Interpolation result is NaN for large number of samples

[wsshin]

I get NaN as the result of interpolation when the number of samples is large (e.g., N = 700). I tried to reproduce the problem with a simpler set of samples, but I couldn't, so I copy and paste the problematic sample points and values below; sorry for the inconvenience.

xs = [8.000064047793606e-7, 8.000230069522153e-7, 8.000396098141591e-7, 8.000562133652352e-7, 8.000728176054861e-7, 8.000894225349546e-7, 8.00106028153684e-7, 8.001226344617173e-7, 8.001392414590971e-7, 8.001558491458663e-7, 8.00172457522068e-7, 8.001890665877453e-7, 8.002056763429408e-7, 8.002222867876977e-7, 8.002388979220586e-7, 8.002555097460667e-7, 8.00272122259765e-7, 8.002887354631964e-7, 8.003053493564036e-7, 8.003219639394298e-7, 8.003385792123181e-7, 8.003551951751113e-7, 8.003718118278524e-7, 8.003884291705842e-7, 8.004050472033498e-7, 8.004216659261924e-7, 8.004382853391548e-7, 8.0045490544228e-7, 8.004715262356107e-7, 8.004881477191903e-7, 8.005047698930617e-7, 8.00521392757268e-7, 8.005380163118519e-7, 8.005546405568565e-7, 8.005712654923252e-7, 8.005878911183006e-7, 8.006045174348258e-7, 8.006211444419438e-7, 8.006377721396977e-7, 8.006544005281306e-7, 8.006710296072856e-7, 8.006876593772053e-7, 8.007042898379331e-7, 8.007209209895121e-7, 8.007375528319853e-7, 8.007541853653957e-7, 8.007708185897861e-7, 8.007874525051998e-7, 8.0080408711168e-7, 8.008207224092698e-7, 8.00837358398012e-7, 8.008539950779496e-7, 8.008706324491258e-7, 8.00887270511584e-7, 8.00903909265367e-7, 8.009205487105177e-7, 8.009371888470795e-7, 8.009538296750955e-7, 8.009704711946086e-7, 8.009871134056622e-7, 8.010037563082989e-7, 8.010203999025621e-7, 8.010370441884952e-7, 8.010536891661409e-7, 8.010703348355424e-7, 8.010869811967429e-7, 8.011036282497858e-7, 8.011202759947138e-7, 8.011369244315702e-7, 8.011535735603979e-7, 8.011702233812404e-7, 8.011868738941408e-7, 8.012035250991422e-7, 8.012201769962877e-7, 8.012368295856203e-7, 8.012534828671833e-7, 8.012701368410202e-7, 8.012867915071738e-7, 8.013034468656871e-7, 8.013201029166035e-7, 8.013367596599665e-7, 8.013534170958188e-7, 8.013700752242038e-7, 8.013867340451646e-7, 8.014033935587443e-7, 8.014200537649864e-7, 8.01436714663934e-7, 8.014533762556301e-7, 8.01470038540118e-7, 8.014867015174411e-7, 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The number of samples is 700:

julia> length(xs), length(ys)
(700, 700)

Then, I get the following result:

julia> s = Spline1D(xs, ys);

julia> s(xs)
700-element Vector{Float64}:
 NaN
 NaN
 NaN
   ⋮
 NaN
 NaN
 NaN

Note that I am evaluating the interpolator at the sample points, so the interpolation values should be sample values, not NaNs.

I'm curious why this is happening, and if there is a workaround. Strangely, if I reduce the length of the samples, e.g., to 600, the problem disappears:

julia> s = Spline1D(xs[1:600], ys[1:600]);

julia> s(xs[1:600]) ≈ ys[1:600]
true

[wsshin]

I just realized that ys contains one -Inf at the index of 628. Replacing the element with a finite value resolved the issue.

Maybe Dierckx does not work if the sample values contain ±Inf. Is this a known issue?