Sample output of Langbein's est_noise program.

Program estimates the power spectral density (PSD)
of data. PSD functions can be a combination of:
1) white noise
2) Simple power law noise P/f^n
3) a Gauss Markov version P/(fa^n + f^2)
where fa=alpha/2*pi
4) A second power law
5) Band-pass filtered noise

While estimating the PSD function, program also estimates
various parameters that describe the time series including
1) DC term
2) rate
3) sinusoidal amplitudes of specified frequencies
4) rate changes
5) offset

Program can handle data in various formats including
2-color EDM data

Input the data type for automatic processing
most users can will use otr otd or otx
otr=data, such as inferred monument displacements
format of yr, julian day, data, error bar
otd=data, such as inferred monument displacements
format of yrmoda, data, error bar
otx=data, such as inferred monument displacements
format of yr mo da, data, error bar
input number of time series
program will estimate only one set of PSD functions
input the period of interest
start and stop times; year julian_day year julian_day
"julian day" may be decimal day
Day numbers from 1960 13175.0000000000 17839.0000000000

Input the parameters of time series to be estimated

Will rate be estimated? y/n
Rate changes:
time is the "hinge point"
Input the number of rate changes in data

Input the number of periodicities in the data
Input the period (in days) of 1 period
Input the period (in days) of 2 period

Offset: specify first day after offset
Input the number of offsets
Input the time (year jul_day) or (year month day) of 1 offset
Offset at 15375.6161000000
Input the time (year jul_day) or (year month day) of 2 offset
Offset at 15656.9400000000

Input the number of exponentials in time series
The number of exponential time constants that are fixed: 0
rate renomalization is 6.384668
Input the format style of baseline data (otr, otx, otd, tmr, pkf)
Input name of file for baseline number 1
column of A matrix prior to input pressure data 8
Number of files of Auxillary data (pressure)
Number of data read is 4304
Number of model parameters is 8

Average Sampling interval: 1.083895 days
Standard deviation of Ave. interval: 0.8538907 days
Shortest Sampling interval: 1.000000 days
Longest Sampling Interval: 29.00000 days

Input the minimum sampling interval in days to use
Number of points in time series for analysis is 4665

Do you want to get rid of "redundent" data? y/n
Coupled with minimum sampling interval, this can decimate data.
Or, in the case of two baselines with measurements at same time,
it can screw-up design matrix AND make the covariance singular for some cases
After resampling data and getting rid of redundent data
Number of observations is 4304

time, data, and A matrix are in prob1(2).out
prob1.out has stuff after redundencies are removed
prob2.out has stuff in cronological order before redundancies removed

Do you want to substitute real data with random numbers? y/n
Estimate of white noise component of data is: 7.206384
methods to decimate data
0 = no decimation
1 = keep 2, skip 1, keep 2, skip 1
2 = keep 2, skip 1, keep 1, skip 2, keep 2, skip 1...
3 = keep 2, skip 1, keep 1, skip 2, keep 1, skip 3, keep 2 skip 1...
4 or more...make it option 3
input choice
Eigenvalues 1 127.200546298917 2
153.657986228781 3 1221.91656232437 4
2113.71630746652 5 2130.43663134457 6
2170.10470602075 7 2200.15505332048 8
12557.3695800425
Using 8 out of 8 eigenvalues
RMS fit using white noise model is 9.792912
Log MLE for white noise error model is -15923.37

Nomimal value for baseline 1 -28.64 +/- 0.04
Rate in units per year -0.1596 +/- 0.0095
Period of 365.250 days, cos amp= -3.49 +/- 0.02 sin amp= 3.26 +/- 0.02 magnitude= 4.78 +/- 0.02
Period of 182.625 days, cos amp= -0.44 +/- 0.02 sin amp= -0.40 +/- 0.02 magnitude= 0.59 +/- 0.02
Offset number 1 at 2002 34.616 is 40.23 +/- 0.07
Offset number 2 at 2002 315.940 is 6.90 +/- 0.07
sampling interval in yrs 2.737851E-03

Input the initial parameters of the PSD and whether the item is "fix" or "f
loat"
if "fix", then the item is not estimated
if "float", then the item is estimated

Input the white noise "instrument precision" and fix/float
Input the amplitude first Power law function and fix/float
Input the exponent 1 < n < 3 and fix/float
Input the time constant alpha in c/yr and fix/float

Input the parameters for band-passed filtered noise
Input low and high freq stop band in c/yr
low frequency stop band is 0.5000000 c/yr
high frequency stop band is 2.000000 c/yr
number of poles between 1 and 4
Input the amplitude and fix/float
Input the exponent of second Power Law function fix/float
Input the amplitude of second PL function fix/float

Sometimes, it may be necessary to add white noise to data so
that a better estimate of long period PSD parameters can be made.
This is especially true for data that is predominantly power noise
Enter value of white noise to be added (nominal it should be 0)
Calculating power law covariance for first set
Calculating power law covariance for second set

list of trial covariance parameters
white noise PL_1 amp PL_1 exp GM freq BP amp PL_2 amp PL_2 exp determinant chi^2 MLE cpu
1.0000 1.0000 1.0000 0.0000 0.0000 0.0000 2.0000 0.193E+03 292980.438 -150638.297 72.680
0.7000 1.0000 1.0000 0.0000 0.0000 0.0000 2.0000 -0.119E+04 531153.438 -268338.031 65.760
1.0000 1.9500 1.0000 0.0000 0.0000 0.0000 2.0000 0.551E+03 228566.516 -118789.180 65.550
Initial solutions for Amoeba
150638.3 1.000000 1.000000 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
268338.0 0.7000000 1.000000 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
118789.2 1.000000 1.950000 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
150638.3 1.000000 1.000000 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
150638.3 1.000000 1.000000 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
150638.3 1.000000 1.000000 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
150638.3 1.000000 1.000000 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
150638.3 1.000000 1.000000 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
1.3000 1.2714 1.0000 0.0000 0.0000 0.0000 2.0000 0.132E+04 174504.484 -92522.672 65.520
1.6000 1.4071 1.0000 0.0000 0.0000 0.0000 2.0000 0.218E+04 118746.250 -65507.523 65.440
1.1714 1.3878 1.0000 0.0000 0.0000 0.0000 2.0000 0.935E+03 202482.875 -106131.211 65.520
1.2204 1.4985 1.0000 0.0000 0.0000 0.0000 2.0000 0.113E+04 184345.734 -97253.906 65.590
1.2834 1.6410 1.0000 0.0000 0.0000 0.0000 2.0000 0.136E+04 164415.156 -87523.047 65.570
1.3643 1.8241 1.0000 0.0000 0.0000 0.0000 2.0000 0.164E+04 143224.766 -77212.164 65.400
1.4684 2.0596 1.0000 0.0000 0.0000 0.0000 2.0000 0.199E+04 121519.352 -66700.039 65.410
1.6023 2.3623 1.0000 0.0000 0.0000 0.0000 2.0000 0.239E+04 100181.875 -56433.719 65.510
1.9034 3.0435 1.0000 0.0000 0.0000 0.0000 2.0000 0.318E+04 68773.023 -41518.789 65.630
1.8604 1.7247 1.0000 0.0000 0.0000 0.0000 2.0000 0.284E+04 86536.969 -50066.039 65.500
1.8858 2.3833 1.0000 0.0000 0.0000 0.0000 2.0000 0.301E+04 76452.273 -45192.828 65.400
2.0270 2.5253 1.0000 0.0000 0.0000 0.0000 2.0000 0.332E+04 66499.344 -40520.672 65.510
2.4303 3.0386 1.0000 0.0000 0.0000 0.0000 2.0000 0.410E+04 46203.660 -31155.395 65.520
2.2917 2.7822 1.0000 0.0000 0.0000 0.0000 2.0000 0.383E+04 52479.074 -34027.707 65.560
2.4757 2.8727 1.0000 0.0000 0.0000 0.0000 2.0000 0.415E+04 45642.617 -30923.447 65.500
3.0313 3.3970 1.0000 0.0000 0.0000 0.0000 2.0000 0.501E+04 30785.910 -24353.248 65.520
2.8181 3.0194 1.0000 0.0000 0.0000 0.0000 2.0000 0.468E+04 36126.281 -26693.301 65.440
3.0346 4.1325 1.0000 0.0000 0.0000 0.0000 2.0000 0.509E+04 28761.488 -23430.217 65.430
3.7519 5.4952 1.0000 0.0000 0.0000 0.0000 2.0000 0.605E+04 18316.992 -19159.568 65.450
3.3146 4.8922 1.0000 0.0000 0.0000 0.0000 2.0000 0.552E+04 23400.576 -21172.289 65.440
3.6974 4.9505 1.0000 0.0000 0.0000 0.0000 2.0000 0.594E+04 19491.828 -19637.287 65.400
4.1924 4.8351 1.0000 0.0000 0.0000 0.0000 2.0000 0.641E+04 15947.724 -18340.754 65.420
5.3369 5.7309 1.0000 0.0000 0.0000 0.0000 2.0000 0.742E+04 10066.285 -16412.479 65.450
4.6742 5.9389 1.0000 0.0000 0.0000 0.0000 2.0000 0.692E+04 12421.931 -17086.725 65.450
5.1767 6.5111 1.0000 0.0000 0.0000 0.0000 2.0000 0.736E+04 10162.461 -16391.967 65.400
6.5499 8.2473 1.0000 0.0000 0.0000 0.0000 2.0000 0.837E+04 6345.566 -15496.672 65.450
5.8551 8.0240 1.0000 0.0000 0.0000 0.0000 2.0000 0.793E+04 7708.284 -15735.510 65.890
6.4487 8.9684 1.0000 0.0000 0.0000 0.0000 2.0000 0.835E+04 6320.756 -15464.832 65.400
8.1573 11.7540 1.0000 0.0000 0.0000 0.0000 2.0000 0.938E+04 3898.782 -15284.209 65.370
7.5490 9.4338 1.0000 0.0000 0.0000 0.0000 2.0000 0.898E+04 4789.413 -15326.256 65.400
8.2667 10.6565 1.0000 0.0000 0.0000 0.0000 2.0000 0.938E+04 3951.573 -15312.186 65.420
9.5021 11.5864 1.0000 0.0000 0.0000 0.0000 2.0000 0.996E+04 3048.049 -15435.300 65.390
9.9592 12.7562 1.0000 0.0000 0.0000 0.0000 2.0000 0.102E+05 2728.618 -15499.472 65.380
10.6172 14.9715 1.0000 0.0000 0.0000 0.0000 2.0000 0.105E+05 2319.967 -15617.645 65.410
11.4596 14.6633 1.0000 0.0000 0.0000 0.0000 2.0000 0.108E+05 2061.600 -15769.482 65.460
7.2562 9.6839 1.0000 0.0000 0.0000 0.0000 2.0000 0.884E+04 5066.731 -15325.032 65.530
5.7372 6.2051 1.0000 0.0000 0.0000 0.0000 2.0000 0.774E+04 8691.115 -16038.730 65.430
9.3972 12.7799 1.0000 0.0000 0.0000 0.0000 2.0000 0.996E+04 3000.721 -15414.119 65.510
6.2346 8.4272 1.0000 0.0000 0.0000 0.0000 2.0000 0.819E+04 6832.184 -15560.886 65.510
9.0281 11.6739 1.0000 0.0000 0.0000 0.0000 2.0000 0.976E+04 3309.592 -15371.841 65.550
10.3520 13.9151 1.0000 0.0000 0.0000 0.0000 2.0000 0.104E+05 2483.092 -15566.000 65.460
7.5004 9.6643 1.0000 0.0000 0.0000 0.0000 2.0000 0.896E+04 4800.946 -15318.011 65.430
6.8279 10.0268 1.0000 0.0000 0.0000 0.0000 2.0000 0.862E+04 5525.248 -15342.189 65.450
6.1987 8.0467 1.0000 0.0000 0.0000 0.0000 2.0000 0.815E+04 7010.972 -15606.014 65.460
8.5976 11.5966 1.0000 0.0000 0.0000 0.0000 2.0000 0.957E+04 3595.442 -15324.635 65.480
6.4448 9.1306 1.0000 0.0000 0.0000 0.0000 2.0000 0.836E+04 6285.427 -15454.269 65.460
8.3823 11.0381 1.0000 0.0000 0.0000 0.0000 2.0000 0.945E+04 3814.846 -15313.561 65.430
9.0891 11.0667 1.0000 0.0000 0.0000 0.0000 2.0000 0.976E+04 3332.963 -15385.887 65.420
7.3932 10.2868 1.0000 0.0000 0.0000 0.0000 2.0000 0.894E+04 4808.054 -15297.027 65.460
8.3235 11.9034 1.0000 0.0000 0.0000 0.0000 2.0000 0.946E+04 3755.213 -15295.147 65.370
8.9212 12.2875 1.0000 0.0000 0.0000 0.0000 2.0000 0.974E+04 3314.293 -15353.544 65.540
7.6725 10.3348 1.0000 0.0000 0.0000 0.0000 2.0000 0.908E+04 4516.957 -15294.742 65.410
7.3155 10.0142 1.0000 0.0000 0.0000 0.0000 2.0000 0.888E+04 4939.837 -15309.153 65.460
8.3598 12.0465 1.0000 0.0000 0.0000 0.0000 2.0000 0.949E+04 3712.112 -15296.443 65.510
Amoeba exceeding maximum iterations.

Best fitting solutions
MLE= -15284.21
white noise= 8.157310
Bandpass filter amplitude= 0.000000E+00
power law noise 1
amplitude= 11.75405
exponent= 1.000000
G-M freq= 0.000000E+00
power law noise 2
amplitude= 0.000000E+00
exponent= 2.000000

9.7888 11.7540 1.0000 0.0000 0.0000 0.0000 2.0000 0.101E+05 2886.873 -15476.197 65.460
8.1573 22.9204 1.0000 0.0000 0.0000 0.0000 2.0000 0.997E+04 2808.724 -15330.854 65.510
Initial solutions for Amoeba
15284.21 8.157310 11.75405 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
15476.20 9.788773 11.75405 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
15330.85 8.157310 22.92039 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
15284.21 8.157310 11.75405 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
15284.21 8.157310 11.75405 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
15284.21 8.157310 11.75405 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
15284.21 8.157310 11.75405 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
15284.21 8.157310 11.75405 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 2.000000
6.5258 14.9444 1.0000 0.0000 0.0000 0.0000 2.0000 0.877E+04 4950.469 -15202.870 65.590
4.8944 16.5396 1.0000 0.0000 0.0000 0.0000 2.0000 0.804E+04 6856.128 -15426.552 65.530
7.6912 1.4992 1.0000 0.0000 0.0000 0.0000 2.0000 0.879E+04 6659.046 -16076.798 65.520
8.0408 17.5651 1.0000 0.0000 0.0000 0.0000 2.0000 0.962E+04 3342.892 -15250.721 65.570
7.6579 14.3259 1.0000 0.0000 0.0000 0.0000 2.0000 0.928E+04 3973.027 -15220.808 65.480
7.5152 15.0607 1.0000 0.0000 0.0000 0.0000 2.0000 0.925E+04 3994.780 -15207.222 65.440
7.3317 16.0055 1.0000 0.0000 0.0000 0.0000 2.0000 0.923E+04 4022.129 -15192.372 65.470
6.9189 18.1312 1.0000 0.0000 0.0000 0.0000 2.0000 0.917E+04 4077.885 -15168.667 65.450
6.9779 17.8275 1.0000 0.0000 0.0000 0.0000 2.0000 0.918E+04 4070.602 -15171.293 65.450
6.6409 19.5628 1.0000 0.0000 0.0000 0.0000 2.0000 0.915E+04 4108.121 -15159.361 65.420
5.8827 23.4671 1.0000 0.0000 0.0000 0.0000 2.0000 0.913E+04 4143.565 -15155.591 65.450
5.9911 22.9093 1.0000 0.0000 0.0000 0.0000 2.0000 0.913E+04 4143.586 -15154.493 65.590
4.9079 28.4870 1.0000 0.0000 0.0000 0.0000 2.0000 0.920E+04 4049.646 -15183.117 65.510
5.5220 18.6252 1.0000 0.0000 0.0000 0.0000 2.0000 0.856E+04 5394.006 -15211.683 65.460
5.2946 23.0928 1.0000 0.0000 0.0000 0.0000 2.0000 0.885E+04 4715.053 -15166.986 65.440
7.3655 20.0699 1.0000 0.0000 0.0000 0.0000 2.0000 0.949E+04 3512.877 -15203.794 65.450
5.3296 25.0657 1.0000 0.0000 0.0000 0.0000 2.0000 0.905E+04 4322.794 -15162.197 65.420
4.8975 21.4838 1.0000 0.0000 0.0000 0.0000 2.0000 0.853E+04 5480.706 -15226.291 65.470
6.7485 20.4234 1.0000 0.0000 0.0000 0.0000 2.0000 0.926E+04 3905.840 -15165.524 65.450
5.8008 28.1747 1.0000 0.0000 0.0000 0.0000 2.0000 0.948E+04 3531.001 -15203.898 65.530
6.3446 18.2520 1.0000 0.0000 0.0000 0.0000 2.0000 0.892E+04 4572.325 -15162.458 65.520
5.1678 25.4129 1.0000 0.0000 0.0000 0.0000 2.0000 0.901E+04 4392.139 -15165.720 65.530
4.7265 27.1898 1.0000 0.0000 0.0000 0.0000 2.0000 0.902E+04 4412.730 -15179.054 65.560
6.3708 20.3958 1.0000 0.0000 0.0000 0.0000 2.0000 0.909E+04 4212.109 -15154.090 65.540
6.6583 21.4575 1.0000 0.0000 0.0000 0.0000 2.0000 0.929E+04 3837.766 -15167.026 65.510
5.6355 22.6840 1.0000 0.0000 0.0000 0.0000 2.0000 0.896E+04 4479.419 -15156.088 65.520
6.9187 18.3577 1.0000 0.0000 0.0000 0.0000 2.0000 0.919E+04 4047.370 -15168.460 65.440
5.6055 23.6491 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4339.257 -15156.240 65.490
5.0114 24.2689 1.0000 0.0000 0.0000 0.0000 2.0000 0.885E+04 4745.451 -15174.772 65.490
6.3143 21.3848 1.0000 0.0000 0.0000 0.0000 2.0000 0.914E+04 4112.492 -15154.622 65.500
5.4067 27.3354 1.0000 0.0000 0.0000 0.0000 2.0000 0.927E+04 3896.867 -15177.643 65.450
6.1101 20.5228 1.0000 0.0000 0.0000 0.0000 2.0000 0.899E+04 4419.531 -15153.302 65.400
6.6447 19.2238 1.0000 0.0000 0.0000 0.0000 2.0000 0.913E+04 4153.084 -15159.452 65.400
6.3159 20.6842 1.0000 0.0000 0.0000 0.0000 2.0000 0.909E+04 4215.089 -15153.476 65.460
6.5717 19.7932 1.0000 0.0000 0.0000 0.0000 2.0000 0.914E+04 4132.414 -15157.688 65.460
5.8471 22.6851 1.0000 0.0000 0.0000 0.0000 2.0000 0.905E+04 4296.753 -15153.289 65.420
6.6022 20.7586 1.0000 0.0000 0.0000 0.0000 2.0000 0.922E+04 3973.179 -15160.868 65.420
5.8772 22.2027 1.0000 0.0000 0.0000 0.0000 2.0000 0.902E+04 4349.986 -15152.851 65.400
6.3534 19.6143 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4346.274 -15154.950 65.420
6.2357 20.5775 1.0000 0.0000 0.0000 0.0000 2.0000 0.905E+04 4299.920 -15152.851 65.410
5.8994 21.4659 1.0000 0.0000 0.0000 0.0000 2.0000 0.897E+04 4452.893 -15153.516 65.500
6.1965 19.5290 1.0000 0.0000 0.0000 0.0000 2.0000 0.895E+04 4502.954 -15156.316 65.580
6.0424 22.0642 1.0000 0.0000 0.0000 0.0000 2.0000 0.908E+04 4232.460 -15152.586 65.530
5.7229 22.5192 1.0000 0.0000 0.0000 0.0000 2.0000 0.898E+04 4430.932 -15154.572 65.550
6.2088 20.9267 1.0000 0.0000 0.0000 0.0000 2.0000 0.906E+04 4268.928 -15152.562 65.570
6.2826 21.2950 1.0000 0.0000 0.0000 0.0000 2.0000 0.912E+04 4151.414 -15153.802 65.560
5.9952 21.4232 1.0000 0.0000 0.0000 0.0000 2.0000 0.901E+04 4374.979 -15152.454 65.550
5.7745 22.2878 1.0000 0.0000 0.0000 0.0000 2.0000 0.899E+04 4425.007 -15153.998 65.530
6.1806 21.0851 1.0000 0.0000 0.0000 0.0000 2.0000 0.906E+04 4268.485 -15152.417 65.500
6.0005 22.6099 1.0000 0.0000 0.0000 0.0000 2.0000 0.911E+04 4182.114 -15153.563 65.540
6.0827 21.0446 1.0000 0.0000 0.0000 0.0000 2.0000 0.902E+04 4359.525 -15152.402 65.550
6.3308 19.9788 1.0000 0.0000 0.0000 0.0000 2.0000 0.904E+04 4309.770 -15154.043 65.520
5.9680 22.0086 1.0000 0.0000 0.0000 0.0000 2.0000 0.905E+04 4303.811 -15152.383 65.620
6.3267 20.4059 1.0000 0.0000 0.0000 0.0000 2.0000 0.907E+04 4248.056 -15153.568 65.560
5.9896 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4326.403 -15152.272 65.540
Amoeba exceeding maximum iterations.

Best fitting solutions
MLE= -15152.27
white noise= 5.989558
Bandpass filter amplitude= 0.000000E+00
power law noise 1
amplitude= 21.75346
exponent= 1.000000
G-M freq= 0.000000E+00
power law noise 2
amplitude= 0.000000E+00
exponent= 2.000000

5.9896 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4326.403 -15152.272 65.600
Start the covariance calculations for noise model
best estimate 5.989558 dither 1.000000E-02
best estimate 21.75346 dither 1.000000E-02
best estimate 1.000000 dither 5.000000E-02
best estimate 0.000000E+00 dither 5.000000E-02
best estimate 0.000000E+00 dither 5.000000E-02
best estimate 0.000000E+00 dither 5.000000E-02
best estimate 2.000000 dither 0.1000000
5.9297 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.901E+04 4378.312 -15152.646 65.550
Dither changed to 6.000000E-03
5.9536 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.902E+04 4357.449 -15152.455 65.590
Dither changed to 3.600000E-03
5.9680 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.902E+04 4344.996 -15152.368 65.550
Dither changed to 2.160000E-03
5.9766 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4337.546 -15152.324 65.570
Dither changed to 1.296000E-03
5.9818 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4333.084 -15152.302 65.580
Dither changed to 7.776001E-04
5.9849 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4330.411 -15152.289 65.560
5.9942 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.904E+04 4322.401 -15152.258 65.490
5.9896 21.5359 1.0000 0.0000 0.0000 0.0000 2.0000 0.902E+04 4361.599 -15152.377 65.610
Dither changed to 6.000000E-03
5.9896 21.6229 1.0000 0.0000 0.0000 0.0000 2.0000 0.902E+04 4347.481 -15152.315 65.570
Dither changed to 3.600000E-03
5.9896 21.6751 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4339.036 -15152.291 65.550
5.9896 21.8318 1.0000 0.0000 0.0000 0.0000 2.0000 0.904E+04 4313.813 -15152.276 65.570
5.9942 21.8318 1.0000 0.0000 0.0000 0.0000 2.0000 0.904E+04 4309.832 -15152.267 65.570
5.9849 21.8318 1.0000 0.0000 0.0000 0.0000 2.0000 0.904E+04 4317.799 -15152.288 65.590
5.9942 21.6751 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4335.014 -15152.271 65.590
5.9849 21.6751 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4343.063 -15152.313 65.550

Inverse covariance matrix
1 acov= 90.03841 14.05651 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
2 acov= 14.05651 3.662404 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
3 acov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
4 acov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
5 acov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
6 acov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
7 acov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
The eigenvalues 1.432470 92.26835
ier= 0

the covariance matrix
1 cov= 2.770945E-02 -0.1063504 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
2 cov= -0.1063504 0.6812234 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
3 cov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
4 cov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
5 cov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
6 cov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00
7 cov= 0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00

Cross correlation matrix
1 cross correlation 1.000000 -0.7740703 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
2 cross correlation -0.7740703 1.000000 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
3 cross correlation 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
4 cross correlation 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
5 cross correlation 0.000000E+00 0.000000E+00 0.000000E+00
0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00
15152.27


row number for optimal solution is 115
nmod= 8
-32.07200 0.000000E+00 9.846000 0.000000E+00 -3.540000
0.000000E+00 3.686000 0.000000E+00 -0.4670000 0.000000E+00
-0.3010000 0.000000E+00 26.66000 0.000000E+00 5.183000
0.000000E+00
Eigenvalues 1 3.74611910120969D-002 2
7.17193826658182D-002 3 0.127016565108053 4
0.251911574073773 5 1.57312529521336 6
1.62081302617026 7 3.01568244130233 8
3.10585976417084
Using 8 out of 8 eigenvalues
5.9896 21.7535 1.0000 0.0000 0.0000 0.0000 2.0000 0.903E+04 4326.403 -15152.272 68.960
number of rows is 117
-32.07200 2.842000 9.846000 4.446000 -3.540000
0.8020000 3.686000 0.8040000 -0.4670000 0.5710000
-0.3010000 0.5790000 26.66000 3.511000 5.183000
3.502000
Residual, decimated data in resid_dec.out
col 1 & 2, time; 3 is residual,4 is calculated, 5 is data
Residual, data in resid.out
col 1 & 2, time; 3 is residual,4 is calculated, 5 is data


Nomimal value for baseline 1 -32.07 +/- 2.84
Rate in units per year 1.5421 +/- 0.6964
Period of 365.250 days, cos amp= -3.54 +/- 0.80 sin amp= 3.69 +/- 0.80 magnitude= 5.11 +/- 0.80
Period of 182.625 days, cos amp= -0.47 +/- 0.57 sin amp= -0.30 +/- 0.58 magnitude= 0.56 +/- 0.57
Offset number 1 at 2002 34.616 is 26.66 +/- 3.51
Offset number 2 at 2002 315.940 is 5.18 +/- 3.50


Best fitting solutions
MLE= -15152.27
white noise= 5.989558 +/- 0.1664616
Bandpass filter amplitude= 0.000000E+00 +/- 0.000000E+00
power law noise 1
amplitude= 21.75346 +/- 0.8253626
exponent= 1.000000 +/- 0.000000E+00
G-M freq= 0.000000E+00 +/- 0.000000E+00
power law noise 2
amplitude= 0.000000E+00 +/- 0.000000E+00
exponent= 2.000000 +/- 0.000000E+00