TsRegressionPreventer::SetResult:
  adjusted: true
  havePreSegment: false
  preActiveAdjusted: false
  preActiveAdjustedWidth: 0.000000
  preOppositeAdjusted: false
  preOppositeAdjustedWidth: 0.000000
  havePostSegment: true
  postActiveAdjusted: true
  postActiveAdjustedWidth: 14.660030
  postOppositeAdjusted: true
  postOppositeAdjustedWidth: 3.401930
-----
Spline:
  hermite false
  preExtrap Held
  postExtrap Held
Knots:
  156.000000: 0.000000, Curve, preSlope 0.000000, postSlope -1.300000, preLen 0.000000, postLen 14.660030, auto false / false
  167.000000: 28.800000, Curve, preSlope 0.400000, postSlope 0.000000, preLen 3.401930, postLen 0.000000, auto false / false
-----
Ts.TsTest_SplineData(isHermite = False, preExtrapolation = Ts.TsTest_SplineData.Extrapolation(method = Ts.TsTest_SplineData.ExtrapHeld), postExtrapolation = Ts.TsTest_SplineData.Extrapolation(method = Ts.TsTest_SplineData.ExtrapHeld), knots = [Ts.TsTest_SplineData.Knot(time = float.fromhex('0x1.38p+7'), nextSegInterpMethod = Ts.TsTest_SplineData.InterpCurve, value = float.fromhex('0x0p+0'), preSlope = float.fromhex('0x0p+0'), postSlope = float.fromhex('-0x1.4cccccccccccdp+0'), preLen = float.fromhex('0x0p+0'), postLen = float.fromhex('0x1.d51ef685d1a68p+3'), preAuto = False, postAuto = False), Ts.TsTest_SplineData.Knot(time = float.fromhex('0x1.4ep+7'), nextSegInterpMethod = Ts.TsTest_SplineData.InterpCurve, value = float.fromhex('0x1.ccccccccccccdp+4'), preSlope = float.fromhex('0x1.999999999999ap-2'), postSlope = float.fromhex('0x0p+0'), preLen = float.fromhex('0x1.b37271a44a374p+1'), postLen = float.fromhex('0x0p+0'), preAuto = False, postAuto = False)])
-----
153.800000 0.000000
156.000000 0.000000
156.054726 -0.070971
156.109453 -0.141591
156.164179 -0.211859
156.218905 -0.281771
156.273632 -0.351324
156.328358 -0.420514
156.383085 -0.489338
156.437811 -0.557793
156.492537 -0.625876
156.547264 -0.693583
156.601990 -0.760911
156.656716 -0.827856
156.711443 -0.894414
156.766169 -0.960581
156.820896 -1.026354
156.875622 -1.091731
156.930348 -1.156705
156.985075 -1.221274
157.039801 -1.285434
157.094527 -1.349180
157.149254 -1.412509
157.203980 -1.475416
157.258706 -1.537897
157.313433 -1.599948
157.368159 -1.661563
157.422886 -1.722740
157.477612 -1.783474
157.532338 -1.843759
157.587065 -1.903592
157.641791 -1.962967
157.696517 -2.021880
157.751244 -2.080325
157.805970 -2.138299
157.860697 -2.195795
157.915423 -2.252808
157.970149 -2.309334
158.024876 -2.365365
158.079602 -2.420899
158.134328 -2.475929
158.189055 -2.530448
158.243781 -2.584452
158.298507 -2.637935
158.353234 -2.690890
158.407960 -2.743311
158.462687 -2.795193
158.517413 -2.846528
158.572139 -2.897311
158.626866 -2.947535
158.681592 -2.997192
158.736318 -3.046276
158.791045 -3.094781
158.845771 -3.142700
158.900498 -3.190024
158.955224 -3.236746
159.009950 -3.282859
159.064677 -3.328356
159.119403 -3.373228
159.174129 -3.417467
159.228856 -3.461065
159.283582 -3.504013
159.338308 -3.546304
159.393035 -3.587926
159.447761 -3.628874
159.502488 -3.669137
159.557214 -3.708706
159.611940 -3.747570
159.666667 -3.785721
159.721393 -3.823149
159.776119 -3.859842
159.830846 -3.895791
159.885572 -3.930985
159.940299 -3.965412
159.995025 -3.999062
160.049751 -4.031923
160.104478 -4.063983
160.159204 -4.095230
160.213930 -4.125652
160.268657 -4.155236
160.323383 -4.183968
160.378109 -4.211836
160.432836 -4.238826
160.487562 -4.264924
160.542289 -4.290114
160.597015 -4.314384
160.651741 -4.337716
160.706468 -4.360096
160.761194 -4.381506
160.815920 -4.401932
160.870647 -4.421356
160.925373 -4.439759
160.980100 -4.457125
161.034826 -4.473435
161.089552 -4.488669
161.144279 -4.502808
161.199005 -4.515832
161.253731 -4.527720
161.308458 -4.538450
161.363184 -4.548000
161.417910 -4.556348
161.472637 -4.563469
161.527363 -4.569339
161.582090 -4.573934
161.636816 -4.577226
161.691542 -4.579190
161.746269 -4.579797
161.800995 -4.579019
161.855721 -4.576826
161.910448 -4.573186
161.965174 -4.568068
162.019900 -4.561440
162.074627 -4.553265
162.129353 -4.543510
162.184080 -4.532136
162.238806 -4.519106
162.293532 -4.504379
162.348259 -4.487914
162.402985 -4.469668
162.457711 -4.449595
162.512438 -4.427649
162.567164 -4.403782
162.621891 -4.377940
162.676617 -4.350072
162.731343 -4.320122
162.786070 -4.288032
162.840796 -4.253741
162.895522 -4.217185
162.950249 -4.178297
163.004975 -4.137008
163.059701 -4.093243
163.114428 -4.046926
163.169154 -3.997975
163.223881 -3.946304
163.278607 -3.891824
163.333333 -3.834438
163.388060 -3.774047
163.442786 -3.710544
163.497512 -3.643816
163.552239 -3.573744
163.606965 -3.500200
163.661692 -3.423051
163.716418 -3.342153
163.771144 -3.257351
163.825871 -3.168483
163.880597 -3.075373
163.935323 -2.977834
163.990050 -2.875663
164.044776 -2.768643
164.099502 -2.656539
164.154229 -2.539097
164.208955 -2.416041
164.263682 -2.287204
164.318408 -2.152044
164.373134 -2.010303
164.427861 -1.861593
164.482587 -1.705490
164.537313 -1.541522
164.592040 -1.369171
164.646766 -1.187858
164.701493 -0.996935
164.756219 -0.795679
164.810945 -0.583271
164.865672 -0.358781
164.920398 -0.121149
164.975124 0.130848
165.029851 0.398626
165.084577 0.683843
165.139303 0.988460
165.194030 1.314820
165.248756 1.665764
165.303483 2.044786
165.358209 2.456263
165.412935 2.905795
165.467662 3.400732
165.522388 3.951038
165.577114 4.570775
165.631841 5.280871
165.686567 6.114828
165.741294 7.132367
165.796020 8.460454
165.850746 10.486902
165.905473 22.155703
165.960199 23.707503
166.014925 24.750757
166.069652 25.555565
166.124378 26.144178
166.179104 26.608640
166.233831 26.985580
166.288557 27.297521
166.343284 27.559142
166.398010 27.780506
166.452736 27.968840
166.507463 28.129516
166.562189 28.266641
166.616915 28.383441
166.671642 28.482492
166.726368 28.565894
166.781095 28.635377
166.835821 28.692391
167.000000 28.800000
169.200000 28.800000
