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        @threads for j ∈ eachindex(𝒯.R)
            Rⱼ = 𝒯.R[j]
            αⱼ = real(𝒯.α[j])
            ð̄²α = real(𝒯.ð̄²α[j])
            κ⁻¹ = γ * (1 - v⃗ ⋅ Rⱼ(𝐤))
            spline = @view 𝒯.splines[:, :, j]
            guess = firstindex(𝒯.u)
            for (i, u′) ∈ enumerate(𝒯.u′)
                u = κ⁻¹ * u′ + αⱼ
                d, guess = interpolate(𝒯.u, 𝒯.h, spline, u, guess)

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#%%
"""
This script shows how to load CCE data into a `scri.AsymptoticBondiData` object
and evaluate a set of constraints that should be satisfied involving the strain
and the Newman-Penrose quantities (assuming the waveform is in Bondi gauge).
These constraints are listed in the plot legend (see below) as equations to be
satisfied at each instant.  The violation is just one side minus the other, and
the norm is that difference squared integrated over the sphere (and
square-rooted).
"""

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# This bash/zsh function queries for the input DOI's bibtex entry.  When the DOI points
# to a journal article, this usually resolves to the journal's own bibtex output.

doi2bibtex () {
    curl -fsSLH "Accept: application/x-bibtex" "https://doi.org/$1" | sed -e 's|%2F|/|g'
}

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=======================================
Running in CoprecessingFrameTransients1
=======================================
/Users/boyle/.continuum/mambaforge/envs/CoprecessingFrameTransients1/lib/python3.9/site-packages/quaternion/calculus.py:310: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
  def fd_indefinite_integral(f, t):

python.__version__ = 3.9.16 | packaged by conda-forge | (main, Feb  1 2023, 21:42:20) 
[Clang 14.0.6 ]
numpy.__version__ = 1.21.0
scipy.__version__ = 1.7.0

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# The vector method is roughly 2.5x than the existing code with a
# sorted t′, and about 15% slower with a worst-case-disordered t′.

using BenchmarkTools
using DataInterpolations

shuffle(v) = collect(Iterators.flatten(zip(v[begin:length(v)÷2], v[length(v)÷2+1:end])))

function inplace(u, interp, t)
    @assert length(u) == length(t)