AMO trained on region outside of dashed line, so that’s the cross-validated region, using Descent optimized LTE annual time-series, Python
python3 ts_lte.py amo.dat –cc –plot –low 1930 –high 1960
using the following JSON parameters file
amo.dat.p
{
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"DeltaTime": 7.217156366226141e-06,
"Hold": 0.001560890374528988,
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"Initial": 0.023119471463386495,
"LTE_Amp": 1.2149052076222568,
"LTE_Freq": 232.0780473685175,
"LTE_Phase": -1.98155204056087,
"Periods": [
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13.5961,
13.6708,
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13.78725,
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9.142931547,
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],
"PeriodsAmp": [
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0.11758452315976271,
0.04510213195702457,
0.06361160068822835,
0.05674788795284906,
-0.043657524764462274,
0.07791151774412787,
0.019631216465477552,
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],
"PeriodsPhase": [
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7.500513005887212,
7.860442540394975
],
"Year": 365.2520198,
"final_state": {
"D_prev": 0.04294
}
}Primary 27.2122 > 27.5545 > 27.3216 > others
{
"Aliased": [
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"0.220485143",
"0.5",
"1",
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],
"AliasedAmp": [
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"DC": -0.2680318100166013,
"DeltaTime": 3.339861667887925,
"Hold": 0.0014930905734095634,
"Imp_Amp": 199.01398139940682,
"Imp_Amp2": -0.5731191105049073,
"Imp_Stride": 8,
"Initial": 0.06704092573627028,
"LTE_Amp": 1.3767200538799589,
"LTE_Freq": 125.32556941298645,
"LTE_Phase": 0.7307737940808291,
"LTE_Zero": 1.4258992716041372,
"Periods": [
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"27.3216",
"27.5545",
"13.63339513",
"13.69114014",
"13.6061",
"13.6608",
"13.71877789",
"6795.985773",
"1616.215172",
"2120.513853",
"13.77725",
"3232.430344",
"9.132931547",
"9.108450374",
"9.120674533",
"27.0926041",
"3397.992886",
"9.095011909",
"9.082856397",
"6.809866946",
"2190.530426",
"6.816697567",
"6.823541904",
"1656.572278"
],
"PeriodsAmp": [
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0.20288728084656027,
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0.03511304770515584,
0.011720764708907096,
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0.029609791456868918,
0.039125743540538716,
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0.011857949379825367,
0.01479879984548296,
0.02390111774094945,
-0.039440470787466424,
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"PeriodsPhase": [
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5.8886081102774535,
4.9456215506967265,
4.232011434810574
],
"Year": "365.2495755"
}Alternate Julia fitting routine
Warnemunde, DE tidal station
https://docs.google.com/spreadsheets/d/1HysiqoPN-j1M2lTLUpQaGZPAxvJIesFrJMBoYJyWA5I/edit?usp=sharing
started from Warnemunde SLH model fit
Name Value
—- —–
ALIGN 0
EXTRA 0
FORCING 0
MAX_ITERS 50
RELATIVE 1
STEP 0.05
PS C:\Users\paul\github\pukpr\python\simple\run0> cat .\amo.dat.p
{
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“0.220485143”,
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“1”,
“2”
],
“AliasedAmp”: [
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0.09920867308439774,
-0.1737322618819654,
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0.052057481476465634
],
“AliasedPhase”: [
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],
“DC”: -0.008758390763317466,
“Damp”: -0.022154904178658865,
“DeltaTime”: “3.416666667”,
“Hold”: 0.0014873352555352655,
“Imp_Amp”: 190.5449378494113,
“Imp_Amp2”: 1.466945315006192,
“Imp_Stride”: 7,
“Initial”: 0.060016503798714434,
“LTE_Amp”: 1.587179814234174,
“LTE_Freq”: 125.53536829233441,
“LTE_Phase”: 1.2439579952989555,
“LTE_Zero”: 0.896474895660982,
“Periods”: [
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“13.63339513”,
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“6795.985773”,
“1616.215172”,
“2120.513853”,
“13.77725”,
“3232.430344”,
“9.132931547”,
“9.108450374”,
“9.120674533”,
“27.0926041”,
“3397.992886”,
“9.095011909”,
“9.082856397”,
“6.809866946”,
“2190.530426”,
“6.816697567”,
“6.823541904”,
“1656.572278”
],
“PeriodsAmp”: [
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],
“PeriodsPhase”: [
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],
“Year”: “365.2495755”
}
PS C:\Users\paul\github\pukpr\python\simple\run0>
amo.dat.pcompare to warnemunde reference, warne.dat.p
{
“Aliased”: [
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],
“AliasedAmp”: [
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],
“AliasedPhase”: [
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],
“DC”: -0.008758390763317466,
“Damp”: -0.004169125923760335,
“DeltaTime”: “3.416666667”,
“Hold”: 0.001492802207237816,
“Imp_Amp”: 197.45336102884804,
“Imp_Amp2”: 1.8841384082886352,
“Imp_Stride”: 8,
“Initial”: 0.06797642598445004,
“LTE_Amp”: 1.1234722131058268,
“LTE_Freq”: 129.45433340867712,
“LTE_Phase”: 0.9136521644032515,
“LTE_Zero”: 1.4732244756410906,
“Periods”: [
“27.2122”,
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“9.108450374”,
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],
“PeriodsAmp”: [
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“PeriodsPhase”: [
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6.043091099560879,
4.664388204620863,
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],
“Year”: “365.2495755”
}
warne.dat.p
AMO LTE
“LTE_Amp”: 1.587179814234174,
“LTE_Freq”: 125.53536829233441,
“LTE_Phase”: 1.2439579952989555,
“LTE_Zero”: 0.896474895660982,
Warne LTE
“LTE_Amp”: 1.1234722131058268,
“LTE_Freq”: 129.45433340867712,
“LTE_Phase”: 0.9136521644032515,
“LTE_Zero”: 1.4732244756410906,
AMO Warnemunde
