csaps.CubicSmoothingSpline provides incorrect derivatives under certain circumstances

[shusheer]

Certain arrangements of smoothing intensity, weights and xdata, in particular irregularly spaced data where some xvalues are rather close together, causes CubicSmoothingSpline to produce a first derivative that is not the integral of the second derivative. This can be exacerbated by certain weights. In particular, the first derivative can be very non-smooth, even though the second derivative is smooth.

This is important, because a very common use-case for CubicSmoothingSpline (as opposed to say LOWESS smoothing) is to ensure smooth derivatives.

There are at least two possible solutions:
1 - modifying x-values or combining close points, for some definition of close (might be very data-specific).
2 - Sequentially integrating derivatives from the 3rd upwards, to enforce correct 1st and 2nd derivatives (might be quite computationally expensive).
3 - Converting to compressed scipy.CubicSpline form using code discussed at #46 (comment) also avoids this problem, as the spline is effectively resampled at optimally distributed x values.

Code demonstrating the problem (apologies for the large amount of data in the test case - it's some real data from a project I am working on, and I haven't been able to determine exactly which features are causing the problem, though enforcing a minimum x spacing seems to work as shown, and removing weights reduces the issue but doesn't eliminate it).

As you can see, the resulting splines all give quite different estimates of the data, so what we mean by "correct" is up for discussion.

# Problem case giving unsmooth derivatives
# The first derivative of the smoothed function is NOT the integral of the second derivative
# (notice small discontinuities / steps)
# This is particularly acute when certain weight values play a part as well

x = [-288.55699074,-288.28206019,-287.66591435,-287.64053241,-286.65069444,
-286.65068866,-285.52694444,-285.23972222,-284.64372685,-284.44784722,
-283.38444444,-283.38365741,-282.55511574,-282.53914352,-281.63871528,
-280.46857639,-280.46855324,-278.46340278,-278.37118056,-277.58601852,
-277.58574074,-276.54689815,-276.54663194,-275.62396991,-274.54616898,
-274.54615741,-273.55909722,-273.38333333,-272.57217593,-272.5721412,
-269.7065625,-269.51540509,-268.61980324,-268.60930556,-267.57458333,
-267.57457755,-263.58122685,-259.53576389,-258.82375,-258.82333333,
-257.85988426,-257.85986111,-254.25375,-254.25374421,-253.60337963,
-253.60333333,-251.7546875,-251.75461806,-250.94400463,-247.44850694,
-244.78422454,-244.7834838,-243.80513889,-243.80423611,-242.545625,
-242.54511574,-241.58532407,-241.58527778,-240.53894676,-240.53893519,
-239.59284722,-239.59278935,-234.86810185,-234.86706019,-229.50280093,
-229.50270833,-228.67150463,-228.67148148,-227.83733796,-227.63991898,
-225.75958333,-225.75953704,-224.77252315,-224.77251157,-223.98796296,
-223.98793981,-222.53775463,-222.53773148,-221.47532407,-219.68340278,
-219.6830787,-218.77277778,-218.77275463,-216.28890046,-216.28887731,
-215.52909722,-214.40041667,-214.40039352,-212.61553241,-212.61550926,
-211.81768519,-211.47722222,-211.47716435,-208.37016204,-208.36606481,
-207.62565972,-206.73474537,-206.73467593,-205.8547338,-205.83864583,
-204.94078704,-204.85533565,-203.72707176,-203.72493056,-203.72258102,
-203.57979167,-203.57978009,-202.72645833,-202.68732639,-202.54818287,
-201.68228009,-201.51018519,-201.43820602,-200.49405093,-200.49201389,
-199.62027778,-199.50150463,-199.50037037,-199.49247685,-198.74945602,
-198.74351852,-197.64726852,-197.6459375,-197.48726852,-196.49546296,
-196.43032407,-195.52611111,-195.46309028,-194.50974537,-194.50868056,
-193.46177083,-193.46171296,-192.88958333,-192.88957176,-191.64532407,
-191.64497685,-190.5394213,-190.51228009,-189.69407407,-189.69158565,
-188.55555556,-188.43287037,-186.77581019,-186.76670139,-184.90331019,
-184.90259259,-182.46954861,-182.46900463,-181.82518519,-180.79622685,
-180.79261574,-179.48037037,-179.47877315,-178.50547454,-178.50518519,
-177.54893519,-177.5484838,-176.70898148,-176.70897569,-172.47497685,
-171.58761574,-171.58756944,-169.23037037,-169.19318287,-168.54819444,
-165.79189815,-165.79189236,-165.79136574,-164.7587037,-164.75511574,
-161.85717593,-161.85717014,-160.74731481,-160.74730903,-156.4134838,
-156.41269676,-155.81989583,-155.81988426,-154.45269676,-154.43328704,
-153.80079861,-153.80032407,-152.54888889,-152.54819444,-148.93758102,
-147.84275463,-147.84274884,-146.98821759,-145.84561343,-145.84560185,
-140.25047454,-140.25046296,-138.99819444,-138.99818866,-137.92916667,
-137.85449074,-136.48898148,-136.48894676,-124.4325,-122.78548611,
-122.78547454,-121.78831019,-121.7624537,-119.5171875,-119.51571759,
-118.54518519,-118.54511574,-116.81631944,-116.6050463,-115.41570602,
-115.41196759,-114.53983796,-114.51606481,-112.65020833,-112.59722222,
-111.58196759,-111.58173611,-110.28418981,-108.88050926,-108.88037037,
-106.58741898,-106.58740741,-105.82334491,-105.61048611,-102.78446759,
-102.78175926,-101.92847222,-101.92841435,-99.70208333,-99.66606481,
-98.69592593,-98.69592014,-97.68861111,-96.54909722,-96.43082176,
-93.6450463,-93.64504051,-91.82571759,-91.82571181,-83.88231481,
-83.88003472,-82.53726852,-81.8415162,-81.78796296,-78.41912037,
-77.88599537,-77.88598958,-75.8,-75.79999421,-74.91194444,
-74.91193866,-70.52842593,-70.52751157,-66.66107639,-66.49625,
-64.62689815,-64.62497685,-63.84877315,-62.67493056,-62.67486111,
-56.45991898,-56.45849537,-55.80101852,-53.5209375,-53.42854167,
-51.79412037,-51.70625,-50.72030093,-50.72027778,-49.55359954,
-48.56030093,-48.52893519,-47.61050926,-47.61006944,-45.62118056,
-45.61965278,-43.70581019,-43.70578704,-42.61976852,-42.61907407,
-41.67361111,-41.64385417,-32.35585648,-31.6496875,-31.64862269,
-17.80746528,-17.77924769,-16.75157407,-16.75155093,-15.57109954,
-15.57106481,-14.64717593,-13.72175926,-13.67280093,-12.61773148,
-10.63041667,-10.63005787,-9.80740741,-9.80736111,-8.71253472,
-8.70666667,-6.74260417,-6.74258102,-5.34083333,-4.91016204,
-4.90943287,-2.30743056,-2.30638889,-0.42960648,-0.42840278]

y = [70.7572706,70.54032874,70.05606702,70.03624935,69.27432557,
69.27432117,68.43243561,68.22067996,67.78516637,67.64308661,
66.88118275,66.88062559,66.30052443,66.28947425,65.67543639,
64.90642245,64.90640759,63.66449628,63.60904412,63.14039473,
63.14022978,62.52515116,62.52499365,61.97652441,61.32001027,
61.32000308,60.68789985,60.57033966,60.00261623,60.00259097,
57.61865841,57.43806534,56.55456891,56.54383346,55.43956239,
55.43955595,50.38210045,44.53858607,43.50952278,43.50892299,
42.12805664,42.12802358,36.99275737,36.99274908,36.05906926,
36.05900272,33.41368763,33.41358922,32.27359692,27.70135708,
24.84323913,24.84254672,23.98532718,23.98459096,23.0642553,
23.0639276,22.5150554,22.51503233,22.08034126,22.08033742,
21.8389586,21.8389484,23.10692951,23.10771641,29.81761462,
29.81776653,31.21601939,31.21605924,32.68017101,33.03425222,
36.54083419,36.54092348,38.47670459,38.47672766,40.0590836,
40.05913083,43.0771602,43.07720928,45.35765585,49.31879272,
49.31952077,51.38303931,51.38309234,57.16432204,57.16437592,
58.92167454,61.46687347,61.46692457,65.24326265,65.24330938,
66.81838801,67.46953204,67.46964169,72.89983173,72.90644953,
74.08206155,75.44675245,75.44685685,76.74713302,76.77050437,
78.05420313,78.17438359,79.7183499,79.72118421,79.72429379,
79.91227229,79.91228745,80.99326008,81.04109716,81.20997602,
82.219385,82.41175016,82.49142149,83.49504942,83.49713412,
84.36266061,84.47724037,84.47833159,84.4859241,85.18879314,
85.19431565,86.18839601,86.18957211,86.32923784,87.17818962,
87.23249526,87.96788031,88.01785229,88.75344863,88.75424891,
89.51806878,89.51810973,89.91613619,89.91614411,90.73453049,
90.73474985,91.40125092,91.41664377,91.85156726,91.85279549,
92.34222988,92.38575227,92.75900826,92.75984105,92.59792734,
92.59772702,91.20024149,91.19975484,90.56381405,89.28641717,
89.28134229,87.14597333,87.14300865,85.15861765,85.15797276,
82.84855607,82.84738518,80.54827418,80.54825753,66.28762042,
63.00718054,63.00700893,54.31636812,54.18091963,51.84551574,
42.2968528,42.29683372,42.29509707,38.97123725,38.95998918,
30.75854662,30.75853234,28.19489508,28.19488261,21.31146526,
21.31063361,20.72423984,20.72422916,19.66675016,19.65459906,
19.30037669,19.30014055,18.82393793,18.82375279,18.89283954,
19.25883033,19.25883262,19.63216496,20.23324209,20.2332487,
24.12129249,24.12130103,25.03004562,25.03004973,25.76826801,
25.81806455,26.67629851,26.67631891,30.46561539,30.74262981,
30.74263179,30.91672453,30.92128596,31.30818311,31.3084222,
31.4588991,31.45890927,31.69236101,31.71975256,31.87221841,
31.87267866,31.9724062,31.97484795,32.10544092,32.10723373,
32.11864839,32.11864583,32.06417118,31.90691934,31.90689831,
31.39692865,31.3969252,31.14906127,31.07254683,29.76210069,
29.76059886,29.26593574,29.26590077,27.78880075,27.76287183,
27.0440895,27.04408511,26.26627309,25.36699318,25.27332511,
23.11069468,23.11069035,21.80609638,21.80609244,18.29594004,
18.295638,18.20182827,18.21706169,18.22000001,18.82965191,
18.98616378,18.98616554,19.69017271,19.6901748,20.01523736,
20.0152395,21.42764295,21.42784833,21.84871481,21.84950598,
21.78327154,21.78313985,21.72088105,21.59779516,21.59778708,
20.92961501,20.92955839,20.91266214,21.04288478,21.05619122,
21.41830913,21.44484473,21.79485625,21.79486561,22.3360831,
22.90340539,22.92289613,23.5358956,23.53620858,25.13688862,
25.13825808,27.00134805,27.00137215,28.16778118,28.16854978,
29.24170683,29.27634617,42.20507624,43.32162108,43.32331588,
68.1726824,68.22889458,70.29050421,70.29055095,72.69085058,
72.69092166,74.59109794,76.51130697,76.6133282,78.82157314,
83.02355351,83.0243163,84.77591304,84.77601173,87.11188547,
87.12441636,91.32441849,91.32446806,94.32811947,95.25177267,
95.25333675,100.83484899,100.83708178,104.85695,104.85952725]

w = [0.09462211,0.04377652,0.14838558,0.05943898,0.15541495,0.13085324,
0.062704,0.06243595,0.08839641,0.08066833,0.08655245,0.13400375,
0.12432887,0.09684014,0.08021506,0.1265312,0.05262315,0.06449225,
0.0758878,0.08259978,0.0806998,0.18695698,0.08123137,0.07486425,
0.12497271,0.10635125,0.15783934,0.08400718,0.24832489,0.12591398,
0.75945646,0.13893085,0.07963481,0.10749733,0.12091365,0.06358539,
0.01520488,0.03701155,0.16559939,0.21220943,0.08161835,0.16941486,
0.08949668,0.13363251,0.09692045,0.18369284,0.13672927,0.08379181,
0.01858722,0.01133573,0.06237293,0.24676197,0.10859054,0.12052321,
0.13203374,0.14501103,0.14774643,0.09328596,0.04971943,0.15257333,
0.28338405,0.09501884,0.98374631,0.05226721,0.09065792,0.13678137,
0.09321165,0.44103697,0.06999414,0.05989127,0.12692356,0.0915057,
0.11936501,0.10480617,0.1567755,0.07513431,0.07117161,0.06858016,
0.01640047,0.29806503,0.22542,0.12794823,0.21382192,0.80778319,
0.05041481,0.02423374,0.14427077,0.10418953,0.21508381,0.10940715,
0.04888315,0.04528401,0.04778175,0.11278799,0.43211842,0.03932949,
0.11803702,0.07236406,0.09973482,0.11787877,0.10316486,0.03396932,
0.07494178,0.0980055,0.15682156,0.39430807,0.2966579,0.0321651,
0.04839777,0.09632106,0.08455145,0.03593526,0.09416399,0.1499288,
0.20404766,0.05393556,0.05599547,0.08508938,0.03143724,0.03516901,
0.48995939,0.22142592,0.05018331,0.02789187,0.02197713,0.03036933,
0.20079765,0.0914527,0.04390079,0.0632086,0.12006129,0.04469976,
0.10379572,0.06173869,0.04413667,0.03245433,0.1633229,0.0436747,
0.06350709,0.05263706,0.04127021,0.04639606,0.06203611,0.0542154,
0.04355763,0.0831866,0.03767251,0.04846966,0.01901781,0.24288383,
0.05686747,0.0987101,0.11008607,0.0864383,0.17024615,0.23189691,
0.13231802,0.11141714,0.11234526,0.04201772,0.10555506,0.23622534,
0.15991181,0.07504075,0.08703826,0.078157,0.07949321,0.29557024,
0.0676124,0.16409294,0.24457436,0.10823184,0.14073518,0.35402206,
0.13634875,0.08048864,0.11152011,0.0714971,0.11998616,0.07697284,
0.08883651,0.08903601,0.12036493,0.07058305,0.01155317,0.08867653,
0.21117371,0.05630087,0.0809992,0.27670459,0.26026519,0.13855529,
0.15380278,0.23979833,0.20920358,0.1440551,0.13541747,0.1463744,
0.04642054,0.24201843,0.11214505,0.18320577,0.04986542,0.19388245,
0.07125025,0.22815885,0.08084224,0.06743765,0.28559653,0.11051846,
1.71124858,0.10948372,0.1427056,0.14208709,0.21579165,0.14818066,
0.10186744,0.02192575,0.07098707,0.09253821,0.13900954,0.12502331,
0.06113228,0.15379208,0.05778127,0.25567184,0.12133423,0.1361762,
0.06835819,0.17893468,0.07440845,0.05984869,0.02637486,0.06455977,
0.05552673,0.16468979,0.06516525,0.06218418,0.10451724,0.4187673,
0.08585429,0.02827772,0.19387362,0.0435158,0.02646894,0.16485791,
0.12165923,0.11332932,0.06376871,0.1044266,0.059728,0.14428556,
0.10856501,0.16783946,0.12808263,0.06199017,0.112075,0.04430589,
0.17411464,0.09521449,0.05556677,0.10710905,0.16484723,0.09197195,
0.09080481,0.11942555,0.29181033,0.05171834,0.10642797,0.0591445,
0.07264419,0.05472201,0.07241181,0.07863706,0.05298894,0.17034628,
0.13621438,0.12312648,0.20335133,0.10124627,0.08194233,0.08727886,
0.07454792,0.17554491,0.13723729,0.04581896,0.11138872,0.09357495,
0.09733723,0.21097312,0.05863559,0.02409536,0.11577836,0.08534116,
0.02837538,0.09877817,0.03943265,0.09397333,0.16018314,0.09669458,
0.18185679,0.11361261,0.07640622,0.0501189,0.1409333,0.04278513,
0.08166752,0.0946821,0.138107,0.05173292]

xrnd = np.rint(np.array(x)*25.)/25.
xrndv = xrnd[1:]
xrndv[np.flatnonzero(np.diff(xrnd)==0)]+=1./50.
xrndv[np.flatnonzero(np.diff(xrnd)==0)]+=1./100.

xs = np.unique(np.r_[x[0]+np.cumsum(np.repeat(np.diff(x)/4,4)),np.linspace(x[0], x[-1], len(x)*10)])

unweighted = CubicSmoothingSpline(x, y, smooth=5e-06)
weighted = CubicSmoothingSpline(x, y, weights=w, smooth=5e-06)
weightedimprovedx = CubicSmoothingSpline(xrnd, y, weights=w, smooth=5e-06)

def CubicSplineFromCubicSmoothingSpline(smoothing_spline, xrange=None, bc_type='natural', extrapolate=None):
    # If using this to generate a periodic spline from  a csaps spline, data is usually replicated
    # Therefore restrict the xrange to the actual period to be modelled
    x = smoothing_spline.spline.breaks
    y3 = smoothing_spline.spline.coeffs[0,:]*6
    if xrange:
        assert xrange[0] < xrange[1]
        xminidx = max(0,np.flatnonzero(np.r_[xrange[0],x]<=xrange[0])[-1]-1)
        xmaxidx = min(x.size-1,np.flatnonzero(np.r_[x,xrange[1]]>=xrange[1])[0])
        x = x[xminidx:xmaxidx+1].copy()
        x[0]=max(xrange[0],x[0])
        x[-1]=min(xrange[1],x[-1])
        y3 = y3[xminidx:xmaxidx+1]
    idxs_to_use = [0,x.size-1]
    idx_before_zero_crossings = np.where(np.diff(np.sign(y3)))[0]
    idxs_to_use.extend(idx_before_zero_crossings.tolist())
    for aidx,bidx in zip(np.r_[0,idx_before_zero_crossings],np.r_[idx_before_zero_crossings,x.size-1]):
        idx = np.argmax(np.abs(y3[aidx:bidx+1]))+aidx
        idxs_to_use.append(idx)
    x_compressed = np.unique(x[idxs_to_use])
    y_compressed = smoothing_spline(x_compressed)
    if bc_type=='periodic':
        y_compressed[0] = y_compressed[-1] = np.mean(y_compressed[[0,-1]])
    return CubicSpline(x_compressed, y_compressed, bc_type=bc_type, extrapolate=extrapolate)

compressedspline = CubicSplineFromCubicSmoothingSpline(weighted)

plt.plot(xs, unweighted(xs), '-', xs, weighted(xs), '-', xs, weightedimprovedx(xs), '-', xs, compressedspline(xs), '-', x, y, 'o')
plt.show()

for nu in range(1,4):
    fig,(ax1,ax2,ax3,ax4)=plt.subplots(1, 4, figsize=(12,4))
    ax1.plot(xs, unweighted(xs,nu=nu), '-')
    ax1.title.set_text('unweighted nu='+str(nu))
    ax2.plot(xs, weighted(xs,nu=nu), '-')
    ax2.title.set_text('weighted nu='+str(nu))
    ax3.plot(xs, weightedimprovedx(xs,nu=nu), '-')
    ax3.title.set_text('weighted, improved x nu='+str(nu))
    ax4.plot(xs, compressedspline(xs,nu=nu), '-')
    ax4.title.set_text('compressed spline nu='+str(nu))
    plt.show()

[espdev]

Have you considered the possibility to use bivariate spline (xs = u(t), ys = v(t)) for your data with close X-values?

For example this can be implemented something like this:

from csaps import CubicSmoothingSpline

import numpy as np
from matplotlib import pyplot as plt

x = [-288.55699074,-288.28206019,-287.66591435,-287.64053241,-286.65069444,
-286.65068866,-285.52694444,-285.23972222,-284.64372685,-284.44784722,
-283.38444444,-283.38365741,-282.55511574,-282.53914352,-281.63871528,
-280.46857639,-280.46855324,-278.46340278,-278.37118056,-277.58601852,
-277.58574074,-276.54689815,-276.54663194,-275.62396991,-274.54616898,
-274.54615741,-273.55909722,-273.38333333,-272.57217593,-272.5721412,
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-153.80079861,-153.80032407,-152.54888889,-152.54819444,-148.93758102,
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-98.69592593,-98.69592014,-97.68861111,-96.54909722,-96.43082176,
-93.6450463,-93.64504051,-91.82571759,-91.82571181,-83.88231481,
-83.88003472,-82.53726852,-81.8415162,-81.78796296,-78.41912037,
-77.88599537,-77.88598958,-75.8,-75.79999421,-74.91194444,
-74.91193866,-70.52842593,-70.52751157,-66.66107639,-66.49625,
-64.62689815,-64.62497685,-63.84877315,-62.67493056,-62.67486111,
-56.45991898,-56.45849537,-55.80101852,-53.5209375,-53.42854167,
-51.79412037,-51.70625,-50.72030093,-50.72027778,-49.55359954,
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-41.67361111,-41.64385417,-32.35585648,-31.6496875,-31.64862269,
-17.80746528,-17.77924769,-16.75157407,-16.75155093,-15.57109954,
-15.57106481,-14.64717593,-13.72175926,-13.67280093,-12.61773148,
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-8.70666667,-6.74260417,-6.74258102,-5.34083333,-4.91016204,
-4.90943287,-2.30743056,-2.30638889,-0.42960648,-0.42840278]

y = [70.7572706,70.54032874,70.05606702,70.03624935,69.27432557,
69.27432117,68.43243561,68.22067996,67.78516637,67.64308661,
66.88118275,66.88062559,66.30052443,66.28947425,65.67543639,
64.90642245,64.90640759,63.66449628,63.60904412,63.14039473,
63.14022978,62.52515116,62.52499365,61.97652441,61.32001027,
61.32000308,60.68789985,60.57033966,60.00261623,60.00259097,
57.61865841,57.43806534,56.55456891,56.54383346,55.43956239,
55.43955595,50.38210045,44.53858607,43.50952278,43.50892299,
42.12805664,42.12802358,36.99275737,36.99274908,36.05906926,
36.05900272,33.41368763,33.41358922,32.27359692,27.70135708,
24.84323913,24.84254672,23.98532718,23.98459096,23.0642553,
23.0639276,22.5150554,22.51503233,22.08034126,22.08033742,
21.8389586,21.8389484,23.10692951,23.10771641,29.81761462,
29.81776653,31.21601939,31.21605924,32.68017101,33.03425222,
36.54083419,36.54092348,38.47670459,38.47672766,40.0590836,
40.05913083,43.0771602,43.07720928,45.35765585,49.31879272,
49.31952077,51.38303931,51.38309234,57.16432204,57.16437592,
58.92167454,61.46687347,61.46692457,65.24326265,65.24330938,
66.81838801,67.46953204,67.46964169,72.89983173,72.90644953,
74.08206155,75.44675245,75.44685685,76.74713302,76.77050437,
78.05420313,78.17438359,79.7183499,79.72118421,79.72429379,
79.91227229,79.91228745,80.99326008,81.04109716,81.20997602,
82.219385,82.41175016,82.49142149,83.49504942,83.49713412,
84.36266061,84.47724037,84.47833159,84.4859241,85.18879314,
85.19431565,86.18839601,86.18957211,86.32923784,87.17818962,
87.23249526,87.96788031,88.01785229,88.75344863,88.75424891,
89.51806878,89.51810973,89.91613619,89.91614411,90.73453049,
90.73474985,91.40125092,91.41664377,91.85156726,91.85279549,
92.34222988,92.38575227,92.75900826,92.75984105,92.59792734,
92.59772702,91.20024149,91.19975484,90.56381405,89.28641717,
89.28134229,87.14597333,87.14300865,85.15861765,85.15797276,
82.84855607,82.84738518,80.54827418,80.54825753,66.28762042,
63.00718054,63.00700893,54.31636812,54.18091963,51.84551574,
42.2968528,42.29683372,42.29509707,38.97123725,38.95998918,
30.75854662,30.75853234,28.19489508,28.19488261,21.31146526,
21.31063361,20.72423984,20.72422916,19.66675016,19.65459906,
19.30037669,19.30014055,18.82393793,18.82375279,18.89283954,
19.25883033,19.25883262,19.63216496,20.23324209,20.2332487,
24.12129249,24.12130103,25.03004562,25.03004973,25.76826801,
25.81806455,26.67629851,26.67631891,30.46561539,30.74262981,
30.74263179,30.91672453,30.92128596,31.30818311,31.3084222,
31.4588991,31.45890927,31.69236101,31.71975256,31.87221841,
31.87267866,31.9724062,31.97484795,32.10544092,32.10723373,
32.11864839,32.11864583,32.06417118,31.90691934,31.90689831,
31.39692865,31.3969252,31.14906127,31.07254683,29.76210069,
29.76059886,29.26593574,29.26590077,27.78880075,27.76287183,
27.0440895,27.04408511,26.26627309,25.36699318,25.27332511,
23.11069468,23.11069035,21.80609638,21.80609244,18.29594004,
18.295638,18.20182827,18.21706169,18.22000001,18.82965191,
18.98616378,18.98616554,19.69017271,19.6901748,20.01523736,
20.0152395,21.42764295,21.42784833,21.84871481,21.84950598,
21.78327154,21.78313985,21.72088105,21.59779516,21.59778708,
20.92961501,20.92955839,20.91266214,21.04288478,21.05619122,
21.41830913,21.44484473,21.79485625,21.79486561,22.3360831,
22.90340539,22.92289613,23.5358956,23.53620858,25.13688862,
25.13825808,27.00134805,27.00137215,28.16778118,28.16854978,
29.24170683,29.27634617,42.20507624,43.32162108,43.32331588,
68.1726824,68.22889458,70.29050421,70.29055095,72.69085058,
72.69092166,74.59109794,76.51130697,76.6133282,78.82157314,
83.02355351,83.0243163,84.77591304,84.77601173,87.11188547,
87.12441636,91.32441849,91.32446806,94.32811947,95.25177267,
95.25333675,100.83484899,100.83708178,104.85695,104.85952725]

w = [0.09462211,0.04377652,0.14838558,0.05943898,0.15541495,0.13085324,
0.062704,0.06243595,0.08839641,0.08066833,0.08655245,0.13400375,
0.12432887,0.09684014,0.08021506,0.1265312,0.05262315,0.06449225,
0.0758878,0.08259978,0.0806998,0.18695698,0.08123137,0.07486425,
0.12497271,0.10635125,0.15783934,0.08400718,0.24832489,0.12591398,
0.75945646,0.13893085,0.07963481,0.10749733,0.12091365,0.06358539,
0.01520488,0.03701155,0.16559939,0.21220943,0.08161835,0.16941486,
0.08949668,0.13363251,0.09692045,0.18369284,0.13672927,0.08379181,
0.01858722,0.01133573,0.06237293,0.24676197,0.10859054,0.12052321,
0.13203374,0.14501103,0.14774643,0.09328596,0.04971943,0.15257333,
0.28338405,0.09501884,0.98374631,0.05226721,0.09065792,0.13678137,
0.09321165,0.44103697,0.06999414,0.05989127,0.12692356,0.0915057,
0.11936501,0.10480617,0.1567755,0.07513431,0.07117161,0.06858016,
0.01640047,0.29806503,0.22542,0.12794823,0.21382192,0.80778319,
0.05041481,0.02423374,0.14427077,0.10418953,0.21508381,0.10940715,
0.04888315,0.04528401,0.04778175,0.11278799,0.43211842,0.03932949,
0.11803702,0.07236406,0.09973482,0.11787877,0.10316486,0.03396932,
0.07494178,0.0980055,0.15682156,0.39430807,0.2966579,0.0321651,
0.04839777,0.09632106,0.08455145,0.03593526,0.09416399,0.1499288,
0.20404766,0.05393556,0.05599547,0.08508938,0.03143724,0.03516901,
0.48995939,0.22142592,0.05018331,0.02789187,0.02197713,0.03036933,
0.20079765,0.0914527,0.04390079,0.0632086,0.12006129,0.04469976,
0.10379572,0.06173869,0.04413667,0.03245433,0.1633229,0.0436747,
0.06350709,0.05263706,0.04127021,0.04639606,0.06203611,0.0542154,
0.04355763,0.0831866,0.03767251,0.04846966,0.01901781,0.24288383,
0.05686747,0.0987101,0.11008607,0.0864383,0.17024615,0.23189691,
0.13231802,0.11141714,0.11234526,0.04201772,0.10555506,0.23622534,
0.15991181,0.07504075,0.08703826,0.078157,0.07949321,0.29557024,
0.0676124,0.16409294,0.24457436,0.10823184,0.14073518,0.35402206,
0.13634875,0.08048864,0.11152011,0.0714971,0.11998616,0.07697284,
0.08883651,0.08903601,0.12036493,0.07058305,0.01155317,0.08867653,
0.21117371,0.05630087,0.0809992,0.27670459,0.26026519,0.13855529,
0.15380278,0.23979833,0.20920358,0.1440551,0.13541747,0.1463744,
0.04642054,0.24201843,0.11214505,0.18320577,0.04986542,0.19388245,
0.07125025,0.22815885,0.08084224,0.06743765,0.28559653,0.11051846,
1.71124858,0.10948372,0.1427056,0.14208709,0.21579165,0.14818066,
0.10186744,0.02192575,0.07098707,0.09253821,0.13900954,0.12502331,
0.06113228,0.15379208,0.05778127,0.25567184,0.12133423,0.1361762,
0.06835819,0.17893468,0.07440845,0.05984869,0.02637486,0.06455977,
0.05552673,0.16468979,0.06516525,0.06218418,0.10451724,0.4187673,
0.08585429,0.02827772,0.19387362,0.0435158,0.02646894,0.16485791,
0.12165923,0.11332932,0.06376871,0.1044266,0.059728,0.14428556,
0.10856501,0.16783946,0.12808263,0.06199017,0.112075,0.04430589,
0.17411464,0.09521449,0.05556677,0.10710905,0.16484723,0.09197195,
0.09080481,0.11942555,0.29181033,0.05171834,0.10642797,0.0591445,
0.07264419,0.05472201,0.07241181,0.07863706,0.05298894,0.17034628,
0.13621438,0.12312648,0.20335133,0.10124627,0.08194233,0.08727886,
0.07454792,0.17554491,0.13723729,0.04581896,0.11138872,0.09357495,
0.09733723,0.21097312,0.05863559,0.02409536,0.11577836,0.08534116,
0.02837538,0.09877817,0.03943265,0.09397333,0.16018314,0.09669458,
0.18185679,0.11361261,0.07640622,0.0501189,0.1409333,0.04278513,
0.08166752,0.0946821,0.138107,0.05173292]

t = np.linspace(x[0], x[-1], len(x))  # simple t-parameter
xy = np.stack((x, y), axis=0)

s = CubicSmoothingSpline(t, xy, weights=w, smooth=5e-06)
ts = np.linspace(x[0], x[-1], len(x)*3) 

xys = s(ts)
xs2 = xys[0, :]
ys2 = xys[1, :]

plt.plot(x, y, 'o', x, y, '.', xs2, ys2, '-')
plt.show()

# Derivatives
ds = CubicSpline(xs2, ys2)

fig, axes = plt.subplots(1, 3, figsize=(12, 4))

for nu, ax in zip(range(1, 4), axes):
    dy = ds(xs2, nu=nu)
    ax.plot(xs2, dy, '-')
    ax.set_title(f'nu={nu}')
plt.show()

This fixes the problem with close X-values, but you need to determine t-data without close values.

[shusheer]

I think this category of solution comes under my proposed solution (1), "modifying x-values or combining close points, for some definition of close (might be very data-specific)".

For this particular dataset, it's a good result - and a very nice idea, which I'll play with for helping in this issue for my project. In my case, the very close x-values result from almost simultaneous sampling of points in two different data streams, each stream having already been fitted to a cubic smoothing spline, and the y data above is the difference between the two splines. So in my case, the y differences are guaranteed to be very small when the x differences are very small, because the input data has already been smoothed once. So for my specific data, it may be much easier to yield good results than for the more general case of real-world (unsmoothed) y values.

This bivariate solution doesn't change the fact that the CubicSmoothingSpline derivatives are, for some sets of parameters and data, incorrect - so we can't assume that users of the package will manually review the input data every time they do some smoothing in a production environment.

If, more generally, a bivariate spline should be used for unevenly spaced xvalues, then perhaps we should automatically raise a warning for unevenly spaced data suggesting this. At least this would warn users to check their input data if they happen to hit cases where we know CubicSmoothingSpline has issues (if indeed the issue is simply due to xvalue spacing, but the addition of weights causing such a massive change makes me think there is more going on here).