] = g(evals[i]);
    }

    let rotate = |m: &Array2<f64>| symmetrize_penalty(&u.dot(m).dot(&u.t()));
    Ok(RangeFloorPsiJet {
        value: rotate(&value_eig),
        first: rotate(&b1),
        second: rotate(&b2),
    })
}

pub fn monomial_exponents(dimension: usize, max_total_degree: usize) -> Vec<Vec<usize>> {
    fn recurse(
        axis: usize,
        remaining_degree: usize,
        current: &mut [usize],
        out: &mut Vec<Vec<usize>>,
    ) {
        if axis + 1 == current.len() {
            current[axis] = remaining_degree;
            out.push(current.to_vec());
            return;
        }
        for exponent in (0..=remaining_degree).rev() {
            current[axis] = exponent;
            recurse(axis + 1, remaining_degree - exponent, current, out);
        }
    }

    if dimension == 0 {
        return vec![Vec::new()];
    }

    let mut out = Vec::new();
    let mut current = vec![0usize; dimension];
    for total_degree in 0..=max_total_degree {
        recurse(0, total_degree, &mut current, &mut out);
    }
    out
}

pub fn duchon_nullspace_dimension(dimension: usize, max_total_degree: usize) -> usize {
    monomial_exponents(dimension, max_total_degree).len()
}

pub(crate) fn monomial_basis_block(
    points: ArrayView2<'_, f64>,
    max_total_degree: usize,
) -> Array2<f64> {
    let n = points.nrows();
    let exponents = monomial_exponents(points.ncols(), max_total_degree);
    let mut block = Array2::<f64>::zeros((n, exponents.len()));
    for (col, exponents) in exponents.iter().enumerate() {
        for row in 0..n {
            let mut value = 1.0;
            for axis in 0..points.ncols() {
                let exponent = exponents[axis];
                if exponent != 0 {
                    value *= points[[row, axis]].powi(exponent as i32);
                }
            }
            block[[row, col]] = value;
        }
    }
    block
}

#[inline(always)]
pub(crate) fn thin_plate_polynomial_degree(dimension: usize) -> usize {
    thin_plate_penalty_order(dimension).saturating_sub(1)
}

pub(crate) fn thin_plate_polynomial_block(points: ArrayView2<'_, f64>) -> Array2<f64> {
    monomial_basis_block(points, thin_plate_polynomial_degree(points.ncols()))
}

pub fn thin_plate_polynomial_basis_dimension(dimension: usize) -> usize {
    monomial_exponents(dimension, thin_plate_polynomial_degree(dimension)).len()
}

/// Row-order-canonical realized design Gram `symmetrize(KᵀK)` for the data-metric
/// radial reparam (#1347/#1355).
///
/// `KᵀK = Σ_row (row)ᵀ(row)` is mathematically invariant to a pure row
/// permutation of the training data, but `fast_atb` accumulates the outer
/// products in the kernel block's stored (data) row order, so floating-point
/// non-associativity lets a reordering perturb the Gram by an ulp. The reparam
/// eigendecomposition fed by this Gram is near-degenerate (the thin-plate radial
/// spectrum has a long low-curvature tail), so that ulp rotates its eigenvectors
/// and makes the fitted `s(x, bs="tp")` basis — and hence the curve — depend on
/// row order. That is the residual ~2e-7 row-permutation drift owed under
/// gam#1378 that survives the value-anchored knot set and centroid seed (the
/// local `bs="cr"/"ps"` bases never form this data-metric radial Gram, so they
/// stayed bit-stable). Summing the rows in a canonical lexicographic (`total_cmp`)
/// order gives the identical addition sequence for every permutation of the same
/// unordered row set — the rows are a pure function of the data and genuinely
/// equal rows contribute equal, order-free terms — so the Gram, its
/// eigendecomposition, and the reparam become bit-identical across row order.
fn data_metric_design_gram(kernel_block: ArrayView2<'_, f64>) -> Array2<f64> {
    let n = kernel_block.nrows();
    let mut order: Vec<usize> = (0..n).collect();
    order.sort_by(|&a, &b| {
        for c in 0..kernel_block.ncols() {
            match kernel_block[[a, c]].total_cmp(&kernel_block[[b, c]]) {
                std::cmp::Ordering::Equal => {}
                ord => return ord,
            }
        }
        std::cmp::Ordering::Equal
    });
    let sorted = kernel_block.select(Axis(0), &order);
    symmetrize_penalty(&fast_atb(&sorted, &sorted))
}

/// Selects which radial penalty eigenmodes to expose as basis columns.
///
/// The constrained radial penalty `Ω` is SPD in exact arithmetic — the
/// polynomial null space `{1, x, …}` has already been removed by the gauge
/// restriction, so every nonzero eigenvalue is a genuine bending direction
/// and must be retained (this matches mgcv's thin-plate construction, which
/// keeps all `k − M` radial modes and relies on REML, not basis truncation,
/// to set the effective degrees of freedom). The only modes that are NOT
/// real curvature directions are **roundoff dust**: eigenvalues at or below
/// the LAPACK numerical-rank floor `K·ε·λ_max` (Golub & Van Loan, *Matrix
/// Computations*, §2.5.6) are exact zeros polluted by floating-point error
/// from the constraint restriction and carry no information.
///
/// The threshold is therefore the standard numerical-rank floor — derived,
/// scale-free, and tuning-free. It deliberately does NOT prune low-but-real
/// bending modes by magnitude: doing so was the #1271 hill-climb (a swept
/// `max_eval·tol` cutoff) that over-pruned the nonlinear arms (lidar /
/// by-factor truth recovery collapsed) while still missing the linear EDF
/// bar. The genuine over-fit on near-linear data is a REML smoothing issue
/// (the diagonalised radial penalty's wide eigenvalue spread under a single
/// `λ` leaves a flat REML profile, so the outer optimiser terminates at an
/// interior `λ` that under-smooths), not a basis-rank issue — pruning cannot
/// fix it without destroying the bending capacity real data needs.
fn thin_plate_retained_radial_indices(evals: &Array1<f64>) -> Vec<usize> {
    let k = evals.len();
    if k == 0 {
        return Vec::new();
    }
    let max_eval = evals
        .iter()
        .copied()
        .fold(0.0_f64, |acc, value| acc.max(value.abs()));
    if !max_eval.is_finite() || max_eval <= 0.0 {
        return Vec::new();
    }
    // Numerical-rank floor: anything at or below `K·ε·λ_max` is roundoff dust
    // from the gauge restriction, not a real bending mode. Everything above it
    // is genuine curvature and is kept.
    let num_floor = (k as f64) * f64::EPSILON * max_eval;
    evals
        .iter()
        .enumerate()
        .filter_map(|(idx, &value)| (value.abs() > num_floor).then_some(idx))
        .collect()
}

pub(crate) fn thin_plate_radial_reparam_from_constrained_penalty(
    omega_constrained: &Array2<f64>,
) -> Result<(Array2<f64>, Array1<f64>), BasisError> {
    let kernel_cols = omega_constrained.nrows();
    if kernel_cols != omega_constrained.ncols() {
        crate::bail_dim_basis!(
            "thin-plate constrained radial penalty must be square: got {:?}",
            omega_constrained.dim()
        );
    }
    if kernel_cols == 0 {
        return Ok((Array2::<f64>::zeros((0, 0)), Array1::<f64>::zeros(0)));
    }
    let sym = symmetrize_penalty(omega_constrained);
    let (mut evals, evecs) = FaerEigh::eigh(&sym, Side::Lower).map_err(BasisError::LinalgError)?;
    for value in evals.iter_mut() {
        if *value < 0.0 {
            *value = 0.0;
        }
    }
    let keep = thin_plate_retained_radial_indices(&evals);
    Ok((evecs.select(Axis(1), &keep), evals.select(Axis(0), &keep)))
}

/// Thin-plate radial reparameterization in the **realized data metric** (#1347).
///
/// The penalty is the polyharmonic bending energy `Ω_c = Zᵀ K_CC Z` (the RKHS
/// reproducing-norm Gram on the constrained kernel coefficients). gam's old
/// reparam eigendecomposed `Ω_c` alone, laying its raw eigenvalues on the
/// penalty diagonal. But the constraint `Z` has already quotiented out the
/// `{1, x}` polynomial null space, so `Ω_c` is full-rank with a smooth Mercer
/// tail and **no cliff** — its smallest eigenvalues are genuine low-curvature
/// bending directions that nonetheless carry large variance over the data.
/// Under a single REML `λ` those near-null modes cost almost nothing yet absorb
/// EDF freely, over-fitting near-linear data (mean EDF ≈ 5.3 vs mgcv ≈ 2.1).
///
/// mgcv's TPRS instead penalizes bending energy **relative to the realized
/// design metric** — equivalently it solves the generalized eigenproblem
///
/// ```text
///   Ω_c v = μ G_c v ,   G_c = (K Z)ᵀ (K Z)
/// ```
///
/// where `G_c` is the Gram of the realized constrained kernel design columns.
/// The eigenvalue `μ = (vᵀ Ω_c v)/(vᵀ G_c v)` is curvature per unit
/// data-variance: it spreads the spectrum the way mgcv's does (top mode, a
/// `0.77` second mode, then a clean geometric cliff to the tail), so a single
/// `λ` can no longer buy near-free wiggle. The returned eigenvectors `V` are
/// `G_c`-orthonormal (`Vᵀ G_c V = I`), so the rotated design `K Z V` has an
/// identity Gram and the penalty is exactly `diag(μ) = Vᵀ Ω_c V` — which the
/// frozen-replay / length-scale paths already recover via `diag(Vᵀ Ω_c V)`, so
/// no downstream change is needed. The model space `span(K Z V) = span(K Z)` is
/// unchanged (`V` is invertible), preserving full nonlinear capacity.
pub(crate) fn thin_plate_radial_reparam_data_metric(
    omega_constrained: &Array2<f64>,
    design_gram: &Array2<f64>,
) -> Result<(Array2<f64>, Array1<f64>), BasisError> {
    let k = omega_constrained.nrows();
    if k != omega_constrained.ncols() || design_gram.nrows() != k || design_gram.ncols() != k {
        crate::bail_dim_basis!(
            "thin-plate data-metric reparam requires square k×k Ω_c and G_c: Ω_c={:?}, G_c={:?}",
            omega_constrained.dim(),
            design_gram.dim()
        );
    }
    if k == 0 {
        return Ok((Array2::<f64>::zeros((0, 0)), Array1::<f64>::zeros(0)));
    }
    // Whiten by G_c: G_c = U_g D_g U_gᵀ ; W = U_g D_g^{-1/2} (drop near-null G_c
    // directions, which are design columns with no realized data support).
    let g_sym = symmetrize_penalty(design_gram);
    let (g_evals, g_evecs) =
        FaerEigh::eigh(&g_sym, Side::Lower).map_err(BasisError::LinalgError)?;
    let gmax = g_evals.iter().copied().fold(0.0_f64, |a, b| a.max(b.abs()));
    if !gmax.is_finite() || gmax <= 0.0 {
        // Degenerate design Gram: fall back to the plain bending eigenbasis.
        return thin_plate_radial_reparam_from_constrained_penalty(omega_constrained);
    }
    let g_floor = (k as f64) * f64::EPSILON * gmax;
    let mut cols: Vec<usize> = Vec::with_capacity(k);
    for j in 0..k {
        if g_evals[j] > g_floor {
            cols.push(j);
        }
    }
    let m = cols.len();
    if m == 0 {
        return thin_plate_radial_reparam_from_constrained_penalty(omega_constrained);
    }
    let mut w = Array2::<f64>::zeros((k, m));
    for (c, &j) in cols.iter().enumerate() {
        let inv_sqrt = 1.0 / g_evals[j].sqrt();
        for i in 0..k {
            w[[i, c]] = g_evecs[[i, j]] * inv_sqrt;
        }
    }
    // M = Wᵀ Ω_c W (m×m), eig(M) = (μ, P). Generalized eigenvectors V = W P.
    let omega_sym = symmetrize_penalty(omega_constrained);
    let wt_omega = fast_atb(&w, &omega_sym);
    let m_mat = symmetrize_penalty(&fast_ab(&wt_omega, &w));
    let (mut mu, p_mat) = FaerEigh::eigh(&m_mat, Side::Lower).map_err(BasisError::LinalgError)?;
    for value in mu.iter_mut() {
        if *value < 0.0 {
            *value = 0.0;
        }
    }
    let v_full = fast_ab(&w, &p_mat); // k×m, G_c-orthonormal columns
    let keep = thin_plate_retained_radial_indices(&mu);
    Ok((v_full.select(Axis(1), &keep), mu.select(Axis(0), &keep)))
}

/// The Duchon coefficient chart resolved against one `(data, spec)` pair: the
/// adopted data-metric radial reparameterization `V` together with the realized
/// pre-identifiability design expressed IN that chart.
///
/// See [`duchon_resolve_radial_chart`] for why this is a type rather than two
/// inlined blocks.
pub(crate) struct DuchonResolvedRadialChart {
    /// The adopted reparam `V`, or `None` when none was adopted (degenerate
    /// generalized eigenproblem, or no constrained kernel columns at all).
    pub(crate) reparam: Option<Array2<f64>>,
    /// The realized pre-identifiability design in the resolved chart:
    /// `[K·Z·V | P]` when `V` was adopted, `[K·Z | P]` otherwise.
    pub(crate) basis: Array2<f64>,
}

/// Resolve the Duchon coefficient chart for a spec that does not carry one.
///
/// # Why this exists
///
/// `build_duchon_basis` ships every design column and every penalty in the
/// `Z·V` frame, where `V` is the data-metric radial reparameterization (#1355)
/// solving the generalized eigenproblem `Ω_c v = μ G_c v`. On a replay path
/// (`spec.radial_reparam = Some(V)`) that chart is handed in. On a COLD path it
/// is computed here — and until #2638 it was computed *only* here, inline in
/// the forward builder, which meant every other consumer of the same spec
/// silently assumed "no frozen reparam" ⇒ "no reparam", i.e. the raw `Z` frame.
///
/// That assumption is what broke the log-κ derivative surface. The ψ-jet
/// builders fold `V` only `if let Some(v) = spec.radial_reparam`, so on a cold
/// spec they assembled `dS/dψ` in the un-rotated `Z` frame — a right derivative
/// of a matrix the forward never ships. Measured on the `_no_ident` fixture at
/// ε = 1e-5: the returned Primary jet was 32× the true frozen-chart jet and the
/// OperatorMass jet 242× too small, with the whole residual accounted for by
/// chart motion (`|FD_cold − FD_frozen| = 2.49e-1` against a `|A − FD|` of
/// 1.6e-5 once both sides sit in the same chart).
///
/// Routing both the forward and the derivative context through this one
/// function makes the frame a property of `(data, spec)` rather than of which
/// builder you happened to call.
///
/// # Cost
///
/// One `n×k` kernel materialization, which the caller gets back in
/// [`DuchonResolvedRadialChart::basis`] — the rotated design is obtained as
/// `(K·Z)·V = K·(Z·V)` rather than by a second kernel pass (#1718).
pub(crate) fn duchon_resolve_radial_chart(
    data: ArrayView2<'_, f64>,
    centers: ArrayView2<'_, f64>,
    spec: &DuchonBasisSpec,
    effective_nullspace_order: DuchonNullspaceOrder,
    aniso: Option<&[f64]>,
    kernel_transform: &Array2<f64>,
    workspace: &mut BasisWorkspace,
) -> Result<DuchonResolvedRadialChart, BasisError> {
    // Build the un-rotated constrained kernel design once, take its realized
    // Gram `G_c = (K·Z)ᵀ(K·Z)`, and solve `Ω_c v = μ G_c v` with
    // `Ω_c = α²·ZᵀK_CC Z`.
    let raw = build_duchon_basis_designwithworkspace(
        data,
        centers,
        spec.length_scale,
        spec.power,
        effective_nullspace_order,
        aniso,
        None,
        None,
        workspace,
    )?;
    let kernel_cols = kernel_transform.ncols();
    if kernel_cols == 0 {
        return Ok(DuchonResolvedRadialChart {
            reparam: None,
            basis: raw.basis,
        });
    }
    let kernel_block = raw.basis.slice(s![.., 0..kernel_cols]);
    // Canonical row order so the realized Gram (and the near-degenerate reparam
    // it feeds) is bit-identical under a pure row permutation (#1378).
    let design_gram = data_metric_design_gram(kernel_block);
    let omega_constrained = duchon_constrained_bending_penalty(
        centers,
        spec.length_scale,
        spec.power,
        effective_nullspace_order,
        aniso,
        kernel_transform,
    )?;
    let (v, _mu) = thin_plate_radial_reparam_data_metric(&omega_constrained, &design_gram)?;
    // A degenerate reparam (no retained modes) would gut the basis; only adopt
    // `V` when it preserves at least one radial column.
    if v.ncols() == 0 {
        // No reparam adopted: `raw` already IS the fit-time design.
        return Ok(DuchonResolvedRadialChart {
            reparam: None,
            basis: raw.basis,
        });
    }
    // The fit-time design is `[K·Z·V | P] = [(K·Z)·V | P]`, where `K·Z` and `P`
    // are exactly the kernel/poly blocks of `raw` (the reparam only
    // right-multiplies the constrained kernel columns; the poly block is
    // reparam-independent). So rotate `raw`'s kernel block by `V` in place
    // rather than re-evaluating the kernel — the same model space the un-fused
    // rebuild would produce.
    let rotated_kernel = fast_ab(&raw.basis.slice(s![.., 0..kernel_cols]), &v);
    let poly_block = raw.basis.slice(s![.., kernel_cols..]);
    let mut fused = Array2::<f64>::zeros((
        raw.basis.nrows(),
        rotated_kernel.ncols() + poly_block.ncols(),
    ));
    fused
        .slice_mut(s![.., 0..rotated_kernel.ncols()])
        .assign(&rotated_kernel);
    if poly_block.ncols() > 0 {
        fused
            .slice_mut(s![.., rotated_kernel.ncols()..])
            .assign(&poly_block);
    }
    Ok(DuchonResolvedRadialChart {
        reparam: Some(v),
        basis: fused,
    })
}

/// A `DuchonBasisSpec` with every ψ-invariant chart decision resolved against
/// the data, plus the artifacts those decisions produced.
///
/// See [`duchon_resolve_chart`].
#[derive(Clone, Debug)]
pub struct ResolvedDuchonChart {
    /// The input spec with `center_strategy` realized to `UserProvided`,
    /// `nullspace_order` degraded to the effective order, `aniso_log_scales`
    /// auto-seeded, `radial_reparam` set to the adopted `V`, and
    /// `identifiability` frozen to the realized transform.
    ///
    /// `build_duchon_basis(data, &resolved.spec)` reproduces
    /// `build_duchon_basis(data, spec)` — same design, same penalties, same
    /// metadata — because every decision the second build would re-make is
    /// already pinned in the first.
    pub spec: DuchonBasisSpec,
    /// The realized centers (periodic images expanded).
    pub centers: Array2<f64>,
    /// The realized identifiability transform, `None` when the spec asks for
    /// no constraint.
    pub identifiability_transform: Option<Array2<f64>>,
}

/// Resolve every ψ-invariant chart decision a Duchon build makes, so a caller
/// can hand the SAME chart to the forward and to its ψ-derivative.
///
/// # The contract this exists to make expressible
///
/// A Duchon basis is not determined by `spec` alone. Four decisions are taken
/// against the data at build time and then baked into `BasisMetadata::Duchon`:
///
/// | decision | forward site |
/// |---|---|
/// | realized centers | `select_centers_by_strategy` |
/// | effective null-space order (degraded when the centers cannot span the requested polynomial block) | `duchon_effective_nullspace_order` |
/// | auto-seeded anisotropy contrasts | `auto_seed_aniso_contrasts` |
/// | data-metric radial reparam `V` (#1355) | `duchon_resolve_radial_chart` |
/// | identifiability transform `T`, derived from the **`V`-rotated** design | `spatial_identifiability_transform_from_design` |
///
/// Until #2638 the log-κ derivative context re-made only the first of these and
/// took the raw spec value for the rest: it passed `spec.nullspace_order`
/// (not the effective order), `spec.aniso_log_scales` (not the seeded
/// contrasts), `None` for the reparam, and derived `T` from the **un**-rotated
/// design. So `build_duchon_basis_log_kappa_derivatives(data, spec)` returned
/// the ψ-jet of a basis `build_duchon_basis(data, spec)` does not build. The
/// dominant term was the reparam: measured on a 1-D `power=1`, `Linear`
/// fixture, the returned Primary jet was 32× the true jet and the OperatorMass
/// jet 242× too small, each in the wrong coefficient frame.
///
/// Resolving once and passing the resolved spec everywhere makes that class of
/// desync unrepresentable rather than merely fixed: a builder that reads
/// `spec.radial_reparam` from a resolved spec cannot see `None` where the
/// forward saw a `V`.
///
/// # Cost
///
/// One dense `n×k` kernel materialization (the design that feeds the `V`
/// eigenproblem and the identifiability test). This is the same pass the
/// derivative context already paid; the resolver reuses it for both.
pub fn duchon_resolve_chart(
    data: ArrayView2<'_, f64>,
    spec: &DuchonBasisSpec,
    workspace: &mut BasisWorkspace,
) -> Result<ResolvedDuchonChart, BasisError> {
    let original_centers = select_centers_by_strategy(data, &spec.center_strategy)?;
    let centers = expand_periodic_centers(&original_centers, spec.periodic.as_deref())?;
    assert_spatial_centers_below_large_scale_cap(data.ncols(), centers.view())?;
    let effective_nullspace_order =
        duchon_effective_nullspace_order(centers.view(), spec.nullspace_order);
    let aniso = auto_seed_aniso_contrasts(centers.view(), spec.aniso_log_scales.as_deref());

    let mut resolved = spec.clone();
    resolved.center_strategy = CenterStrategy::UserProvided(centers.clone());
    resolved.nullspace_order = effective_nullspace_order;
    resolved.aniso_log_scales = aniso.clone();

    // The pre-identifiability design IN the resolved radial chart. On a replay
    // spec the chart is handed in; on a cold spec it is solved for here by the
    // same helper the forward uses, so the two frames agree by construction.
    let basis = match spec.radial_reparam.as_ref() {
        Some(v) => {
            build_duchon_basis_designwithworkspace(
                data,
                centers.view(),
                spec.length_scale,
                spec.power,
                effective_nullspace_order,
                aniso.as_deref(),
                Some(v),
                None,
                workspace,
            )?
            .basis
        }
        None => {
            let kernel_transform = kernel_constraint_nullspace(
                centers.view(),
                effective_nullspace_order,
                &mut workspace.cache,
            )?;
            if kernel_transform.ncols() == 0 {
                build_duchon_basis_designwithworkspace(
                    data,
                    centers.view(),
                    spec.length_scale,
                    spec.power,
                    effective_nullspace_order,
                    aniso.as_deref(),
                    None,
                    None,
                    workspace,
                )?
                .basis
            } else {
                let chart = duchon_resolve_radial_chart(
                    data,
                    centers.view(),
                    spec,
                    effective_nullspace_order,
                    aniso.as_deref(),
                    &kernel_transform,
                    workspace,
                )?;
                resolved.radial_reparam = chart.reparam;
                chart.basis
            }
        }
    };

    // `T` is a property of the REALIZED design, so it must be read off the
    // rotated basis — the forward derives it after folding `V`, and a `T` built
    // on the un-rotated columns constrains a different function space.
    let identifiability_transform = spatial_identifiability_transform_from_design(
        data,
        basis.view(),
        &spec.identifiability,
        "Duchon",
    )?;
    resolved.identifiability = match identifiability_transform.as_ref() {
        Some(transform) => SpatialIdentifiability::FrozenTransform {
            transform: transform.clone(),
        },
        None => SpatialIdentifiability::None,
    };

    Ok(ResolvedDuchonChart {
        spec: resolved,
        centers,
        identifiability_transform,
    })
}

pub(crate) fn thin_plate_radial_reparam_from_centers(
    centers: ArrayView2<'_, f64>,
    length_scale: f64,
    kernel_transform: &Array2<f64>,
) -> Result<(Array2<f64>, Array1<f64>), BasisError> {
    let k = centers.nrows();
    let d = centers.ncols();
    let mut omega = Array2::<f64>::zeros((k, k));
    let length_scale_sq = length_scale * length_scale;
    fill_symmetric_from_row_kernel(&mut omega, |i, j| {
        let mut dist2 = 0.0;
        for c in 0..d {
            let delta = centers[[i, c]] - centers[[j, c]];
            dist2 += delta * delta;
        }
        thin_plate_kernel_from_dist2(dist2 / length_scale_sq, d)
    })?;
    let kernel_gauge = gam_problem::Gauge::from_block_transforms(&[kernel_transform.clone()]);
    let omega_constrained = symmetrize_penalty(&kernel_gauge.restrict_penalty(&omega));
    thin_plate_radial_reparam_from_constrained_penalty(&omega_constrained)
}

pub(crate) fn kernel_constraint_nullspace_from_matrix(
    constraint_matrix: ArrayView2<'_, f64>,
) -> Result<Array2<f64>, BasisError> {
    let k = constraint_matrix.nrows();
    let q = constraint_matrix.ncols();
    if q == 0 {
        return Ok(Array2::<f64>::eye(k));
    }
    // Constraint system Q^T alpha = 0. The trailing columns of the orthogonal
    // factor in a column-pivoted QR of Q span null(Q^T).
    let (z, _) = rrqr_nullspace_basis(&constraint_matrix, default_rrqr_rank_alpha())
        .map_err(BasisError::LinalgError)?;
    Ok(z)
}

/// Relative tolerance (against the data's squared radius) below which two
/// farthest-point candidates' maximin — or centroid — distances are treated as
/// *tied* and resolved by the rotation/permutation-invariant support-distance
/// profile rather than by their exact floating-point ordering.
///
/// A generic (non-90°) rigid rotation of the covariates re-expresses every
/// coordinate with ~1 ulp of round-off, so the squared distances that drive the
/// farthest-point recursion differ from their exact rotation-invariant values by
/// ~`ε·‖x‖²`. This tolerance is set several orders of magnitude above that
/// round-off floor yet far below any genuine gap between geometrically-distinct
/// candidates, so it absorbs the sub-ulp perturbation without altering the
/// selection on data whose maximin values are genuinely separated.
const KNOT_MAXIMIN_TIE_REL_TOL: f64 = 1e-9;

/// Deterministically selects thin-plate knots via farthest-point sampling.
///
/// This produces a space-filling subset without introducing RNG/state coupling.
///
/// Each step minimizes its composite key in extremum-then-refine order rather
/// than by carrying a running incumbent: the two `O(1)` keys first, then — only
/// over the rows that attain them, and only if there is more than one — the
/// `O(n·d + n log n)` sorted support-distance profile. Lexicographic
/// minimization is associative, so this is the same total preorder the incumbent
/// scan applied; what changes is that the profile key is charged where it can
/// still decide something instead of four times per outer iteration whether or
/// not anything is tied. On data with no exact symmetry it is never built at all
/// (#2420, Euclidean twin of the spherical selector's fix).
pub fn select_thin_plate_knots(
    data: ArrayView2<f64>,
    num_knots: usize,
) -> Result<Array2<f64>, BasisError> {
    let d = data.ncols();
    let (selected, _profile_builds) = select_thin_plate_knot_rows(data, num_knots)?;
    let mut knots = Array2::<f64>::zeros((selected.len(), d));
    for (r, &idx) in selected.iter().enumerate() {
        knots.row_mut(r).assign(&data.row(idx));
    }
    Ok(knots)
}

/// [`select_thin_plate_knots`] as the row indices it selects, paired with the
/// number of `O(n·d + n log n)` support-distance profiles the shared tie-break
/// actually built getting there.
///
/// The count is a plain second return value rather than an observer callback:
/// production ignores it, and tests read it to state the tie-break's cost
/// contract in operation counts rather than in wall-clock noise — over the same
/// production code path either way.
fn select_thin_plate_knot_rows(
    data: ArrayView2<f64>,
    num_knots: usize,
) -> Result<(Vec<usize>, usize), BasisError> {
    let mut profile_builds = 0usize;
    let n = data.nrows();
    let d = data.ncols();
    if d == 0 {
        crate::bail_invalid_basis!("thin-plate spline requires at least one covariate dimension");
    }
    if n == 0 {
        crate::bail_invalid_basis!("cannot select thin-plate knots from empty data");
    }
    if data.iter().any(|v| !v.is_finite()) {
        crate::bail_invalid_basis!("thin-plate spline knot selection requires finite data");
    }
    if num_knots == 0 {
        crate::bail_invalid_basis!("thin-plate spline knot count must be positive");
    }
    if num_knots > n {
        crate::bail_invalid_basis!(
            "requested {} knots but only {} rows are available",
            num_knots,
            n
        );
    }

    // Rotation-equivariant maximin seed. The greedy farthest-point recursion
    // below uses ONLY Euclidean distances, which are invariant under any rigid
    // rotation of the covariates, so the only frame-dependent ingredients of
    // the selected knot set are the seed point and the tie-break. A thin-plate
    // spline is mathematically *exactly* rotation-invariant — its `r^{2m-d}`
    // (log r) kernel depends only on the pairwise distance `r`, and its
    // polynomial null space `span{1, x, …}` is mapped onto itself by any
    // orthogonal map — so rotating the data must leave the fitted surface
    // unchanged, which requires the knot SET to be rotation-invariant. The old
    // lexicographically-smallest-coordinate seed broke exactly this: a rigid
    // rotation changes which row is "lexicographically smallest", reseeding the
    // recursion at a different physical point and selecting a genuinely
    // different knot set — a 90° rotation about the centroid drifted the
    // default `thinplate(x, z)` surface by ~2% of its range while a pure row
    // permutation was bit-stable.
    //
    // Seed at the row nearest the data centroid instead. The centroid is
    // rotation-EQUIVARIANT (it rotates rigidly with the data) and the
    // nearest-row test is a Euclidean distance, so the SAME physical row is
    // chosen in every rotated frame; both are pure functions of the unordered
    // value set, so the seed also stays row-permutation invariant (gam#1378).
    //
    // The column sum is taken in CANONICAL (value-sorted) order rather than row
    // order. A plain `for i in 0..n { s += data[[i, c]] }` accumulates in the
    // data's ROW order, so floating-point round-off makes the result depend on
    // that order: a pure row permutation re-sequences the additions and shifts
    // the mean by an ulp. That ulp is enough to break the EXACT equidistance of
    // points that are symmetric about the mean (the common 1-D case), so the
    // `dist2_to_centroid` comparisons below stop reducing to the
    // value-lexicographic tie-break and the seed — and hence the whole knot set
    // — flips with row order. That is the residual ~1e-7 `s(x, bs="tp")`
    // row-permutation drift owed under gam#1378 (value-anchored `bs="cr"/"ps"`
    // stayed bit-stable because they never seed off this centroid). Sorting the
    // column values yields the identical addition sequence for every permutation
    // of the same data — all values are finite (guarded above), so `total_cmp`
    // is a total order — restoring a bit-identical, order-independent centroid.
    let centroid: Vec<f64> = (0..d)
        .map(|c| {
            let mut col: Vec<f64> = (0..n).map(|i| data[[i, c]]).collect();
            col.sort_by(|a, b| a.total_cmp(b));
            let s: f64 = col.iter().sum();
            s / n as f64
        })
        .collect();
    let dist2_to_centroid: Vec<f64> = (0..n)
        .into_par_iter()
        .map(|i| {
            let mut d2 = 0.0;
            for c in 0..d {
                let delta = data[[i, c]] - centroid[c];
                d2 += delta * delta;
            }
            d2
        })
        .collect();

    // Rotation- and permutation-invariant tie-break on a candidate's distance
    // profile to the whole support.  Lexicographic coordinate order is
    // permutation-invariant, but it is NOT rotation-invariant: on symmetric
    // clouds (regular grids, rings, centred designs) the centroid/fill-distance
    // keys often tie exactly, and a rigid rotation can change which coordinate
    // tuple is lexicographically smallest.  That reseeds the farthest-point
    // recursion with a different physical row and breaks the isotropic Duchon /
    // thin-plate equivariance contract.  The sorted multiset
    // `{‖x_i - x_l‖² : l=1..n}` is a pure function of the unordered Euclidean
    // geometry, so it survives both row permutations and rigid rotations.  Only
    // A complete tie after this key is a nontrivial symmetry orbit. No
    // permutation-equivariant rule can choose one distinct member of that
    // orbit, so callers below retain the whole class atomically *when it fits the
    // knot budget*; when a strict subset is unavoidable they cap it to the budget
    // deterministically rather than refusing the fit (see the seed/loop notes).
    // Only coincident rows are collapsed, because they generate the same kernel
    // column.
    // The profile's pairwise scalar: the squared Euclidean distance between two
    // rows. It is a pure function of the unordered geometry, so the multiset it
    // generates over the whole support survives both a rigid motion and a row
    // permutation.
    let pair_dist2 = |i: usize, j: usize| -> f64 {
        let mut distance2 = 0.0;
        for c in 0..d {
            let delta = data[[i, c]] - data[[j, c]];
            distance2 += delta * delta;
        }
        distance2
    };
    // Reduce an already-`O(1)`-tied candidate list to the rows attaining the
    // lexicographically least sorted support-distance profile. The `O(n log n)`
    // key is built once per candidate and serves both the choice and the class
    // filter; a lone candidate — the common case, and every case on data without
    // an exact symmetry — builds none at all. See
    // [`crate::basis::invariant_tie_break`] for why this is the same total
    // preorder the two-profile comparator scan applied.
    let resolve_profile_tie = |tied: &[usize], builds: &mut usize| -> Vec<usize> {
        resolve_sorted_profile_tie(n, tied, &pair_dist2, &mut |built: usize| *builds += built)
    };

    let distinct_orbit = |candidates: &[usize], already_selected: &[usize]| -> Vec<usize> {
        let mut distinct = Vec::with_capacity(candidates.len());
        'candidate: for &candidate in candidates {
            for &selected in already_selected.iter().chain(distinct.iter()) {
                let mut distance2 = 0.0;
                for c in 0..d {
                    let delta = data[[candidate, c]] - data[[selected, c]];
                    distance2 += delta * delta;
                }
                if distance2 == 0.0 {
                    continue 'candidate;
                }
            }
            distinct.push(candidate);
        }
        distinct
    };

    // Round-off-robust tie tolerance (#1818). The data's squared radius sets the
    // scale of the maximin/centroid distances; a generic rigid rotation perturbs
    // each of them by ~`ε·radius²`, so exact-equality tie-break gates let that
    // round-off — rather than the intended rotation-invariant key — decide
    // near-equidistant candidates, and a single flip cascades into a materially
    // different knot set. `tie_tol` sits well above that round-off floor and far
    // below any genuine maximin gap, so near-ties are consistently resolved by
    // the invariant support-distance profile in every rotated frame.
    //
    // The scale is the squared radius ITSELF, with no floor (gam#2750). It used
    // to be `.max(1.0)`, which compares a squared LENGTH against the
    // dimensionless number one and therefore turns the tolerance ABSOLUTE for
    // every cloud smaller than unit radius — breaking both halves of the
    // contract above at once. Measured on a 240-row 1-D chart scaled by `c`:
    // at `c = 1e-3` the squared radius is `2.7e-7`, so the floor holds `tie_tol`
    // at `1e-9` while the genuine maximin gap between neighbouring candidates is
    // `~6e-10` — the tolerance is LARGER than the gap it was required to sit far
    // below, every candidate ties, and the support-distance profile decides a
    // selection it was only supposed to referee. The selected knots then stop
    // being equivariant: the same configuration in metres and in millimetres
    // yields different knots, and hence a different median nearest-node spacing,
    // a different auto range, and a different basis.
    //
    // Without the floor every ingredient scales as `c²` — the squared distances,
    // the squared radius, and the tolerance — so the comparisons are exactly
    // invariant. A degenerate cloud (all rows coincident) gives `radius² = 0` and
    // `tie_tol = 0`, which is the right test there: every squared distance is
    // exactly zero, so exact equality already ties everything, and the previous
    // `1e-9` tied exactly the same set.
    let knot_scale2 = dist2_to_centroid.iter().copied().fold(0.0_f64, f64::max);
    let tie_tol = KNOT_MAXIMIN_TIE_REL_TOL * knot_scale2;

    // Seed = centroid-nearest row; near-equidistant rows (within `tie_tol`) are
    // resolved by the invariant support-distance profile so the seed is a
    // deterministic, rotation- and permutation-invariant function of the data.
    let seed_min = dist2_to_centroid
        .iter()
        .copied()
        .fold(f64::INFINITY, f64::min);
    let seed_tied: Vec<usize> = (0..n)
        .filter(|&i| dist2_to_centroid[i] <= seed_min + tie_tol)
        .collect();
    let seed_class = resolve_profile_tie(&seed_tied, &mut profile_builds);
    // When an indivisible symmetry orbit is larger than the entire knot budget,
    // no rule can pick an *equivariant* strict subset of it — the orbit's members
    // are interchangeable under the data's symmetry group (#2319). The previous
    // behaviour refused the fit outright, which bricks the single most common
    // gridded/lattice spatial input (an integer raster or designed grid has
    // exactly-representable coordinates, so its corner/edge orbits tie exactly and
    // exceed typical `k`). Refusing is strictly worse than a deterministic subset,
    // so we cap the orbit to the budget by taking its lowest-row members. That
    // choice is still rotation-equivariant — a rigid rotation preserves each row's
    // identity, so the base and rotated fits select corresponding physical points
    // and the knot set rotates with the data. Permutation-invariance is provably
    // unattainable for a strict subset of an exact orbit, and is knowingly traded
    // away only in that measure-zero case. Orbits that fit the budget are still
    // taken atomically (whole), preserving both invariants exactly.
    let seed_orbit: Vec<usize> = distinct_orbit(&seed_class, &[])
        .into_iter()
        .take(num_knots)
        .collect();

    let mut selected = Vec::with_capacity(num_knots);
    let mut chosen = vec![false; n];
    let mut min_dist2 = vec![f64::INFINITY; n];

    for &i in &seed_class {
        chosen[i] = true;
    }
    selected.extend(seed_orbit.iter().copied());

    min_dist2.par_iter_mut().enumerate().for_each(|(i, slot)| {
        *slot = seed_orbit
            .iter()
            .map(|&center| {
                let mut d2 = 0.0;
                for c in 0..d {
                    let delta = data[[i, c]] - data[[center, c]];
                    d2 += delta * delta;
                }
                d2
            })
            .fold(f64::INFINITY, f64::min);
    });
    for &i in &seed_class {
        min_dist2[i] = 0.0;
    }

    while selected.len() < num_knots {
        // Maximin: take the larger min-distance to the chosen set. Exact
        // `min_dist2` ties — common on regular grids and, under a generic
        // rotation, wherever round-off perturbs two near-equidistant candidates —
        // are resolved by a rotation-invariant key first (the larger distance to
        // the centroid, which spreads knots outward and is a pure function of the
        // unordered value set), and only by the invariant support-distance profile
        // for points that also tie there. Both the maximin and the centroid keys
        // use `tie_tol` (not exact equality) so sub-ulp coordinate perturbation
        // can never decide the selection; this keeps the knot SET invariant under
        // both rigid rotation and row permutation of the data.
        let max_val = min_dist2
            .par_iter()
            .enumerate()
            .filter(|(i, _)| !chosen[*i])
            .map(|(_, &cand)| cand)
            .reduce(|| f64::NEG_INFINITY, f64::max);
        if !max_val.is_finite() {
            break;
        }
        // Candidates within round-off tolerance of the maximin extremum, in
        // canonical (ascending) row order (parallel collect is index-ordered).
        let mut candidates: Vec<usize> = (0..n)
            .into_par_iter()
            .filter(|&i| !chosen[i] && min_dist2[i] >= max_val - tie_tol)
            .collect();
        if candidates.is_empty() {
            break;
        }
        // Secondary invariant key: farthest from the centroid, round-off-robust.
        let cand_max_centroid = candidates
            .iter()
            .map(|&i| dist2_to_centroid[i])
            .fold(f64::NEG_INFINITY, f64::max);
        candidates.retain(|&i| dist2_to_centroid[i] >= cand_max_centroid - tie_tol);
        // Tertiary invariant key: smallest support-distance profile. A tie
        // after every intrinsic key is an indivisible symmetry orbit. A single
        // surviving candidate has already won every refinement of the keys it
        // attained, so the profile is not built at all there.
        let candidates = resolve_profile_tie(&candidates, &mut profile_builds);
        let remaining = num_knots - selected.len();
        // Cap an oversized indivisible orbit to the remaining budget rather than
        // refusing the fit (see the seed-orbit note above): take its lowest-row
        // members, which keeps the selection rotation-equivariant and always
        // yields a fittable `num_knots`-center design. An orbit that fits is still
        // completed atomically.
        let orbit: Vec<usize> = distinct_orbit(&candidates, &selected)
            .into_iter()
            .take(remaining)
            .collect();
        for &i in &candidates {
            chosen[i] = true;
            min_dist2[i] = 0.0;
        }
        if orbit.is_empty() {
            continue;
        }
        selected.extend(orbit.iter().copied());

        min_dist2.par_iter_mut().enumerate().for_each(|(i, slot)| {
            if chosen[i] {
                return;
            }
            for &center in &orbit {
                let mut d2 = 0.0;
                for c in 0..d {
                    let delta = data[[i, c]] - data[[center, c]];
                    d2 += delta * delta;
                }
                if d2 < *slot {
                    *slot = d2;
                }
            }
        });
    }

    // A request for more knots than the data has geometrically distinct points
    // is not a malformed request — it is arithmetically unsatisfiable, and the
    // largest satisfiable answer is every distinct point there is. Refusing here
    // made a duplicate-heavy covariate a hard failure at BASIS CONSTRUCTION,
    // before the rank-reduction machinery that exists precisely to handle it
    // could see the design: `binary_outcome_shape_bms_matern_centers60_are_rank_reduced`
    // asks for 60 centers from a fixture whose PC cloud is 4 points cycled over
    // 160 rows, and its contract is that redundant centers are "rank-reduced
    // before the joint audit" — its error arm explicitly excludes the joint
    // audit's own `joint rank` / `dropped column` text.
    //
    // Clamping is what the surrounding code already assumes: `select_thin_plate_knots`
    // sizes its returned matrix by `selected.len()`, never by `num_knots`. It is
    // also the established convention for this family of libraries — mgcv reduces
    // `k` to the number of unique covariate values and warns rather than erroring.
    //
    // The diagnostic is kept, as a warning: silently handing back a smaller basis
    // than asked for would hide a genuine `centers=` typo, which is the one thing
    // the old refusal was good at.
    if selected.is_empty() {
        crate::bail_invalid_basis!(
            "thin-plate knot selection found no geometrically distinct selectable points in {} rows",
            data.nrows()
        );
    }
    if selected.len() < num_knots {
        log::warn!(
            "[thin-plate] requested {num_knots} distinct knots but the data contain only {} \
             geometrically distinct selectable points; reducing the basis to {} knots",
            selected.len(),
            selected.len()
        );
    }

    Ok((selected, profile_builds))
}

#[inline(always)]
pub(crate) fn thin_plate_kernel_from_dist2(
    dist2: f64,
    dimension: usize,
) -> Result<f64, BasisError> {
    if !dist2.is_finite() || dist2 < 0.0 {
        crate::bail_invalid_basis!("thin-plate kernel distance must be finite and non-negative");
    }
    if dist2 == 0.0 {
        return Ok(0.0);
    }
    match dimension {
        // For d≤3, the minimum penalty order m=2 (biharmonic) suffices.
        // Hand-optimized closed forms avoid the overhead of the general evaluator.
        //   d=1:  r^3
        //   d=2:  r^2 log(r)
        //   d=3: -r
        1 => Ok(dist2 * dist2.sqrt()),
        2 => Ok(0.5 * dist2 * dist2.ln()),
        3 => Ok(-dist2.sqrt()),
        _ => {
            // General case: choose the smallest penalty order m with 2m > d,
            // i.e. m = floor(d/2) + 1, and evaluate via the Duchon polyharmonic
            // kernel which handles arbitrary (m, d) combinations.
            let m = dimension / 2 + 1;
            let r = dist2.sqrt();
            Ok(polyharmonic_kernel(r, (m) as f64, dimension))
        }
    }
}

#[inline(always)]
pub(crate) fn thin_plate_penalty_order(dimension: usize) -> usize {
    match dimension {
        1..=3 => 2,
        _ => dimension / 2 + 1,
    }
}

/// True when canonical TPS is mathematically infeasible at this (d, k) — the
/// polynomial nullspace P(C) has more columns than centers, so the side
/// constraint `P(C)^T α = 0` is overdetermined and the basis collapses.
#[inline(always)]
pub(crate) fn d_canonical_tps_infeasible(dimension: usize, num_centers: usize) -> bool {
    num_centers < thin_plate_polynomial_basis_dimension(dimension)
}

/// Whether canonical thin-plate splines are infeasible at THESE specific
/// centers — the single governing feasibility test for the auto-promotion gate.
///
/// Canonical TPS requires the polynomial nullspace block `P(C)` (`k × M(d)`) to
/// have full column rank `M(d)`; otherwise the side constraint `P(C)ᵀα = 0` is
/// overdetermined (count-short) or rank-deficient (degenerate geometry), and
/// `thin_plate_kernel_constraint_nullspace` hard-errors. There are two failure
/// modes and rank subsumes both:
///   * too few centers — `k < M(d)` (the cheap count short-circuit, which also
///     avoids materialising an oversized `k × M(d)` block when `M(d)` explodes
///     in high dimension, e.g. `M(16) = 735_471`); and
///   * enough centers but geometrically DEGENERATE — the selected centers are
///     affinely/polynomially dependent, so `rank P(C) < M(d)` even though
///     `k ≥ M(d)` (e.g. coplanar points in 3-D).
///
/// The prior gate tested only the count, so a degenerate-but-sufficient center
/// set slipped past it into canonical TPS and hard-errored instead of promoting
/// to the Duchon generalisation (which handles a rank-deficient nullspace
/// gracefully — it takes the RRQR nullspace at the *actual* rank and downgrades
/// the effective nullspace order). Making rank the test keeps the promotion gate
/// from drifting out of sync with the linear-algebra feasibility the builder
/// enforces downstream.
pub(crate) fn thin_plate_canonical_infeasible_at_centers(centers: ArrayView2<'_, f64>) -> bool {
    let dimension = centers.ncols();
    // Cheap count short-circuit; also guards high `d`, where forming the
    // `k × M(d)` polynomial block is itself intractable.
    if d_canonical_tps_infeasible(dimension, centers.nrows()) {
        return true;
    }
    // Enough centers by count (`M(d) ≤ k`), so the block is small enough to
    // form: check the ACTUAL rank so a degenerate center geometry promotes to
    // Duchon rather than hard-erroring in canonical TPS.
    let poly_block = thin_plate_polynomial_block(centers);
    let poly_cols = poly_block.ncols();
    match rrqr_nullspace_basis(&poly_block, default_rrqr_rank_alpha()) {
        Ok((_, rank)) => rank < poly_cols,
        // If the rank probe itself fails, defer to the canonical path, which
        // surfaces a precise error rather than silently promoting.
        Err(_) => false,
    }
}

/// Pick Duchon parameters for the TPS auto-promotion at infeasible (d, k).
/// Returns `Some((nullspace_order, power))` when a hybrid-Duchon spec exists
/// satisfying the collocation gate `2(p + s) > d + max_op` for max_op = 2
/// (default operator penalties: mass + tension + stiffness). The hybrid
/// kernel (Matern-blended) sidesteps the pure-Duchon `2s < d` gate, leaving
/// only the collocation/spectral-existence condition.
///
/// Strategy: prefer Linear nullspace (M' = d+1) so the polynomial trend
/// retains the affine span; fall back to Zero (M' = 1) when k < d+1. The
/// smallest admissible s in each case gives the most TPS-like behavior
/// (largest spectral roughness for a given polynomial nullspace).
pub(crate) fn duchon_thin_plate_fallback_params(
    dimension: usize,
    num_centers: usize,
) -> Option<(DuchonNullspaceOrder, usize)> {
    let d = dimension;
    let max_op = 2usize; // mass + tension + stiffness collocation
    for (order, p, m_poly) in [
        (DuchonNullspaceOrder::Linear, 2usize, d + 1),
        (DuchonNullspaceOrder::Zero, 1usize, 1usize),
    ] {
        if num_centers < m_poly {
            continue;
        }
        // Smallest integer s with 2(p + s) > d + max_op.
        let target = d + max_op;
        let s_min = if 2 * p > target {
            0
        } else {
            (target - 2 * p) / 2 + 1
        };
        return Some((order, s_min));
    }
    None
}

/// Length scale at which the auto-promoted hybrid-Duchon kernel is well
/// conditioned: the typical separation between centers.
///
/// The hybrid spectrum `||w||^(2p)·(kappa²+||w||²)^s` produces real-space
/// partial-fraction coefficients that scale as `length_scale^(2(p+s-n))`
/// (`duchon_partial_fraction_coeffs`). To keep every block O(1), `kappa·r`
/// must be O(1) at the center separations the kernel actually evaluates on,
/// i.e. `length_scale ≈ typical center distance`. We use the geometric mean
/// of the min and max pairwise center distances — robust to a few clustered
/// or far-flung centers and exactly the scale where the kernel's smooth and
/// Matern-tail parts are both resolved. Falls back to the requested length
/// scale when fewer than two distinct centers exist (no pairwise distance).
pub(crate) fn hybrid_duchon_promotion_length_scale(
    centers: ArrayView2<'_, f64>,
    requested_length_scale: f64,
) -> f64 {
    match pairwise_distance_bounds_sampled(centers) {
        Some((r_min, r_max)) => {
            // Geometric mean keeps the scale between the tightest and widest
            // center pairs; both are positive and finite by construction.
            (r_min * r_max).sqrt()
        }
        None => {
            if requested_length_scale.is_finite() && requested_length_scale > 0.0 {
                requested_length_scale
            } else {
                1.0
            }
        }
    }
}

#[inline(always)]
pub(crate) fn thin_plate_kernel_triplet_from_scaled_distance(
    scaled_distance: f64,
    dimension: usize,
) -> Result<(f64, f64, f64), BasisError> {
    if !scaled_distance.is_finite() || scaled_distance < 0.0 {
        crate::bail_invalid_basis!("thin-plate scaled distance must be finite and non-negative");
    }
    if scaled_distance == 0.0 {
        return Ok((0.0, 0.0, 0.0));
    }

    match dimension {
        1 => {
            let value = scaled_distance.powi(3);
            let first = 3.0 * scaled_distance.powi(2);
            let second = 6.0 * scaled_distance;
            Ok((value, first, second))
        }
        2 => {
            let log_r = scaled_distance.max(1e-300).ln();
            let value = scaled_distance.powi(2) * log_r;
            let first = 2.0 * scaled_distance * log_r + scaled_distance;
            let second = 2.0 * log_r + 3.0;
            Ok((value, first, second))
        }
        3 => Ok((-scaled_distance, -1.0, 0.0)),
        _ => polyharmonic_kernel_triplet(
            scaled_distance,
            thin_plate_penalty_order(dimension) as f64,
            dimension,
        ),
    }
}

#[inline(always)]
pub(crate) fn thin_plate_kernel_psi_triplet_from_distance(
    distance: f64,
    length_scale: f64,
    dimension: usize,
) -> Result<(f64, f64, f64), BasisError> {
    if !distance.is_finite() || distance < 0.0 {
        crate::bail_invalid_basis!("thin-plate kernel distance must be finite and non-negative");
    }
    if !length_scale.is_finite() || length_scale <= 0.0 {
        crate::bail_invalid_basis!("thin-plate length_scale must be finite and positive");
    }

    // ThinPlate psi-derivative convention:
    // the optimizer uses psi = log(kappa) = -log(length_scale), so the scaled
    // radial argument is
    //   r(psi) = ||x - c|| / length_scale = ||x - c|| * exp(psi).
    //
    // Therefore
    //   dr/dpsi     = r
    //   d²r/dpsi²   = r
    //
    // and for any TPS radial kernel phi(r),
    //   d phi / dpsi       = phi_r(r) * r
    //   d²phi / dpsi²      = phi_rr(r) * r² + phi_r(r) * r.
    //
    // This is exactly the chain rule requested by the math spec, translated to
    // the code's stored inverse-length-scale parameterization.
    let scaled_distance = distance / length_scale;
    let (value, radial_first, radial_second) =
        thin_plate_kernel_triplet_from_scaled_distance(scaled_distance, dimension)?;
    let psi = radial_first * scaled_distance;
    let psi_psi = radial_second * scaled_distance * scaled_distance + psi;
    Ok((value, psi, psi_psi))
}

/// Creates a thin-plate regression spline basis from data and knot locations.
///
/// # Arguments
/// * `data` - `n x d` matrix of evaluation points
/// * `knots` - `k x d` matrix of knot locations
///
/// # Returns
/// `ThinPlateSplineBasis` containing:
/// - `basis`: `n x (k_c + M)` matrix (`[K_c | P]`) where `M` is the TPS
///   polynomial null-space dimension for the selected ambient dimension
/// - `penalty_bending`: constrained TPS curvature penalty
/// - `penalty_ridge`: center-metric penalty for null-function shrinkage
pub fn create_thin_plate_spline_basis(
    data: ArrayView2<f64>,
    knots: ArrayView2<f64>,
) -> Result<ThinPlateSplineBasis, BasisError> {
    let mut workspace = BasisWorkspace::default();
    create_thin_plate_spline_basiswithworkspace(data, knots, &mut workspace)
}

pub fn create_thin_plate_spline_basiswithworkspace(
    data: ArrayView2<f64>,
    knots: ArrayView2<f64>,
    workspace: &mut BasisWorkspace,
) -> Result<ThinPlateSplineBasis, BasisError> {
    create_thin_plate_spline_basis_scaledwithworkspace(data, knots, 1.0, None, workspace)
}

/// Evaluates a thin-plate basis at `data` in a radial chart supplied by the
/// caller, rather than one chosen from `data` itself.
///
/// [`create_thin_plate_spline_basis`] selects its radial chart `V` from the rows
/// it is handed: since #1347 the reparameterization is taken in the *realized
/// data metric* `G_c = (K Z)ᵀ (K Z)`, so two different row sets over the same
/// knots yield two different `V`, and the design columns `Φ Z V` are two
/// different coordinate systems for the same model space. A coefficient vector
/// fitted against one therefore does not describe the same function against the
/// other — silently, since both designs have the same shape.
///
/// Scoring a fit on rows it was not fitted to must consequently replay the
/// training chart: pass the [`ThinPlateSplineBasis::radial_reparam`] of the
/// basis the coefficients were fitted against. The knots must be the same ones,
/// as usual; the chart is checked against the side-constrained radial dimension
/// and a mismatch is a typed error rather than a wrong answer.
pub fn create_thin_plate_spline_basis_in_chart(
    data: ArrayView2<f64>,
    knots: ArrayView2<f64>,
    radial_reparam: &Array2<f64>,
) -> Result<ThinPlateSplineBasis, BasisError> {
    let mut workspace = BasisWorkspace::default();
    create_thin_plate_spline_basis_scaledwithworkspace(
        data,
        knots,
        1.0,
        Some(radial_reparam),
        &mut workspace,
    )
}

pub(crate) fn create_thin_plate_spline_basis_scaledwithworkspace(
    data: ArrayView2<f64>,
    knots: ArrayView2<f64>,
    length_scale: f64,
    frozen_radial_reparam: Option<&Array2<f64>>,
    workspace: &mut BasisWorkspace,
) -> Result<ThinPlateSplineBasis, BasisError> {
    let n = data.nrows();
    let k = knots.nrows();
    let d = data.ncols();

    if d == 0 {
        crate::bail_invalid_basis!("thin-plate spline requires at least one covariate dimension");
    }
    if d != knots.ncols() {
        crate::bail_dim_basis!(
            "thin-plate spline dimension mismatch: data has {} columns, knots have {} columns",
            d,
            knots.ncols()
        );
    }
    let poly_cols = thin_plate_polynomial_basis_dimension(d);
    if k < poly_cols {
        crate::bail_invalid_basis!(
            "thin-plate spline requires at least {} knots to span the degree-{} polynomial null space in dimension {}; got {}",
            poly_cols,
            thin_plate_polynomial_degree(d),
            d,
            k
        );
    }
    if data.iter().any(|v| !v.is_finite()) || knots.iter().any(|v| !v.is_finite()) {
        crate::bail_invalid_basis!("thin-plate spline requires finite data and knot values");
    }
    if !length_scale.is_finite() || length_scale <= 0.0 {
        crate::bail_invalid_basis!("thin-plate length_scale must be finite and positive");
    }

    // Translation-invariant frame (#1269). The thin-plate kernel reads only
    // coordinate *differences* `data − knots`, so it is already invariant to a
    // covariate translation `x → x + c`; the polynomial null-space block
    // `P = {1, x, x², …}` and the side-constraint nullspace `P(knots)ᵀα = 0`,
    // however, are assembled at the *absolute* coordinate. When the covariate is
    // offset (e.g. a centred-vs-raw "year", or this term's standardized axis
    // carrying a large mean), the `{1, x}` columns become near-collinear, the
    // design ill-conditions, and REML λ-selection lands in a slightly different
    // basin — moving the fit by ~1% of signal range even though the model space
    // is identical (`{1, x − x̄}` spans the same null space). Subtract the knot
    // cloud's per-axis mean from both `data` and `knots` so the polynomial block
    // is built in a location-standardized, well-conditioned frame. The knots are
    // frozen (`UserProvided`) after fit, so this offset is identical at predict;
    // and under `x → x + c` the knots (selected from the data) shift by the same
    // `c`, so the centred coordinate — hence the whole basis — is invariant.
    let knot_mean: Vec<f64> = (0..d)
        .map(|c| knots.column(c).sum() / (k.max(1) as f64))
        .collect();
    let mut data_centered = data.to_owned();
    let mut knots_centered = knots.to_owned();
    for c in 0..d {
        let mu = knot_mean[c];
        data_centered.column_mut(c).mapv_inplace(|v| v - mu);
        knots_centered.column_mut(c).mapv_inplace(|v| v - mu);
    }
    let data = data_centered.view();
    let knots = knots_centered.view();

    // K block: radial basis evaluations data -> knots
    let mut kernel_block = Array2::<f64>::zeros((n, k));
    let kernel_result: Result<(), BasisError> = kernel_block
        .axis_iter_mut(Axis(0))
        .into_par_iter()
        .enumerate()
        .try_for_each(|(i, mut row)| {
            for j in 0..k {
                let mut dist2 = 0.0;
                for c in 0..d {
                    let delta = data[[i, c]] - knots[[j, c]];
                    dist2 += delta * delta;
                }
                row[j] = thin_plate_kernel_from_dist2(dist2 / (length_scale * length_scale), d)?;
            }
            Ok(())
        });
    kernel_result?;

    // P block: all TPS null-space monomials of total degree < m.
    let poly_block = thin_plate_polynomial_block(data);

    // Omega block on knots
    let mut omega = Array2::<f64>::zeros((k, k));
    let length_scale_sq = length_scale * length_scale;
    fill_symmetric_from_row_kernel(&mut omega, |i, j| {
        let mut dist2 = 0.0;
        for c in 0..d {
            let delta = knots[[i, c]] - knots[[j, c]];
            dist2 += delta * delta;
        }
        thin_plate_kernel_from_dist2(dist2 / length_scale_sq, d)
    })?;

    // Enforce TPS side-constraint P(knots)^T α = 0 by projecting onto
    // the nullspace of P(knots)^T.
    let z = thin_plate_kernel_constraint_nullspace(knots, &mut workspace.cache)?;
    let kernel_constrained = fast_ab(&kernel_block, &z);
    let omega_constrained = {
        let zt_o = fast_atb(&z, &omega);
        symmetrize_penalty(&fast_ab(&zt_o, &z))
    };
    let omega_psd = validate_psd_penalty(
        &omega_constrained,
        &format!("thin_plate bending penalty (dimension={d})"),
        "thin-plate kernel and side-constraint assembly must yield a PSD penalty on the constrained subspace",
    )?;
    assert!(
        omega_psd.min_eigenvalue >= -omega_psd.tolerance,
        "thin-plate constrained penalty PSD validation violated tolerance after validation: min_eigenvalue={}, tolerance={}",
        omega_psd.min_eigenvalue,
        omega_psd.tolerance
    );
    assert!(
        omega_psd.max_abs_eigenvalue.is_finite(),
        "thin-plate constrained penalty has non-finite max eigenvalue after validation: max_abs_eigenvalue={}",
        omega_psd.max_abs_eigenvalue
    );
    assert!(
        omega_psd.effective_rank <= omega_constrained.nrows(),
        "thin-plate constrained penalty rank exceeds constrained rows: effective_rank={}, rows={}",
        omega_psd.effective_rank,
        omega_constrained.nrows()
    );

    let constrained_kernel_cols = kernel_constrained.ncols();

    // Radial penalty eigenspace reparameterization. Eigendecompose
    // Ω_constrained = V Λ V' and rotate the radial design columns into the
    // same basis. This preserves the TPS model space while making the bending
    // block diagonal. Numerically near-null radial directions are not part of
    // the polynomial null space; keeping them as almost-free columns lets REML
    // spend EDF on wiggle with effectively zero curvature cost (#1271). Drop
    // them from the exposed basis so only genuinely penalized radial directions
    // remain.
    let (radial_reparam, radial_eigvals): (Array2<f64>, Array1<f64>) = if let Some(frozen) =
        frozen_radial_reparam
    {
        if frozen.nrows() != constrained_kernel_cols {
            crate::bail_dim_basis!(
                "thin-plate frozen radial reparam shape {:?} does not match constrained radial dimension {}",
                frozen.dim(),
                constrained_kernel_cols
            );
        }
        let v = frozen.to_owned();
        let vt_omega_v = fast_atb(&v, &omega_constrained);
        let lambda_diag = fast_ab(&vt_omega_v, &v);
        let mut evals = Array1::<f64>::zeros(v.ncols());
        for i in 0..v.ncols() {
            evals[i] = lambda_diag[[i, i]].max(0.0);
        }
        (v, evals)
    } else if constrained_kernel_cols == 0 {
        (Array2::<f64>::zeros((0, 0)), Array1::<f64>::zeros(0))
    } else {
        // #1347: reparameterize in the realized data metric so the bending
        // spectrum acquires mgcv's cliff (curvature per unit data-variance),
        // rather than the cliff-less raw knot-Gram spectrum that lets REML buy
        // near-free wiggle on near-linear data. G_c = (K Z)ᵀ (K Z).
        // Canonical row order so the Gram is row-permutation invariant (#1378).
        let design_gram = data_metric_design_gram(kernel_constrained.view());
        thin_plate_radial_reparam_data_metric(&omega_constrained, &design_gram)?
    };
    let kernel_cols = radial_eigvals.len();
    let total_cols = kernel_cols + poly_cols;

    let kernel_rotated = if kernel_cols == 0 {
        Array2::<f64>::zeros((n, 0))
    } else {
        fast_ab(&kernel_constrained, &radial_reparam)
    };

    let mut basis = Array2::<f64>::zeros((n, total_cols));
    basis
        .slice_mut(s![.., 0..kernel_cols])
        .assign(&kernel_rotated);
    basis.slice_mut(s![.., kernel_cols..]).assign(&poly_block);

    let mut penalty_bending = Array2::<f64>::zeros((total_cols, total_cols));
    for i in 0..kernel_cols {
        penalty_bending[[i, i]] = radial_eigvals[i];
    }
    // Evaluate the active raw chart on its frozen knot support.  The resulting
    // Gram is a compact domain quadrature for the represented function, so the
    // double penalty measures the L2 size of the polynomial/null component
    // instead of the arbitrary Euclidean size of its coefficient vector.
    let center_kernel_rotated = if kernel_cols == 0 {
        Array2::<f64>::zeros((k, 0))
    } else {
        fast_ab(&fast_ab(&omega, &z), &radial_reparam)
    };
    let center_poly = thin_plate_polynomial_block(knots);
    let mut center_design = Array2::<f64>::zeros((k, total_cols));
    center_design
        .slice_mut(s![.., 0..kernel_cols])
        .assign(&center_kernel_rotated);
    center_design
        .slice_mut(s![.., kernel_cols..])
        .assign(&center_poly);
    let function_gram = symmetrize_penalty(&fast_ata(&center_design));
    let penalty_ridge = function_space_nullspace_shrinkage(&penalty_bending, &function_gram)?
        .unwrap_or_else(|| Array2::<f64>::zeros((total_cols, total_cols)));

    Ok(ThinPlateSplineBasis {
        basis,
        penalty_bending,
        penalty_ridge,
        num_kernel_basis: kernel_cols,
        num_polynomial_basis: poly_cols,
        dimension: d,
        radial_reparam,
    })
}

pub(crate) fn active_thin_plate_penalty_derivatives(
    penalties: &[ActivePenalty],
    primary_derivative: &Array2<f64>,
    nullspace_derivative: &Array2<f64>,
) -> Result<Vec<Array2<f64>>, BasisError> {
    penalties
        .iter()
        .map(|penalty| match &penalty.info.source {
            PenaltySource::Primary => Ok(primary_derivative.clone()),
            PenaltySource::DoublePenaltyNullspace => Ok(nullspace_derivative.clone()),
            other => Err(BasisError::InvalidInput(format!(
                "unexpected ThinPlate penalty source in psi-derivative path: {other:?}"
            ))),
        })
        .collect()
}

// The dense per-pair ThinPlate ψ-derivative builder used to live here. It has
// been replaced by `build_thin_plate_scalar_design_psi_derivatives`, which
// drives the same math through the shared scalar streaming infrastructure
// (`build_scalar_design_psi_derivatives_shared`) so large-scale TPS terms no
// longer materialize dense `(n × p)` first/second derivative arrays.

pub fn build_thin_plate_penalty_psi_derivativeswithworkspace(
    centers: ArrayView2<'_, f64>,
    spec: &ThinPlateBasisSpec,
    identifiability_transform: Option<&Array2<f64>>,
    workspace: &mut BasisWorkspace,
) -> Result<(Array2<f64>, Array2<f64>, Array2<f64>, Array2<f64>), BasisError> {
    // Match build_thin_plate_basis exactly (Wood-TPRS path):
    //
    //   M(ψ)        = Z_kernel^T Ω(ψ) Z_kernel
    //   V, Λ(ψ)    = eigh(M)        (or V from spec.radial_reparam, frozen)
    //   S_raw(ψ)   = pad(diag(Λ(ψ)), total_cols)         // kernel block + zero poly
    //   S_norm(ψ)  = S_raw(ψ) / ||S_raw(ψ)||_F
    //   S_final(ψ) = Z_id^T S_norm(ψ) Z_id              // identifiability transform
    //
    // where Ω_ij(ψ) = φ(r_ij(ψ)), r_ij(ψ) = ||c_i - c_j|| · exp(ψ).
    //
    // We need d/dψ S_final and d²/dψ² S_final, applied in the same composition
    // order as the build path so the analytic derivative is of the exact
    // materialized penalty surface.
    let z_kernel = thin_plate_kernel_constraint_nullspace(centers, &mut workspace.cache)?;
    let constrained_kernel_cols = z_kernel.ncols();
    let poly_cols = thin_plate_polynomial_basis_dimension(centers.ncols());
    let k = centers.nrows();
    let d = centers.ncols();

    // 1) Build Ω, Ω_ψ, Ω_ψψ on centers (k × k). Ω is needed to recover Λ when
    //    V is frozen and to apply Hellmann-Feynman in the fresh-V path.
    let mut omega = Array2::<f64>::zeros((k, k));
    let mut omega_psi = Array2::<f64>::zeros((k, k));
    let mut omega_psi_psi = Array2::<f64>::zeros((k, k));

    // Evaluate the dense symmetric center-pair kernel blocks in independent
    // lower-triangular row tiles. Each rayon worker owns its tile-local entry
    // buffer (scratch workspace) and returns immutable results; the serial
    // assembly below is the only place that writes to the dense output arrays,
    // so no mutable ndarray storage is shared across workers.
    struct ThinPlatePsiTileEntry {
        pub(crate) i: usize,
        pub(crate) j: usize,
        pub(crate) phi: f64,
        pub(crate) phi_psi: f64,
        pub(crate) phi_psi_psi: f64,
    }

    let n_tiles = k.div_ceil(THIN_PLATE_PENALTY_PSI_TILE_ROWS);
    let omega_tiles: Result<Vec<Vec<ThinPlatePsiTileEntry>>, BasisError> = (0..n_tiles)
        .into_par_iter()
        .map(|tile_idx| {
            let row_start = tile_idx * THIN_PLATE_PENALTY_PSI_TILE_ROWS;
            let row_end = (row_start + THIN_PLATE_PENALTY_PSI_TILE_ROWS).min(k);
            let tile_pairs = (row_start..row_end).map(|i| i + 1).sum::<usize>();
            let mut entries = Vec::with_capacity(tile_pairs);
            for i in row_start..row_end {
                for j in 0..=i {
                    let mut dist2 = 0.0;
                    for axis in 0..d {
                        let delta = centers[[i, axis]] - centers[[j, axis]];
                        dist2 += delta * delta;
                    }
                    let (phi, phi_psi, phi_psi_psi) = thin_plate_kernel_psi_triplet_from_distance(
                        dist2.sqrt(),
                        spec.length_scale,
                        d,
                    )?;
                    entries.push(ThinPlatePsiTileEntry {
                        i,
                        j,
                        phi,
                        phi_psi,
                        phi_psi_psi,
                    });
                }
            }
            Ok(entries)
        })
        .collect();

    for tile in omega_tiles? {
        for entry in tile {
            omega[[entry.i, entry.j]] = entry.phi;
            omega_psi[[entry.i, entry.j]] = entry.phi_psi;
            omega_psi_psi[[entry.i, entry.j]] = entry.phi_psi_psi;
            if entry.i != entry.j {
                omega[[entry.j, entry.i]] = entry.phi;
                omega_psi[[entry.j, entry.i]] = entry.phi_psi;
                omega_psi_psi[[entry.j, entry.i]] = entry.phi_psi_psi;
            }
        }
    }

    // 2) Project to the constrained kernel space.
    let m_constrained = symmetrize_penalty(&z_kernel.t().dot(&omega).dot(&z_kernel));
    let m_psi_constrained = symmetrize_penalty(&z_kernel.t().dot(&omega_psi).dot(&z_kernel));
    let m_pp_constrained = symmetrize_penalty(&z_kernel.t().dot(&omega_psi_psi).dot(&z_kernel));

    // 3) Get V (frozen or fresh from eigh).
    let (v, lambda) = if let Some(frozen) = spec.radial_reparam.as_ref() {
        if frozen.nrows() != constrained_kernel_cols {
            crate::bail_dim_basis!(
                "thin-plate frozen radial reparam shape {:?} does not match constrained radial dimension {}",
                frozen.dim(),
                constrained_kernel_cols
            );
        }
        let v_owned = frozen.to_owned();
        let lambda_diag = fast_ab(&fast_atb(&v_owned, &m_constrained), &v_owned);
        let mut evals = Array1::<f64>::zeros(v_owned.ncols());
        for i in 0..v_owned.ncols() {
            evals[i] = lambda_diag[[i, i]].max(0.0);
        }
        (v_owned, evals)
    } else if constrained_kernel_cols == 0 {
        (Array2::<f64>::zeros((0, 0)), Array1::<f64>::zeros(0))
    } else {
        let (mut evals, evecs) =
            FaerEigh::eigh(&m_constrained, Side::Lower).map_err(BasisError::LinalgError)?;
        for ev in evals.iter_mut() {
            if *ev < 0.0 {
                *ev = 0.0;
            }
        }
        let keep = thin_plate_retained_radial_indices(&evals);
        (evecs.select(Axis(1), &keep), evals.select(Axis(0), &keep))
    };
    let kernel_cols = lambda.len();
    let total_cols = kernel_cols + poly_cols;
    let v_is_frozen = spec.radial_reparam.is_some();

    // 4) Rotate the constrained-space derivatives into V's basis. These are the
    //    coefficients used by Hellmann-Feynman / standard perturbation theory:
    //      A_ψ[i,j]  = v_i^T M_ψ  v_j
    //      A_ψψ[i,j] = v_i^T M_ψψ v_j.
    let a_psi = if kernel_cols > 0 {
        v.t().dot(&m_psi_constrained).dot(&v)
    } else {
        Array2::<f64>::zeros((0, 0))
    };
    let a_pp = if kernel_cols > 0 {
        v.t().dot(&m_pp_constrained).dot(&v)
    } else {
        Array2::<f64>::zeros((0, 0))
    };

    // 5) Build the un-normalized rotated penalty and its ψ-derivatives.
    //
    //    Frozen V (predict-time): the penalty is V^T M(ψ) V — a full kc×kc
    //    matrix that equals diag(Λ_0) only at fit-time ψ_0. Its ψ-derivatives
    //    are simply A_ψ and A_ψψ (full matrices).
    //
    //    Fresh V (fit-time, no frozen reparam): V(ψ) re-diagonalizes M(ψ) at
    //    each ψ, so the penalty is identically diag(Λ(ψ)). Off-diagonals
    //    vanish at every ψ; on-diagonals follow from non-degenerate eigenvalue
    //    perturbation:
    //      dΛ_i/dψ   = A_ψ[i,i]
    //      d²Λ_i/dψ² = A_ψψ[i,i] + 2 Σ_{k ≠ i} A_ψ[i,k]² / (Λ_i − Λ_k)
    //    For degenerate eigenvalues the off-diagonal correction is dropped on
    //    the offending pairs (their contribution is encoded in subspace
    //    rotations rather than scalar eigenvalue motion).
    let s_raw_kernel = Array2::from_diag(&lambda);
    let s_raw_psi_kernel = if v_is_frozen {
        a_psi.clone()
    } else {
        let mut diag = Array2::<f64>::zeros((kernel_cols, kernel_cols));
        for i in 0..kernel_cols {
            diag[[i, i]] = a_psi[[i, i]];
        }
        diag
    };
    let s_raw_pp_kernel = if v_is_frozen {
        a_pp.clone()
    } else {
        let mut diag = Array2::<f64>::zeros((kernel_cols, kernel_cols));
        for i in 0..kernel_cols {
            let mut acc = a_pp[[i, i]];
            for k_idx in 0..kernel_cols {
                if k_idx == i {
                    continue;
                }
                let denom = lambda[i] - lambda[k_idx];
                if denom.abs() > 1e-14 {
                    acc += 2.0 * a_psi[[i, k_idx]].powi(2) / denom;
                }
            }
            diag[[i, i]] = acc;
        }
        diag
    };

    // 6) Pad to total_cols (poly block has zero penalty).
    let pad = |kernel_block: &Array2<f64>| -> Array2<f64> {
        let mut s = Array2::<f64>::zeros((total_cols, total_cols));
        if kernel_cols > 0 {
            s.slice_mut(s![0..kernel_cols, 0..kernel_cols])
                .assign(kernel_block);
        }
        s
    };
    let s_raw = pad(&s_raw_kernel);
    let s_raw_psi = pad(&s_raw_psi_kernel);
    let s_raw_pp = pad(&s_raw_pp_kernel);

    // 7) Apply the Frobenius normalization chain rule. The build path divides
    //    by c(ψ)=||S_raw(ψ)||_F before applying the identifiability transform.
    //    Therefore:
    //      S_norm'  = S_raw'/c - c' S_raw/c²
    //      S_norm'' = S_raw''/c - 2c' S_raw'/c²
    //                  + (2(c')²/c³ - c''/c²) S_raw,
    //    exactly as implemented by `normalize_penaltywith_psi_derivatives`.
    let (_, s_norm_psi, s_norm_pp, _c) =
        normalize_penaltywith_psi_derivatives(&s_raw, &s_raw_psi, &s_raw_pp);

    // 8) Apply the identifiability transform last (matches build path order:
    //    `if let Some(z) = ... { Z^T penalty_norm Z }`).
    let s_psi_out = project_penalty_matrix(&s_norm_psi, identifiability_transform);
    let s_psi_psi_out = project_penalty_matrix(&s_norm_pp, identifiability_transform);

    // 9) Differentiate the double penalty in the same compact function metric
    // used by the value path.  The frozen center support is an n-independent
    // quadrature for this regression-spline chart.  With V and the outer
    // identifiability chart frozen at the base point, its value design and
    // derivatives are
    //
    //   B    = [Omega Z V | P(C)] T,
    //   B_p  = [Omega_p Z V | 0] T,
    //   B_pp = [Omega_pp Z V | 0] T.
    //
    // Therefore G=B'B follows the exact product rule.  The target frame is
    // structural: coefficients whose kernel coordinates vanish after T, i.e.
    // the surviving polynomial-function subspace.  Differentiating
    // G N (N' G N)^-1 N' G then gives the analytic ridge derivatives; no
    // eigenspace derivative, finite difference, or coefficient-space projector
    // enters this path.
    let kernel_transform = fast_ab(&z_kernel, &v);
    let center_kernel = fast_ab(&omega, &kernel_transform);
    let center_kernel_psi = fast_ab(&omega_psi, &kernel_transform);
    let center_kernel_pp = fast_ab(&omega_psi_psi, &kernel_transform);
    let center_mean: Vec<f64> = (0..d)
        .map(|axis| centers.column(axis).sum() / k.max(1) as f64)
        .collect();
    let mut centered = centers.to_owned();
    for axis in 0..d {
        let mean = center_mean[axis];
        centered.column_mut(axis).mapv_inplace(|value| value - mean);
    }
    let center_poly = thin_plate_polynomial_block(centered.view());
    let mut center_design = Array2::<f64>::zeros((k, total_cols));
    let mut center_design_psi = Array2::<f64>::zeros((k, total_cols));
    let mut center_design_pp = Array2::<f64>::zeros((k, total_cols));
    center_design
        .slice_mut(s![.., 0..kernel_cols])
        .assign(&center_kernel);
    center_design
        .slice_mut(s![.., kernel_cols..])
        .assign(&center_poly);
    center_design_psi
        .slice_mut(s![.., 0..kernel_cols])
        .assign(&center_kernel_psi);
    center_design_pp
        .slice_mut(s![.., 0..kernel_cols])
        .assign(&center_kernel_pp);

    let (center_design, center_design_psi, center_design_pp, null_frame) =
        if let Some(transform) = identifiability_transform {
            if transform.nrows() != total_cols {
                crate::bail_dim_basis!(
                    "thin-plate identifiability transform has {} rows, expected {}",
                    transform.nrows(),
                    total_cols
                );
            }
            let kernel_coordinate_map = transform.slice(s![0..kernel_cols, ..]).to_owned();
            let (frame, _) = rrqr_nullspace_basis(
                &kernel_coordinate_map.t().to_owned(),
                default_rrqr_rank_alpha(),
            )
            .map_err(BasisError::LinalgError)?;
            (
                fast_ab(&center_design, transform),
                fast_ab(&center_design_psi, transform),
                fast_ab(&center_design_pp, transform),
                frame,
            )
        } else {
            let mut frame = Array2::<f64>::zeros((total_cols, poly_cols));
            for column in 0..poly_cols {
                frame[[kernel_cols + column, column]] = 1.0;
            }
            (center_design, center_design_psi, center_design_pp, frame)
        };
    let gram = symmetrize_penalty(&fast_ata(&center_design));
    let gram_psi = symmetrize_penalty(
        &(fast_atb(&center_design_psi, &center_design)
            + fast_atb(&center_design, &center_design_psi)),
    );
    let gram_pp = symmetrize_penalty(
        &(fast_atb(&center_design_pp, &center_design)
            + fast_atb(&center_design_psi, &center_design_psi).mapv(|value| 2.0 * value)
            + fast_atb(&center_design, &center_design_pp)),
    );
    let ridge_jet = function_space_subspace_shrinkage_derivatives(
        &null_frame,
        &gram,
        &gram_psi,
        &gram_psi,
        &gram_pp,
    )?;
    let (_, ridge_psi, ridge_pp, _) = normalize_penaltywith_psi_derivatives(
        &ridge_jet.value,
        &ridge_jet.first_a,
        &ridge_jet.mixed,
    );

    Ok((s_psi_out, s_psi_psi_out, ridge_psi, ridge_pp))
}

/// Build the design ψ-derivatives for a Thin-Plate Spline term via the shared
/// scalar streaming infrastructure that Duchon already uses at large scale.
///
/// At small `n` this materializes both the first and second derivative arrays
/// just like the legacy dense path; at large scale the policy elects
/// streaming and both arrays come back as zero-sized — only an
/// `ImplicitDesignPsiDerivative` is returned, and downstream consumers
/// (`spatial_log_kappa_hyper_dirs_frominfo_list`) dispatch matvecs through it
/// instead of materializing dense `(n × p)` arrays per axis.
pub(crate) fn build_thin_plate_scalar_design_psi_derivatives(
    data: ArrayView2<'_, f64>,
    centers: ArrayView2<'_, f64>,
    spec: &ThinPlateBasisSpec,
    identifiability_transform: Option<&Array2<f64>>,
    workspace: &mut BasisWorkspace,
) -> Result<ScalarDesignPsiDerivatives, BasisError> {
    let z_kernel = thin_plate_kernel_constraint_nullspace(centers, &mut workspace.cache)?;
    let constrained_kernel_cols = z_kernel.ncols();
    let kernel_transform = if let Some(v) = spec.radial_reparam.as_ref() {
        if v.nrows() != constrained_kernel_cols {
            crate::bail_dim_basis!(
                "thin-plate radial reparam shape {:?} does not match constrained radial dimension {}",
                v.dim(),
                constrained_kernel_cols
            );
        }
        fast_ab(&z_kernel, v)
    } else {
        z_kernel
    };
    let kernel_cols = kernel_transform.ncols();
    let poly_cols = thin_plate_polynomial_basis_dimension(data.ncols());
    let p_after_pad = kernel_cols + poly_cols;
    let p_final = identifiability_transform
        .map(|zf| zf.ncols())
        .unwrap_or(p_after_pad);
    build_scalar_design_psi_derivatives_shared(
        data,
        centers,
        None,
        p_final,
        Some(kernel_transform),
        identifiability_transform.cloned(),
        poly_cols,
        RadialScalarKind::ThinPlate {
            length_scale: spec.length_scale,
            dim: data.ncols(),
        },
        0.0,
    )
}

pub fn build_thin_plate_basis_log_kappa_derivative(
    data: ArrayView2<'_, f64>,
    spec: &ThinPlateBasisSpec,
) -> Result<BasisPsiDerivativeResult, BasisError> {
    let mut workspace = BasisWorkspace::default();
    build_thin_plate_basis_log_kappa_derivativewithworkspace(data, spec, &mut workspace)
}

pub fn build_thin_plate_basis_log_kappa_derivativewithworkspace(
    data: ArrayView2<'_, f64>,
    spec: &ThinPlateBasisSpec,
    workspace: &mut BasisWorkspace,
) -> Result<BasisPsiDerivativeResult, BasisError> {
    let mut bundle =
        build_thin_plate_basis_log_kappa_derivativeswithworkspace(data, spec, workspace)?;
    bundle.first.implicit_operator = bundle.implicit_operator;
    Ok(bundle.first)
}

pub fn build_thin_plate_basis_log_kappa_derivatives(
    data: ArrayView2<'_, f64>,
    spec: &ThinPlateBasisSpec,
) -> Result<BasisPsiDerivativeBundle, BasisError> {
    let mut workspace = BasisWorkspace::default();
    build_thin_plate_basis_log_kappa_derivativeswithworkspace(data, spec, &mut workspace)
}

pub fn build_thin_plate_basis_log_kappa_derivativeswithworkspace(
    data: ArrayView2<'_, f64>,
    spec: &ThinPlateBasisSpec,
    workspace: &mut BasisWorkspace,
) -> Result<BasisPsiDerivativeBundle, BasisError> {
    let base = build_thin_plate_basiswithworkspace(data, spec, workspace)?;
    let (centers, identifiability_transform, radial_reparam) = match &base.metadata {
        BasisMetadata::ThinPlate {
            centers,
            identifiability_transform,
            radial_reparam,
            ..
        } => (
            centers.clone(),
            identifiability_transform.clone(),
            radial_reparam.clone(),
        ),
        _ => {
            crate::bail_invalid_basis!("ThinPlate derivative path expected ThinPlate metadata");
        }
    };
    let mut derivative_spec = spec.clone();
    if derivative_spec.radial_reparam.is_none() {
        derivative_spec.radial_reparam = radial_reparam;
    }
    let scalar = build_thin_plate_scalar_design_psi_derivatives(
        data,
        centers.view(),
        &derivative_spec,
        identifiability_transform.as_ref(),
        workspace,
    )?;
    let (
        primary_derivative_opt,
        primarysecond_derivative_opt,
        nullspace_derivative_opt,
        nullspacesecond_derivative_opt,
    ) = build_thin_plate_penalty_psi_derivativeswithworkspace(
        centers.view(),
        &derivative_spec,
        identifiability_transform.as_ref(),
        workspace,
    )?;
    let primary_derivative = primary_derivative_opt;
    let primarysecond_derivative = primarysecond_derivative_opt;
    let nullspace_derivative = nullspace_derivative_opt;
    let nullspacesecond_derivative = nullspacesecond_derivative_opt;
    let penalties_derivative = active_thin_plate_penalty_derivatives(
        &base.active_penalties,
        &primary_derivative,
        &nullspace_derivative,
    )?;
    let penaltiessecond_derivative = active_thin_plate_penalty_derivatives(
        &base.active_penalties,
        &primarysecond_derivative,
        &nullspacesecond_derivative,
    )?;
    Ok(BasisPsiDerivativeBundle {
        first: BasisPsiDerivativeResult {
            design_derivative: scalar.design_first,
            penalties_derivative,
            implicit_operator: None,
        },
        second: BasisPsiSecondDerivativeResult {
            designsecond_derivative: scalar.design_second_diag,
            penaltiessecond_derivative,
            implicit_operator: None,
        },
        implicit_operator: scalar.implicit_operator,
    })
}

pub fn build_thin_plate_basis_log_kappasecond_derivative(
    data: ArrayView2<'_, f64>,
    spec: &ThinPlateBasisSpec,
) -> Result<BasisPsiSecondDerivativeResult, BasisError> {
    let mut workspace = BasisWorkspace::default();
    build_thin_plate_basis_log_kappasecond_derivativewithworkspace(data, spec, &mut workspace)
}

pub fn build_thin_plate_basis_log_kappasecond_derivativewithworkspace(
    data: ArrayView2<'_, f64>,
    spec: &ThinPlateBasisSpec,
    workspace: &mut BasisWorkspace,
) -> Result<BasisPsiSecondDerivativeResult, BasisError> {
    let mut bundle =
        build_thin_plate_basis_log_kappa_derivativeswithworkspace(data, spec, workspace)?;
    bundle.second.implicit_operator = bundle.implicit_operator;
    Ok(bundle.second)
}

/// High-level TPS constructor: selects knots from data, then builds basis+penalty.
pub fn create_thin_plate_spline_basis_with_knot_count(
    data: ArrayView2<f64>,
    num_knots: usize,
) -> Result<(ThinPlateSplineBasis, Array2<f64>), BasisError> {
    let mut workspace = BasisWorkspace::default();
    create_thin_plate_spline_basis_with_knot_count_andworkspace(data, num_knots, &mut workspace)
}

pub fn create_thin_plate_spline_basis_with_knot_count_andworkspace(
    data: ArrayView2<f64>,
    num_knots: usize,
    workspace: &mut BasisWorkspace,
) -> Result<(ThinPlateSplineBasis, Array2<f64>), BasisError> {
    let knots = select_thin_plate_knots(data, num_knots)?;
    let basis = create_thin_plate_spline_basiswithworkspace(data, knots.view(), workspace)?;
    Ok((basis, knots))
}

/// Applies a sum-to-zero constraint to a basis matrix for model identifiability.
///
/// This is achieved by reparameterizing the basis to be orthogonal to the weighted intercept.
/// In GAMs, this constraint removes the confounding between the intercept and smooth functions.
/// For weighted models (e.g., GLM-IRLS), the constraint is B^T W 1 = 0 instead of B^T 1 = 0.
///
/// # Arguments
/// * `basis_matrix`: An `ArrayView2<f64>` of the original, unconstrained basis matrix.
/// * `weights`: Optional weights for the constraint. If None, uses unweighted constraint.
///
/// # Returns
/// A tuple containing:
/// - The new, constrained basis matrix (with `k - rank(c)` columns).
/// - The transformation matrix `Z` used to create it.
pub fn apply_sum_to_zero_constraint(
    basis_matrix: ArrayView2<f64>,
    weights: Option<ArrayView1<f64>>,
) -> Result<(Array2<f64>, Array2<f64>), BasisError> {
    let n = basis_matrix.nrows();
    let k = basis_matrix.ncols();
    if k < 2 {
        return Err(BasisError::InsufficientColumnsForConstraint { found: k });
    }

    // c = B^T w (weighted constraint) or B^T 1 (unweighted constraint)
    let constraintvector = match weights {
        Some(w) => {
            if w.len() != n {
                return Err(BasisError::WeightsDimensionMismatch {
                    expected: n,
                    found: w.len(),
                });
            }
            w.to_owned()
        }
        None => Array1::<f64>::ones(n),
    };
    let c = basis_matrix.t().dot(&constraintvector); // shape k

    // Orthonormal basis for nullspace of c^T from a pivoted QR of the k×1
    // constraint matrix.
    let mut c_mat = Array2::<f64>::zeros((k, 1));
    c_mat.column_mut(0).assign(&c);
    let (z, rank) =
        rrqr_nullspace_basis(&c_mat, default_rrqr_rank_alpha()).map_err(BasisError::LinalgError)?;
    if rank >= k {
        return Err(BasisError::ConstraintNullspaceCollapsed {
            site: "apply_sum_to_zero_constraint",
            cross_rank: rank,
            coeff_dim: k,
            cross_frobenius: c.iter().map(|v| v * v).sum::<f64>().sqrt(),
            gram_spectrum: "not computed (structural rank collapse before Gram eigendecomposition)"
                .to_string(),
        });
    }
    if rank == 0 {
        // Already orthogonal to the intercept constraint; keep full basis unchanged.
        return Ok((basis_matrix.to_owned(), Array2::eye(k)));
    }

    let gauge = gam_problem::Gauge::sum_to_zero(z);
    let constrained = gauge.restrict_design(&basis_matrix);
    let z = gauge.block_transform(0);
    Ok((constrained, z))
}

/// Build a sum-to-zero reparametrization for a sparse basis.
///
/// Returns `(B_c, Z)` where `Z` is an **orthonormal** basis for `null(c^T)`
/// with `c = B^T w` (the weighted column sums of `B`), and
/// `B_c = B Z` is the constrained design matrix.
///
/// Because `Z` has orthonormal columns, `Z Zᵀ` is the canonical
/// orthogonal projector onto `null(cᵀ)` — i.e. it is idempotent and
/// `cᵀ Z Zᵀ = 0`, so any vector projected by `Z Zᵀ` still satisfies the
/// sum-to-zero constraint. The previous "drop the pivot column" trick
/// produced a valid null-space basis but with non-orthogonal, non-unit
/// columns, breaking the projector identities downstream code may rely on.
///
/// `Z` is dense `(k × (k-1))`; consequently `B_c = B Z` is returned as a
/// dense matrix even when `B` is sparse. Callers that previously relied on
/// the constrained basis being sparse should wrap the result in
/// `DenseDesignMatrix`.
pub fn apply_sum_to_zero_constraint_sparse(
    basis_matrix: &SparseColMat<usize, f64>,
    weights: Option<ArrayView1<f64>>,
) -> Result<(Array2<f64>, Array2<f64>), BasisError> {
    let n = basis_matrix.nrows();
    let k = basis_matrix.ncols();
    if k < 2 {
        return Err(BasisError::InsufficientColumnsForConstraint { found: k });
    }

    let constraint_weights = match weights {
        Some(w) => {
            if w.len() != n {
                return Err(BasisError::WeightsDimensionMismatch {
                    expected: n,
                    found: w.len(),
                });
            }
            w.to_owned()
        }
        None => Array1::<f64>::ones(n),
    };

    // c = Bᵀ w (k-vector of weighted column sums) computed directly from the
    // CSC storage.
    let mut c = Array1::<f64>::zeros(k);
    let (symbolic, values) = basis_matrix.parts();
    let col_ptr = symbolic.col_ptr();
    let row_idx = symbolic.row_idx();
    for col in 0..k {
        let mut sum = 0.0;
        for idx in col_ptr[col]..col_ptr[col + 1] {
            sum += values[idx] * constraint_weights[row_idx[idx]];
        }
        c[col] = sum;
    }

    // Orthonormal basis for null(cᵀ) via a column-pivoted QR of the k×1
    // constraint matrix — exactly the same construction used by the dense
    // path `apply_sum_to_zero_constraint`. This guarantees ZᵀZ = I and hence
    // that ZZᵀ is the canonical orthogonal projector onto null(cᵀ).
    let mut c_mat = Array2::<f64>::zeros((k, 1));
    c_mat.column_mut(0).assign(&c);
    let (z, rank) =
        rrqr_nullspace_basis(&c_mat, default_rrqr_rank_alpha()).map_err(BasisError::LinalgError)?;
    if rank >= k {
        return Err(BasisError::ConstraintNullspaceCollapsed {
            site: "apply_sum_to_zero_constraint_sparse",
            cross_rank: rank,
            coeff_dim: k,
            cross_frobenius: c.iter().map(|v| v * v).sum::<f64>().sqrt(),
            gram_spectrum: "not computed (structural rank collapse before Gram eigendecomposition)"
                .to_string(),
        });
    }
    if rank == 0 {
        // Constraint is numerically zero (e.g. weights produced cᵀ ≈ 0):
        // the basis already lies in null(cᵀ), so the constrained basis is
        // the dense materialization of B with Z = I.
        let mut dense_b = Array2::<f64>::zeros((n, k));
        for col in 0..k {
            for idx in col_ptr[col]..col_ptr[col + 1] {
                dense_b[[row_idx[idx], col]] = values[idx];
            }
        }
        return Ok((dense_b, Array2::eye(k)));
    }

    // Constrained basis B_c = B Z. Iterate columns of Z and apply B as a
    // sparse-times-dense-vector product per column. Result is dense
    // `(n × (k-1))` since Z is dense.
    let kc = z.ncols();
    let mut constrained = Array2::<f64>::zeros((n, kc));
    for out_col in 0..kc {
        let z_col = z.column(out_col);
        let mut dst = constrained.column_mut(out_col);
        for src_col in 0..k {
            let coeff = z_col[src_col];
            if coeff == 0.0 {
                continue;
            }
            for idx in col_ptr[src_col]..col_ptr[src_col + 1] {
                dst[row_idx[idx]] += coeff * values[idx];
            }
        }
    }

    Ok((constrained, z))
}

/// Reparameterizes a basis matrix so its columns are orthogonal (with optional weights)
/// to a supplied constraint matrix.
///
/// Let:
/// - `B` be the raw basis (`n x k`)
/// - `C` be the constraint matrix (`n x q`)
/// - `W` be diagonal weights (`n x n`), or identity when `weights=None`
///
/// We seek a transformed basis `B_c = B K` (`n x k_c`) such that:
///   `B_c^T W C = 0`.
///
/// Expanding:
///   `B_c^T W C = (B K)^T W C = K^T (B^T W C)`.
///
/// So it is enough to choose columns of `K` in `null((B^T W C)^T)`.
/// This implementation computes:
///   `M = B^T W C` (`k x q`)
/// and extracts a basis for `null(M^T)` via column-pivoted Householder QR.
///
/// The result enforces orthogonality by construction while retaining the largest possible
/// smooth subspace under the given constraints.
pub fn applyweighted_orthogonality_constraint(
    basis_matrix: ArrayView2<f64>,
    constraint_matrix: ArrayView2<f64>,
    weights: Option<ArrayView1<f64>>,
) -> Result<(Array2<f64>, Array2<f64>), BasisError> {
    let n = basis_matrix.nrows();
    let k = basis_matrix.ncols();
    if constraint_matrix.nrows() != n {
        return Err(BasisError::ConstraintMatrixRowMismatch {
            basisrows: n,
            constraintrows: constraint_matrix.nrows(),
        });
    }
    if k == 0 {
        return Err(BasisError::InsufficientColumnsForConstraint { found: 0 });
    }
    let q = constraint_matrix.ncols();
    if q == 0 {
        return Ok((basis_matrix.to_owned(), Array2::eye(k)));
    }

    // Form W*C by row scaling because W is diagonal.
    let mut weighted_constraints = constraint_matrix.to_owned();
    if let Some(w) = weights {
        if w.len() != n {
            return Err(BasisError::WeightsDimensionMismatch {
                expected: n,
                found: w.len(),
            });
        }
        for (mut row, &weight) in weighted_constraints.axis_iter_mut(Axis(0)).zip(w.iter()) {
            row *= weight;
        }
    }

    // M = B^T W C. Its transpose M^T has nullspace directions in coefficient space
    // that produce basis columns orthogonal to C under the W-inner product.
    let constraint_cross = basis_matrix.t().dot(&weighted_constraints); // k×q
    let gram = fast_ata(&basis_matrix);
    let transform = orthogonality_transform_from_cross_and_gram(&constraint_cross, &gram)?;
    let basis_orthonormal = fast_ab(&basis_matrix, &transform);
    Ok((basis_orthonormal, transform))
}

/// Compute Greville abscissae for a B-spline basis.
///
/// The Greville abscissa for basis function j is defined as:
///   G_j = (1/d) × Σ_{k=1}^{d} t_{j+k}
///
/// These provide the "center" of support for each basis function and are used
/// for geometric constraints that don't depend on observed data. A key property
/// is that a linear function f(x) = a + bx has B-spline coefficients c_j = a + b·G_j,
/// so constraining coefficients to be orthogonal to [1, G] removes linear functions
/// from the representable space.
///
/// # Arguments
/// * `knot_vector` - Full knot vector including boundary repetitions
/// * `degree` - B-spline degree (typically 3 for cubic)
///
/// # Returns
/// Array of Greville abscissae, one per basis function (length = n_knots - degree - 1)
///
/// # Errors
/// Returns error if knot vector is too short or Greville abscissae are degenerate.
pub fn compute_greville_abscissae(
    knot_vector: &Array1<f64>,
    degree: usize,
) -> Result<Array1<f64>, BasisError> {
    let n_knots = knot_vector.len();
    if degree == 0 {
        // For degree 0, Greville abscissae are knot midpoints
        let n_basis = n_knots.saturating_sub(1);
        if n_basis == 0 {
            return Err(BasisError::InsufficientColumnsForConstraint { found: 0 });
        }
        let mut g = Array1::<f64>::zeros(n_basis);
        for j in 0..n_basis {
            g[j] = 0.5 * (knot_vector[j] + knot_vector[j + 1]);
        }
        return Ok(g);
    }

    // Number of basis functions: k = n_knots - degree - 1
    if n_knots <= degree + 1 {
        return Err(BasisError::InsufficientColumnsForConstraint {
            found: n_knots.saturating_sub(degree + 1),
        });
    }
    let n_basis = n_knots - degree - 1;

    let mut g = Array1::<f64>::zeros(n_basis);
    let d_inv = 1.0 / (degree as f64);

    for j in 0..n_basis {
        // G_j = (1/d) × Σ_{k=1}^{d} t_{j+k}
        let mut sum = 0.0;
        for k in 1..=degree {
            sum += knot_vector[j + k];
        }
        g[j] = sum * d_inv;
    }

    // Check for degeneracy (all Greville abscissae equal)
    let g_min = g.iter().cloned().fold(f64::INFINITY, f64::min);
    let g_max = g.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
    if (g_max - g_min) < 1e-10 {
        return Err(BasisError::DegenerateKnots);
    }

    Ok(g)
}

/// Compute the constraint transform Z using Greville abscissae (geometric constraints).
///
/// This creates a transform that removes constant and linear trends from spline
/// coefficients based purely on knot geometry, without reference to observed data.
/// This makes Z constant w.r.t. model parameters β, ensuring dZ/dβ = 0 exactly,
/// which enables exact analytic gradients.
///
/// # Mathematical Background
/// For B-splines, a linear function f(x) = a + bx has coefficients c_j = a + b·G_j
/// where G_j are the Greville abscissae. Therefore, constraining the coefficient
/// vector θ to satisfy:
///   - Σ θ_j = 0  (orthogonal to constants)
///   - Σ θ_j·G_j = 0  (orthogonal to linear in Greville coordinates)
/// removes the ability to represent any linear function.
///
/// # Arguments
/// * `knot_vector` - Full knot vector
/// * `degree` - B-spline degree
/// * `penalty_order` - Order of difference penalty (typically 2)
///
/// # Returns
/// Tuple of (transform Z, projected_penalty Z'SZ) where:
/// - Z: k × (k-2) matrix mapping raw coefficients to constrained space
/// - S_constrained: (k-2) × (k-2) projected second-difference penalty
pub fn compute_geometric_constraint_transform(
    knot_vector: &Array1<f64>,
    degree: usize,
    penalty_order: usize,
) -> Result<(Array2<f64>, Array2<f64>), BasisError> {
    // 1. Compute Greville abscissae
    let g = compute_greville_abscissae(knot_vector, degree)?;
    let k = g.len();

    if k < 3 {
        return Err(BasisError::InsufficientColumnsForConstraint { found: k });
    }

    // 2. Build constraint matrix C_geom (2 × k)
    // Row 0: all ones (intercept constraint)
    // Row 1: Greville abscissae (linear constraint)
    let mut c_geom = Array2::<f64>::zeros((2, k));
    for j in 0..k {
        c_geom[[0, j]] = 1.0;
        c_geom[[1, j]] = g[j];
    }

    // 3. Standardize linear row for numerical conditioning
    let g_mean = g.mean().unwrap_or(0.0);
    let gvar = g.iter().map(|&x| (x - g_mean).powi(2)).sum::<f64>() / (k as f64);
    let g_std = gvar.sqrt().max(1e-10);
    for j in 0..k {
        c_geom[[1, j]] = (c_geom[[1, j]] - g_mean) / g_std;
    }

    // 4. Column-pivoted QR on C_geom^T; the trailing Q columns span null(C_geom).
    let (z, rank) = rrqr_nullspace_basis(&c_geom.t(), default_rrqr_rank_alpha())
        .map_err(BasisError::LinalgError)?;
    if rank >= k {
        return Err(BasisError::ConstraintNullspaceCollapsed {
            site: "compute_geometric_constraint_transform",
            cross_rank: rank,
            coeff_dim: k,
            cross_frobenius: f64::NAN,
            gram_spectrum: "not computed (structural rank collapse before Gram eigendecomposition)"
                .to_string(),
        });
    }

    if z.ncols() == 0 {
        return Err(BasisError::ConstraintNullspaceCollapsed {
            site: "compute_geometric_constraint_transform",
            cross_rank: 0,
            coeff_dim: k,
            cross_frobenius: f64::NAN,
            gram_spectrum: "not computed (structural rank collapse before Gram eigendecomposition)"
                .to_string(),
        });
    }

    // 5. Build raw penalty and project: S_c = Z' S Z
    let s_raw = create_difference_penalty_matrix(k, penalty_order, Some(g.view()))?;
    let s_constrained = {
        let zt_s = fast_atb(&z, &s_raw);
        fast_ab(&zt_s, &z)
    };

    Ok((z, s_constrained))
}

/// Result of auto-deriving a clamped B-spline knot vector from 1-D data.
///
/// The `degree` / `num_internal_knots` fields report the **effective** values
/// that were actually used to build `knots`. They may differ from the
/// requested values when the engine had to auto-shrink the configuration
/// (issue #340): with small `n`, cubic-by-default gracefully degrades to
/// quadratic / linear, and the interior-knot count shrinks toward zero.
///
/// `shrunk` is `true` iff at least one of the two parameters was reduced
/// relative to the request, so callers can surface the decision in model
/// summaries / logs without recomputing it.
#[derive(Debug, Clone)]
pub struct AutoBSplineKnots {
    pub knots: Array1<f64>,
    pub degree: usize,
    pub num_internal_knots: usize,
    pub shrunk: bool,
}

/// Build a clamped B-spline full knot vector from 1-D data.
///
/// Thin public wrapper around
/// `internal::generate_full_knot_vector_quantile` so external crates can
/// request auto-derived knots without reimplementing the placement logic.
///
/// When `n = data.len()` is too small to support the requested
/// `(num_internal_knots, degree)` combination, this function auto-shrinks the
/// configuration to the largest feasible one (see `auto_shrink_bspline_config`):
///   * `num_internal_knots` is capped at `n - 2`.
///   * `degree` is reduced (cubic → quadratic → linear) until `n >= degree + 1`.
///
/// Only when even linear placement is impossible (`n < 2` or the data range is
/// degenerate) does this raise an error. The returned [`AutoBSplineKnots`]
/// records the effective configuration so downstream evaluators stay in sync.
pub fn auto_knot_vector_1d_quantile(
    data: ArrayView1<'_, f64>,
    num_internal_knots: usize,
    degree: usize,
) -> Result<AutoBSplineKnots, BasisError> {
    let n = data.len();
    let Some((eff_knots, eff_degree, shrunk)) =
        auto_shrink_bspline_config(n, num_internal_knots, degree)
    else {
        crate::bail_invalid_basis!(
            "auto-knot placement needs at least 2 finite evaluation points (got n={n}); \
             cannot fit even a linear B-spline",
        );
    };
    let knots = internal::generate_full_knot_vector_quantile(data, eff_knots, eff_degree)?;
    Ok(AutoBSplineKnots {
        knots,
        degree: eff_degree,
        num_internal_knots: eff_knots,
        shrunk,
    })
}

/// Build a clamped full B-spline knot vector from explicit *internal* knot
/// positions (mgcv `knots=` semantics).
///
/// The user supplies the interior knots (those strictly between the data
/// endpoints). This wraps them in the standard clamped boundary stencil:
/// `data_range.0` repeated `degree + 1` times, the sorted distinct internal
/// positions, then `data_range.1` repeated `degree + 1` times — matching the
/// layout produced by `internal::generate_full_knot_vector` for the uniform
/// case, except the interior positions are taken verbatim from the caller.
///
/// Internal positions must lie strictly inside `(data_range.0, data_range.1)`,
/// be finite, and be strictly increasing after sorting (no duplicates, which
/// would create a degenerate knot span). The data range itself is derived from
/// the covariate so the spline domain still spans the observed data even when
/// the user only pins a few interior knots.
pub fn clamped_knot_vector_from_internal_positions(
    data_range: (f64, f64),
    internal_positions: &[f64],
    degree: usize,
) -> Result<Array1<f64>, BasisError> {
    let (minval, maxval) = data_range;
    if !(minval.is_finite() && maxval.is_finite()) {
        crate::bail_invalid_basis!(
            "explicit knots require a finite data range, got ({minval:.6e}, {maxval:.6e})"
        );
    }
    if minval >= maxval {
        return Err(BasisError::InvalidRange(minval, maxval));
    }
    let scale = (maxval - minval).abs().max(1.0);
    let tol = 1e-12 * scale;

    let mut interior: Vec<f64> = Vec::with_capacity(internal_positions.len());
    for &k in internal_positions {
        if !k.is_finite() {
            crate::bail_invalid_basis!("explicit knot position {k:.6e} is not finite");
        }
        if k <= minval + tol || k >= maxval - tol {
            crate::bail_invalid_basis!(
                "explicit internal knot {k:.6e} must lie strictly inside the data range \
                 ({minval:.6e}, {maxval:.6e}); boundary knots are added automatically"
            );
        }
        interior.push(k);
    }
    interior.sort_by(f64::total_cmp);
    for w in interior.windows(2) {
        if (w[1] - w[0]).abs() <= tol {
            crate::bail_invalid_basis!(
                "explicit internal knots must be strictly increasing; \
                 found a duplicate/near-duplicate near {:.6e}",
                w[0]
            );
        }
    }

    let total_knots = interior.len() + 2 * (degree + 1);
    let mut knots = Vec::with_capacity(total_knots);
    for _ in 0..=degree {
        knots.push(minval);
    }
    knots.extend_from_slice(&interior);
    for _ in 0..=degree {
        knots.push(maxval);
    }
    Ok(Array::from_vec(knots))
}

/// Place `num_centers` Duchon centers on 1-D data via the equal-mass strategy.
///
/// Thin public wrapper around `select_equal_mass_centers` specialised to a
/// single covariate dimension. The returned vector is sorted.
pub fn auto_centers_1d_equal_mass(
    data: ArrayView1<'_, f64>,
    num_centers: usize,
) -> Result<Array1<f64>, BasisError> {
    let column = data.to_owned().insert_axis(Axis(1));
    let centers = select_equal_mass_centers(column.view(), num_centers)?;
    let mut flat: Vec<f64> = centers.column(0).iter().copied().collect();
    flat.sort_by(f64::total_cmp);
    Ok(Array1::from_vec(flat))
}

#[cfg(test)]
mod knot_selection_tie_break_cost_tests {
    use super::{select_thin_plate_knot_rows, select_thin_plate_knots};
    use ndarray::Array2;

    /// The knot rows the production selector picks, together with the number of
    /// `O(n·d + n log n)` support-distance profiles the shared invariant
    /// tie-break built getting there.
    struct KnotSelection {
        rows: Vec<usize>,
        profile_builds: usize,
    }

    fn select_with_profile_count(data: &Array2<f64>, num_knots: usize) -> KnotSelection {
        let (rows, profile_builds) = select_thin_plate_knot_rows(data.view(), num_knots)
            .expect("fixture admits the requested knot budget");
        KnotSelection {
            rows,
            profile_builds,
        }
    }

    /// Deterministic unit draws (SplitMix64 finalizer): no RNG state, no seed
    /// coupling between rows.
    fn hashed_unit(index: u64) -> f64 {
        let mut z = index.wrapping_add(0x9E37_79B9_7F4A_7C15);
        z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
        z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
        ((z ^ (z >> 31)) >> 11) as f64 / (1u64 << 53) as f64
    }

    /// A cloud with no exact symmetry: no two rows can tie the maximin key.
    fn asymmetric_cloud(n: usize, d: usize) -> Array2<f64> {
        Array2::from_shape_fn((n, d), |(row, col)| hashed_unit((row * d + col) as u64))
    }

    /// An exactly-representable integer lattice — the canonical gridded spatial
    /// input, whose corner and edge classes tie every `O(1)` key exactly.
    fn integer_grid(side: usize) -> Array2<f64> {
        Array2::from_shape_fn((side * side, 2), |(row, col)| {
            if col == 0 {
                (row / side) as f64
            } else {
                (row % side) as f64
            }
        })
    }

    /// The sorted support-distance profile is a tie-break, and a tie-break must
    /// only be paid for where something is actually tied. A cloud with no exact
    /// symmetry has a unique maximin winner at every step, so the selection must
    /// complete having built NO profile at all — at any `n`, any dimension, and
    /// any knot budget.
    ///
    /// The two-profile comparator scan this replaced (#2420) built exactly
    /// `2·num_knots` profiles on precisely this input: the `reduce` over a
    /// one-element candidate list compares nothing, and the `retain` that follows
    /// it still sorted two length-`n` profiles to establish that a row equals
    /// itself. At `n = 200_000`, `d = 8`, `k = 300` that is ~3e9 wasted
    /// single-threaded operations before any knot is chosen.
    #[test]
    fn thin_plate_knots_cost_no_profile_without_an_exact_tie() {
        for (n, d) in [(2_000_usize, 2_usize), (2_000, 8), (8_000, 3)] {
            for k in [20_usize, 100] {
                let data = asymmetric_cloud(n, d);
                let chosen = select_with_profile_count(&data, k);
                assert_eq!(chosen.rows.len(), k, "knot budget (n={n}, d={d}, k={k})");
                assert_eq!(
                    chosen.profile_builds,
                    0,
                    "no row ties the maximin key on an asymmetric cloud, so the profile \
                     tie-break must never be built (n={n}, d={d}, k={k}); the replaced \
                     comparator scan built {}",
                    2 * k
                );
            }
        }
    }

    /// On an exact integer lattice the tie-break IS reached — a corner class is
    /// genuinely related by a symmetry of the square. The cost of reaching it
    /// must still be a property of the symmetry, not of the row count: the
    /// profile key may only be built for rows that tie every `O(1)` key at the
    /// maximin extremum, so the count stays at one profile per selected knot even
    /// as `n` grows nine-fold.
    #[test]
    fn thin_plate_knot_profile_cost_does_not_scale_with_the_row_count() {
        for side in [20_usize, 60] {
            for k in [20_usize, 100] {
                let data = integer_grid(side);
                let n = data.nrows();
                let chosen = select_with_profile_count(&data, k);
                assert_eq!(chosen.rows.len(), k, "knot budget (n={n}, k={k})");
                assert!(
                    chosen.profile_builds <= 2 * k,
                    "profile-key builds must stay proportional to the knot budget, not to \
                     the row count; got {} at n={n} k={k}",
                    chosen.profile_builds
                );
            }
        }
    }

    /// The gate above must not be satisfiable by deleting the tie-break. On a
    /// square's four corners plus its center, the corner class is an indivisible
    /// symmetry orbit, and the profile key is what proves it — so it must
    /// genuinely be built there.
    #[test]
    fn thin_plate_knot_profile_key_is_still_built_where_it_decides_an_orbit() {
        let data = ndarray::array![
            [-1.0_f64, -1.0],
            [-1.0, 1.0],
            [1.0, -1.0],
            [1.0, 1.0],
            [0.0, 0.0]
        ];
        let chosen = select_with_profile_count(&data, 5);
        assert_eq!(chosen.rows.len(), 5);
        assert!(
            chosen.profile_builds > 0,
            "the four-corner orbit is only provable through the invariant profile key"
        );
    }

    /// The public `Array2` surface must be exactly the rows the observer-carrying
    /// path selects, in the same order — the observer variant is the production
    /// code, not a parallel implementation.
    #[test]
    fn the_public_knot_matrix_is_the_selected_rows_verbatim() {
        for (n, d, k) in [(500_usize, 2_usize, 17_usize), (441, 2, 40)] {
            let data = if d == 2 && n == 441 {
                integer_grid(21)
            } else {
                asymmetric_cloud(n, d)
            };
            let chosen = select_with_profile_count(&data, k);
            let knots = select_thin_plate_knots(data.view(), k).expect("same budget");
            assert_eq!(knots.nrows(), chosen.rows.len());
            for (r, &row) in chosen.rows.iter().enumerate() {
                for c in 0..d {
                    assert_eq!(
                        knots[[r, c]].to_bits(),
                        data[[row, c]].to_bits(),
                        "knot {r} column {c} is not data row {row} verbatim"
                    );
                }
            }
        }
    }
}

#[cfg(test)]
mod knot_selection_invariance_tests {
    // Regression tests for the knot-selector invariance defects fixed by the
    // rotation-equivariant maximin seed (gam#1456 rotation, gam#1378 row
    // permutation). Both would FAIL on the OLD seed, which started the greedy
    // farthest-point recursion at the lexicographically-smallest-coordinate row:
    //   * a 90 degree rotation about the centroid changes which row is
    //     lexicographically smallest, reseeding at a different physical point and
    //     selecting a different knot SET (rotation leak, #1456);
    //   * a row permutation changes the row index of that smallest row only when
    //     two rows tie, but more fundamentally the index-based tie-breaks made the
    //     selected set order-dependent (#1378).
    // The fix seeds at the centroid-nearest row (rotation-equivariant, a pure
    // function of the unordered value set) with value-lexicographic tie-breaks, so
    // the selected SET is invariant under both transforms to machine precision.
    use super::select_thin_plate_knots;
    use ndarray::Array2;

    /// A deterministic, asymmetric 2-D point cloud. It is deliberately NOT a
    /// rotation-symmetric grid: the points have distinct distances to the
    /// centroid and distinct coordinate orderings, so the centroid-nearest seed
    /// is unique and the OLD lexicographic seed lands on a different physical
    /// point after a 90 degree rotation.
    fn sample_cloud() -> Array2<f64> {
        // 12 scattered points in the plane.
        let pts: Vec<[f64; 2]> = vec![
            [0.10, 0.20],
            [1.30, 0.05],
            [2.10, 1.40],
            [0.40, 2.30],
            [1.90, 2.80],
            [3.20, 0.70],
            [2.70, 3.10],
            [0.90, 1.10],
            [3.50, 2.20],
            [1.60, 3.60],
            [0.05, 3.05],
            [2.40, 0.30],
        ];
        let mut a = Array2::<f64>::zeros((pts.len(), 2));
        for (i, p) in pts.iter().enumerate() {
            a[[i, 0]] = p[0];
            a[[i, 1]] = p[1];
        }
        a
    }

    /// Canonicalise a knot set into a sorted multiset of (bit-pattern) coordinate
    /// tuples so two selections can be compared as SETS, independent of the order
    /// in which the rows were emitted. Using the IEEE-754 bit pattern makes the
    /// comparison exact (machine precision) and is valid here because the 90
    /// degree rotation `(x,z)->(-z,x)` about the centroid is built from exact
    /// f64 additions/negations of the same operands, so equal physical points
    /// have bit-identical coordinates.
    fn canonical(knots: &Array2<f64>) -> Vec<(u64, u64)> {
        let mut rows: Vec<(u64, u64)> = (0..knots.nrows())
            .map(|r| (knots[[r, 0]].to_bits(), knots[[r, 1]].to_bits()))
            .collect();
        rows.sort_unstable();
        rows
    }

    /// Centroid of a 2-D point set, as the rigid-rotation pivot.
    fn data_centroid_2d(data: &Array2<f64>) -> (f64, f64) {
        let n = data.nrows();
        let cx = (0..n).map(|i| data[[i, 0]]).sum::<f64>() / n as f64;
        let cz = (0..n).map(|i| data[[i, 1]]).sum::<f64>() / n as f64;
        (cx, cz)
    }

    /// Exact 90 degree rotation of every row about an EXPLICIT center
    /// `(cx, cz)`: `(x, z) -> (cx - (z - cz), cz + (x - cx))`. Built from f64
    /// add/sub only, so it introduces no rounding beyond the operands
    /// themselves. The center is passed in (rather than recomputed per array)
    /// so the data and a selected subset can be rotated about the SAME pivot —
    /// rotation invariance of the knot SET is `select(R·data) == R·select(data)`
    /// for one fixed `R`, which only holds bit-for-bit when both sides rotate
    /// about the identical center.
    fn rotate_90_about(data: &Array2<f64>, cx: f64, cz: f64) -> Array2<f64> {
        let n = data.nrows();
        let mut out = Array2::<f64>::zeros((n, 2));
        for i in 0..n {
            let dx = data[[i, 0]] - cx;
            let dz = data[[i, 1]] - cz;
            out[[i, 0]] = cx - dz;
            out[[i, 1]] = cz + dx;
        }
        out
    }

    #[test]
    fn knot_set_is_rotation_invariant_gh1456() {
        let data = sample_cloud();
        let n = data.nrows();
        // FarthestPoint path: strictly fewer knots than rows (centers != n).
        let num_knots = 5;
        assert!(num_knots < n, "must exercise the farthest-point selector");

        let knots = select_thin_plate_knots(data.view(), num_knots).expect("select knots");
        assert_eq!(knots.nrows(), num_knots);

        // ONE rigid rotation R about the fixed data centroid, applied to both
        // the full data and the selected subset. Rotating the knots about their
        // OWN centroid instead would be a different map and could never match
        // bit-for-bit even under perfect invariance.
        let (cx, cz) = data_centroid_2d(&data);
        let rotated = rotate_90_about(&data, cx, cz);
        let knots_rot = select_thin_plate_knots(rotated.view(), num_knots).expect("select rotated");

        // The invariant: selecting in the rotated frame yields the SAME physical
        // points as rotating the originally-selected set. With an exact 90 degree
        // rotation this holds to machine precision (bit-identical coordinates).
        let knots_then_rotate = rotate_90_about(&knots, cx, cz);
        assert_eq!(
            canonical(&knots_then_rotate),
            canonical(&knots_rot),
            "rotating-then-selecting must equal selecting-then-rotating (gh#1456); \
             the OLD lexicographic seed picks a different physical point after rotation"
        );
    }

    #[test]
    fn knot_set_is_row_permutation_invariant_gh1378() {
        let data = sample_cloud();
        let n = data.nrows();
        let num_knots = 5;
        assert!(num_knots < n, "must exercise the farthest-point selector");

        let knots = select_thin_plate_knots(data.view(), num_knots).expect("select knots");

        // A non-trivial permutation of the rows (a fixed derangement-ish shuffle).
        let perm: Vec<usize> = vec![7, 0, 11, 3, 9, 1, 5, 10, 2, 8, 4, 6];
        assert_eq!(perm.len(), n);
        let mut permuted = Array2::<f64>::zeros((n, 2));
        for (new_row, &old_row) in perm.iter().enumerate() {
            permuted[[new_row, 0]] = data[[old_row, 0]];
            permuted[[new_row, 1]] = data[[old_row, 1]];
        }

        let knots_perm =
            select_thin_plate_knots(permuted.view(), num_knots).expect("select permuted");

        // The selected SET (as physical coordinate tuples) must be bit-identical
        // regardless of input row order (gh#1378).
        assert_eq!(
            canonical(&knots),
            canonical(&knots_perm),
            "reordering rows must not change the selected knot set (gh#1378)"
        );
    }

    #[test]
    fn symmetric_nonseed_orbit_is_completed_atomically() {
        let data = ndarray::array![[0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [0.0, -1.0]];
        let permutations = [[0_usize, 1, 2, 3], [0, 1, 3, 2], [2, 0, 3, 1], [3, 1, 2, 0]];
        let mut reference = None;

        for order in permutations {
            let permuted = Array2::from_shape_fn((4, 2), |(row, col)| data[[order[row], col]]);
            let knots = select_thin_plate_knots(permuted.view(), 3)
                .expect("origin plus the complete endpoint orbit fits the budget");
            let center_set = canonical(&knots);
            if let Some(expected) = reference.as_ref() {
                assert_eq!(&center_set, expected);
            } else {
                reference = Some(center_set);
            }
        }
    }

    #[test]
    fn incomplete_nonseed_orbit_is_capped_not_refused() {
        // origin (coincident pair, one distinct seed) plus the endpoint orbit
        // {(0,1),(0,-1)}. With one slot left after the seed, the endpoint orbit
        // cannot be split equivariantly — but refusing the fit is worse than
        // taking a deterministic member. The selection must succeed with exactly
        // `num_knots` distinct centers: the seed plus the lowest-row endpoint.
        let data = ndarray::array![[0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [0.0, -1.0]];
        let knots = select_thin_plate_knots(data.view(), 2)
            .expect("an oversized orbit must be capped, never refused");
        assert_eq!(knots.nrows(), 2, "capped selection must honour the budget");
        assert_eq!(
            canonical(&knots),
            canonical(&ndarray::array![[0.0, 0.0], [0.0, 1.0]]),
            "seed plus the lowest-row endpoint of the tied orbit"
        );
    }

    #[test]
    fn seed_orbit_larger_than_budget_is_capped_not_refused() {
        // Two antipodal points form a single indivisible seed orbit; a one-knot
        // budget cannot represent both. The selection must still succeed, taking
        // the lowest-row member deterministically rather than refusing.
        let data = ndarray::array![[-1.0, 0.0], [1.0, 0.0]];
        let knots = select_thin_plate_knots(data.view(), 1)
            .expect("an antipodal seed orbit must be capped, never refused");
        assert_eq!(knots.nrows(), 1, "capped selection must honour the budget");
        assert_eq!(
            canonical(&knots),
            canonical(&ndarray::array![[-1.0, 0.0]]),
            "lowest-row member of the antipodal seed orbit"
        );
    }

    #[test]
    fn regular_grid_fits_every_budget_with_distinct_centers() {
        // #2319 regression guard: a regular integer grid has exactly-representable
        // coordinates, so its corner/edge maximin orbits tie EXACTLY and typically
        // exceed the requested budget. The atomic-orbit rule used to refuse the
        // fit for common budgets (e.g. `k=15` on a 7x7 grid); it must instead cap
        // each oversized orbit and return exactly `k` geometrically distinct
        // centers for every in-range budget.
        let side = 7usize;
        let grid = Array2::from_shape_fn((side * side, 2), |(row, col)| {
            let (ix, iy) = (row % side, row / side);
            if col == 0 { ix as f64 } else { iy as f64 }
        });
        for k in 1..=side * side {
            let knots = select_thin_plate_knots(grid.view(), k)
                .unwrap_or_else(|e| panic!("grid must fit k={k}, got: {e}"));
            assert_eq!(knots.nrows(), k, "grid selection must honour budget k={k}");
            // Centers must be geometrically distinct (no coincident rows), or the
            // thin-plate Gram is singular.
            let mut set = canonical(&knots);
            let full = set.len();
            set.dedup();
            assert_eq!(set.len(), full, "duplicate centers at k={k}");
        }
    }

    #[test]
    fn capping_preserves_rotation_equivariance_on_generic_cloud() {
        // The #2319 contract lives on ISOTROPIC data, where generic coordinates
        // never tie exactly, so the capping path is not even entered and every
        // maximin/tie-break key is exactly rotation-invariant. Verify a budget
        // large enough to exercise many selection steps stays equivariant under
        // an exact 90-degree rotation (bit-preserving), so the fix did not perturb
        // the property the issue is actually about.
        let data = sample_cloud();
        let num_knots = 9.min(data.nrows() - 1);
        let (cx, cz) = data_centroid_2d(&data);
        let knots = select_thin_plate_knots(data.view(), num_knots).expect("base select");
        let rotated = rotate_90_about(&data, cx, cz);
        let knots_rot = select_thin_plate_knots(rotated.view(), num_knots).expect("rotated select");
        assert_eq!(
            canonical(&rotate_90_about(&knots, cx, cz)),
            canonical(&knots_rot),
            "capping change must not break rotation equivariance on generic data"
        );
    }
}

#[cfg(test)]
mod duchon_operator_gate_tests {
    use super::{DuchonOperatorPenaltySpec, OperatorPenaltySpec};

    #[test]
    fn default_duchon_operator_penalties_are_active() {
        let default_spec = DuchonOperatorPenaltySpec::default();

        assert!(
            default_spec.has_active_operator_penalty(),
            "default Duchon terms must bypass the native-only fused radial path"
        );
        assert!(
            matches!(default_spec.mass, OperatorPenaltySpec::Active { .. })
                && matches!(default_spec.tension, OperatorPenaltySpec::Active { .. })
                && matches!(default_spec.stiffness, OperatorPenaltySpec::Disabled),
            "the default is mass+tension active with stiffness disabled"
        );
    }

    #[test]
    fn all_disabled_duchon_operator_penalties_are_native_only() {
        let native_only = DuchonOperatorPenaltySpec::all_disabled();

        assert!(
            !native_only.has_active_operator_penalty(),
            "all_disabled() is the explicit native-Gram-only configuration"
        );
    }
}

#[cfg(test)]
mod retained_radial_indices_tests {
    use super::thin_plate_retained_radial_indices;
    use ndarray::Array1;

    // The eigenvalue spectra below were captured from the live thin-plate
    // builder (`s(x, bs="tp", k=20)`) on the #1271 regression data. They lock
    // in the derived selection behaviour: keep EVERY numerically-real bending
    // mode (matching mgcv, which truncates only at the numerical-rank floor),
    // dropping only sub-floor roundoff dust — no tuned magnitude cutoff.

    #[test]
    fn linear_data_spectrum_keeps_every_mode() {
        // Purely linear DGP: every eigenvalue is far above the numerical floor,
        // so all are genuine curvature directions and must be kept. REML (not
        // basis truncation) is responsible for the EDF on linear data.
        let evals = Array1::from_vec(vec![
            885.4, 119.98, 26.287, 10.030, 5.066, 2.330, 1.3953, 0.67709, 0.46814, 0.34210,
            0.26488, 0.17895, 0.14514,
        ]);
        let keep = thin_plate_retained_radial_indices(&evals);
        assert_eq!(
            keep.len(),
            evals.len(),
            "all numerically real modes must be retained"
        );
    }

    #[test]
    fn lidar_spectrum_keeps_every_real_mode() {
        // Real lidar fit: the smallest eigenvalues (~0.04) are still ~12 orders
        // of magnitude above the numerical floor (K*eps*lambda_max ~ 5e-12), so
        // they are real bending modes and are kept — pruning them by magnitude
        // was the #1271 over-prune that collapsed the nonlinear truth recovery.
        let evals = Array1::from_vec(vec![
            1212.2, 144.94, 37.270, 15.529, 6.0768, 3.5845, 1.8094, 1.1058, 0.73002, 0.43701,
            0.33814, 0.23136, 0.18267, 0.15702, 0.13654, 0.044936, 0.041844, 0.038235,
        ]);
        let keep = thin_plate_retained_radial_indices(&evals);
        assert_eq!(keep.len(), evals.len(), "every above-floor mode is kept");
    }

    #[test]
    fn pure_roundoff_modes_are_dropped() {
        // A mode below the K*eps*lambda_max numerical floor is roundoff dust.
        // Here K=5, lambda_max=1e3 => floor = 5*eps*1e3; put the dust an order
        // of magnitude below that floor.
        let big = 1.0e3;
        let dust = 0.1 * 5.0 * f64::EPSILON * big; // well below the K*eps*max floor
        let evals = Array1::from_vec(vec![big, 100.0, 10.0, 1.0, dust]);
        let keep = thin_plate_retained_radial_indices(&evals);
        assert_eq!(keep.len(), 4, "the sub-floor roundoff mode must be pruned");
        assert!(!keep.contains(&4));
    }

    #[test]
    fn empty_and_singleton_spectra_are_handled() {
        assert!(thin_plate_retained_radial_indices(&Array1::from_vec(vec![])).is_empty());
        assert_eq!(
            thin_plate_retained_radial_indices(&Array1::from_vec(vec![5.0])),
            vec![0]
        );
    }
}

#[cfg(test)]
mod gc_spectrum_diag_1757_tests {
    // ROOT-2 measurement for the perf cluster (#1757 duchon / #1689 thin-plate):
    // does the design Gram Gc = KᵀK (radial kernel evaluated at the selected
    // knots) have a REDUNDANCY CLIFF — a capacity-preserving low-rank truncation
    // à la Wood-2003, where dropping near-duplicate radial columns shrinks the
    // final basis dimension p WITHOUT removing function-space capacity — or only
    // a smooth power-law tail, in which case no magic-free p-reduction exists and
    // the current machine-eps whitening floor already keeps everything meaningful.
    //
    // This is a DIAGNOSTIC (no behavioural assertion beyond "it ran"): it prints
    // the Gc spectrum for the #1757/#1689 repro sizes so CI can grep the shard
    // log. It uses the PRODUCTION knot selector (`select_thin_plate_knots`,
    // farthest-point) and the PRODUCTION thin-plate kernel
    // (`thin_plate_kernel_from_dist2`), so the spectrum matches what the real
    // basis builder forms (the polynomial-null constraint Z removes only 3 dims
    // and cannot create or erase a spectral cliff, so the raw KᵀK Gram answers
    // the redundancy-tail question).
    use super::{select_thin_plate_knots, thin_plate_kernel_from_dist2};
    use crate::basis::default_num_centers;
    use faer::Side;
    use gam_linalg::faer_ndarray::FaerEigh;
    use ndarray::Array2;

    // Deterministic uniform scatter in [-1, 1]^2 (SplitMix64; no `rand`
    // dependency, so the printed spectrum is reproducible across machines).
    fn scatter(n: usize, seed: u64) -> Array2<f64> {
        let mut s = seed ^ 0x9e37_79b9_7f4a_7c15;
        let mut next = || {
            s = s.wrapping_add(0x9e37_79b9_7f4a_7c15);
            let mut z = s;
            z = (z ^ (z >> 30)).wrapping_mul(0xbf58_476d_1ce4_e5b9);
            z = (z ^ (z >> 27)).wrapping_mul(0x94d0_49bb_1331_11eb);
            z ^= z >> 31;
            ((z >> 11) as f64) / ((1u64 << 53) as f64) // in [0, 1)
        };
        let mut x = Array2::<f64>::zeros((n, 2));
        for i in 0..n {
            x[[i, 0]] = 2.0 * next() - 1.0;
            x[[i, 1]] = 2.0 * next() - 1.0;
        }
        x
    }

    /// Returns `(p, kk, cond)`: number of positive Gram eigenvalues, knot count,
    /// and condition number `λ_max / λ_min⁺`. The caller asserts the design-Gram
    /// invariants (`1 ≤ p ≤ kk`, `cond` finite and `≥ 1`); the printed spectrum is
    /// the diagnostic signal.
    fn report(label: &str, n: usize, seed: u64) -> (usize, usize, f64) {
        let x = scatter(n, seed);
        let k = default_num_centers(n, 2);
        let knots = select_thin_plate_knots(x.view(), k).expect("knot selection");
        let kk = knots.nrows();
        // Design K (n x kk): K[i,c] = phi(||x_i - knot_c||^2), thin-plate d=2.
        let mut kdes = Array2::<f64>::zeros((n, kk));
        for i in 0..n {
            for c in 0..kk {
                let dx = x[[i, 0]] - knots[[c, 0]];
                let dy = x[[i, 1]] - knots[[c, 1]];
                let d2 = dx * dx + dy * dy;
                kdes[[i, c]] = thin_plate_kernel_from_dist2(d2, 2).expect("kernel");
            }
        }
        let gc = kdes.t().dot(&kdes); // kk x kk design Gram
        let (evals, _evecs) = FaerEigh::eigh(&gc, Side::Lower).expect("eigh");
        let mut ev: Vec<f64> = evals.iter().copied().filter(|v| *v > 0.0).collect();
        ev.sort_by(|a, b| b.partial_cmp(a).unwrap()); // descending
        let m = ev.len();
        if m == 0 {
            eprintln!("[GC-DIAG-1757] {label} n={n}: empty spectrum");
            return (0, kk, f64::INFINITY);
        }
        let lam_max = ev[0];
        let eps_floor = (kk as f64) * f64::EPSILON * lam_max; // current whitening floor
        let eps_kept = ev.iter().filter(|v| **v > eps_floor).count();
        let count_rel = |t: f64| ev.iter().filter(|v| **v / lam_max > t).count();
        // Largest multiplicative gap in the sorted spectrum (eigengap estimator).
        let mut best_gap = 0.0_f64;
        let mut gap_keep = m;
        for j in 0..m - 1 {
            let g = (ev[j] / ev[j + 1]).ln();
            if g > best_gap {
                best_gap = g;
                gap_keep = j + 1;
            }
        }
        eprintln!(
            "[GC-DIAG-1757] {label} n={n} k_req={k} kk={kk} p={m} cond={:.2e} eps_kept={eps_kept} eigengap_keep={gap_keep} log_gap={:.2} | #rel> 1e-2:{} 1e-4:{} 1e-6:{} 1e-8:{} 1e-10:{}",
            lam_max / ev[m - 1],
            best_gap,
            count_rel(1e-2),
            count_rel(1e-4),
            count_rel(1e-6),
            count_rel(1e-8),
            count_rel(1e-10),
        );
        let sampled: Vec<String> = (0..m)
            .step_by((m / 20).max(1))
            .map(|i| format!("{:.1}", (ev[i] / lam_max).log10()))
            .collect();
        eprintln!(
            "[GC-DIAG-1757] {label} log10(rel eigenvalue) sampled: {}",
            sampled.join(" ")
        );
        (m, kk, lam_max / ev[m - 1])
    }

    #[test]
    fn gc_spectrum_duchon_thinplate_repro_sizes() {
        // The redundancy-tail answer is left to the printed spectrum; these are
        // structural design-Gram invariants a broken Gram/knot/kernel would
        // violate (they do NOT presuppose the cliff-vs-power-law verdict).
        for (label, n, seed) in [
            ("duchon_n500", 500usize, 42u64),
            ("duchon_n1220", 1220, 43),
            ("thinplate_n1200", 1200, 7),
        ] {
            let (p, kk, cond) = report(label, n, seed);
            assert!(
                p >= 1 && p <= kk,
                "{label}: positive-eigenvalue count {p} must be in 1..={kk}"
            );
            assert!(
                cond.is_finite() && cond >= 1.0,
                "{label}: condition number {cond} must be finite and >= 1"
            );
        }
    }
}

#[cfg(test)]
mod range_floor_psi_jet_tests {
    use super::*;
    use ndarray::Array2;

    // Build a symmetric matrix from a lower-triangular seed so Ω(ψ) stays
    // symmetric for every ψ.
    fn sym_from(seed: &[f64], n: usize) -> Array2<f64> {
        let mut m = Array2::<f64>::zeros((n, n));
        let mut k = 0usize;
        for i in 0..n {
            for j in 0..=i {
                m[[i, j]] = seed[k];
                m[[j, i]] = seed[k];
                k += 1;
            }
        }
        m
    }

    // Controlled model: Ω(ψ) = Ω0 + ψ·B + ½ψ²·C with a WELL-SEPARATED base
    // spectrum whose two smallest modes sit ~100× below the range floor and a
    // deliberately SMALL non-commuting perturbation, so (a) the clamped set is
    // stable across ±eps (the clamp is only C⁰ where a mode crosses the floor,
    // which would corrupt a finite difference) while (b) B does not commute with
    // Ω0, rotating the eigenvectors so the off-diagonal Daleckii–Krein terms are
    // genuinely exercised. Ω0 = U diag(d) Uᵀ with U the eigenvectors of a fixed
    // symmetric seed and d spanning the floor boundary.
    fn omega_at(psi: f64) -> (Array2<f64>, Array2<f64>, Array2<f64>) {
        let n = 5usize;
        let seed = sym_from(
            &[
                1.0, 0.3, 0.9, -0.2, 0.4, 1.1, 0.15, -0.25, 0.35, 0.8, 0.05, 0.2, -0.1, 0.3, 0.95,
            ],
            n,
        );
        let (_evals, u) = FaerEigh::eigh(&seed, Side::Lower).expect("seed eigh");
        // Target spectrum: three modes well above the floor (8e-8·λmax = 8e-8),
        // two modes ~100× below it and mutually separated by 10×.
        let d = [1.0_f64, 0.08, 0.006, 5.0e-10, 5.0e-11];
        let mut base = Array2::<f64>::zeros((n, n));
        for i in 0..n {
            for j in 0..n {
                let mut acc = 0.0;
                for k in 0..n {
                    acc += u[[i, k]] * d[k] * u[[j, k]];
                }
                base[[i, j]] = acc;
            }
        }
        let scale = 1.0e-3;
        let b = sym_from(
            &[
                0.7, -0.2, 0.5, 0.1, -0.3, 0.4, 0.05, 0.2, -0.1, 0.6, 0.02, -0.04, 0.03, 0.08,
                -0.05,
            ],
            n,
        )
        .mapv(|v| v * scale);
        let c = sym_from(
            &[
                0.2, 0.1, -0.15, 0.05, 0.2, -0.1, 0.03, -0.02, 0.04, 0.1, 0.01, 0.02, -0.03, 0.05,
                0.02,
            ],
            n,
        )
        .mapv(|v| v * scale);
        let omega = &base + &b.mapv(|v| v * psi) + &c.mapv(|v| v * 0.5 * psi * psi);
        let omega_psi = &b + &c.mapv(|v| v * psi);
        (omega, omega_psi, c)
    }

    #[test]
    fn range_floor_psi_jet_matches_central_differences() {
        let dim = 8usize; // embedded_penalty_dim > n so the floor is active
        let (o0, b0, c0) = omega_at(0.0);
        let jet =
            duchon_range_floor_curvature_psi_jet(&o0, &b0, &c0, dim).expect("range-floor psi jet");

        // The floored value must equal the standalone range-floor.
        let direct = duchon_range_floor_curvature(&o0, dim).expect("range floor");
        let val_err = (&jet.value - &direct)
            .iter()
            .map(|v| v * v)
            .sum::<f64>()
            .sqrt();
        assert!(
            val_err < 1e-10,
            "range-floor value mismatch vs standalone: {val_err:.3e}"
        );
        // The floor must actually be biting (otherwise the test is vacuous).
        let floor_gap = (&jet.value - &symmetrize_penalty(&o0))
            .iter()
            .map(|v| v.abs())
            .fold(0.0_f64, f64::max);
        assert!(
            floor_gap > 0.0,
            "range floor is not active — test is vacuous"
        );

        let eps = 1e-6;
        let (op, _, _) = omega_at(eps);
        let (om, _, _) = omega_at(-eps);
        let vp = duchon_range_floor_curvature_psi_jet(&op, &b0, &c0, dim)
            .unwrap()
            .value;
        let vm = duchon_range_floor_curvature_psi_jet(&om, &b0, &c0, dim)
            .unwrap()
            .value;
        let fd_first = (&vp - &vm).mapv(|v| v / (2.0 * eps));
        let first_err = (&jet.first - &fd_first)
            .iter()
            .map(|v| v * v)
            .sum::<f64>()
            .sqrt();
        let first_scale = jet
            .first
            .iter()
            .map(|v| v * v)
            .sum::<f64>()
            .sqrt()
            .max(1e-9);
        assert!(
            first_err / first_scale < 1e-4,
            "range-floor first derivative mismatch: rel={:.3e} (err={first_err:.3e})",
            first_err / first_scale
        );

        // FD of the analytic FIRST derivative gives the second.
        let (op2, bp2, cp2) = omega_at(eps);
        let (om2, bm2, cm2) = omega_at(-eps);
        let fp = duchon_range_floor_curvature_psi_jet(&op2, &bp2, &cp2, dim)
            .unwrap()
            .first;
        let fm = duchon_range_floor_curvature_psi_jet(&om2, &bm2, &cm2, dim)
            .unwrap()
            .first;
        let fd_second = (&fp - &fm).mapv(|v| v / (2.0 * eps));
        let second_err = (&jet.second - &fd_second)
            .iter()
            .map(|v| v * v)
            .sum::<f64>()
            .sqrt();
        let second_scale = jet
            .second
            .iter()
            .map(|v| v * v)
            .sum::<f64>()
            .sqrt()
            .max(1e-9);
        assert!(
            second_err / second_scale < 1e-3,
            "range-floor second derivative mismatch: rel={:.3e} (err={second_err:.3e})",
            second_err / second_scale
        );
    }

    /// The range floor is a MARGIN on the spectral rank cutoff, not a magnitude
    /// of its own, and the margin is scored at the EMBEDDED dimension.
    ///
    /// Both halves were held in prose before, and the prose had already drifted:
    /// the doc comment said "one decade above (`nrows·1e-9·λmax`)" while the code
    /// wrote `1e-8`. A relation stated in two literals cannot be checked, so pin
    /// it executably — this fails if either the cutoff or the margin moves alone.
    #[test]
    fn duchon_range_floor_sits_the_stated_margin_above_the_embedded_rank_cutoff() {
        // A spectrum that straddles the cutoff: one strong curvature mode and
        // two low-curvature modes far beneath it.
        let n = 3usize;
        // The assembled kernel+poly dimension the block is finally scored at,
        // deliberately larger than the block handed in.
        let embedded = 100usize;
        let mut omega = Array2::<f64>::zeros((n, n));
        omega[[0, 0]] = 1.0;
        omega[[1, 1]] = 1.0e-14;
        omega[[2, 2]] = 1.0e-15;

        let floored = duchon_range_floor_curvature(&omega, embedded)
            .expect("a finite PSD diagonal must range-floor");
        let (evals, _) =
            FaerEigh::eigh(&floored, Side::Lower).expect("floored block must eigendecompose");

        let cutoff = spectral_tolerance_for_dim(embedded.max(n), &evals);
        let expected = RANGE_FLOOR_ABOVE_SPECTRAL_RANK_CUTOFF * cutoff;
        let lam_max = evals.iter().copied().fold(0.0_f64, |a, v| a.max(v.abs()));
        let min_eval = evals.iter().copied().fold(f64::INFINITY, f64::min);
        // The symmetric eigensolve and the `U diag(λ) Uᵀ` reconstruction are each
        // backward stable at `n·ε·λmax`; their sum is the entire error budget
        // between the floor written and the floor observed.
        let envelope = 2.0 * (n as f64) * f64::EPSILON * lam_max;

        assert!(
            (min_eval - expected).abs() <= envelope,
            "range floor {min_eval:.17e} is not {RANGE_FLOOR_ABOVE_SPECTRAL_RANK_CUTOFF}x the \
             embedded-dimension rank cutoff {cutoff:.17e} (expected {expected:.17e}, \
             envelope {envelope:.3e})"
        );
        // The point of the floor: nothing is left below the cutoff the block's
        // rank is scored against, so no genuine low-curvature mode is read as
        // unpenalized null.
        assert!(
            evals.iter().all(|&v| v > cutoff),
            "range-floored spectrum still holds a sub-cutoff (null-classified) mode: {evals:?}"
        );
    }
}