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peclayson / ERA_Toolbox

Matlab toolbox for obtaining dependability estimates for ERP measurements
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Issues with convergence and trace plots, and odd error #29

Open KrisDG9 opened 3 years ago

KrisDG9 commented 3 years ago

Context

I ran a model including only pp ID and measurement, using 4 MC chains and 10000 iterations. I only requested the trace plots at 'preferences'. The data consisted of ERNs (about 10-20 per participant) of approximately 150 participants in total.

Three unexpected things happened:

  1. An error that I could not trace back (see at actual behaviour).
  2. The trace plots didn't appear despite the model converging (judging from the 'Model converged' message and the R-hat.
  3. The GUI always asks whether I want to re-run my model using more iterations, which according to the user manual only happens if your model doesn't converge (but it seems to converge, see point 2).

Expected Behavior

  1. No error.
  2. Trace plots appearing.
  3. Not being asked whether I want to re-run.

Actual Behavior

Point 2 and 3 are relatively self-explanatory here I suppose, so I will post the error message from point 1 here:

output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.

Alternatively, I sometimes get the following error:

output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Scale parameter is 0, but must be > 0!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan31-May-2021.stan' at line 19)
If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.

I have not been able to observe a pattern in when I receive which error, but every time I run a model, I get at least one of them.

Possible Fix

No clue, but issues 2 and 3 seem to be related as they both have to do with convergence (maybe issue 1 as well?).

Steps to Reproduce

  1. Load correctly formatted data (csv file): 3012 rows, including one header for pp ID, one for measurement (ERN here), and one for trial (not used in the present model).
  2. Setting the parameters to 4 MC chains and 10000 iterations (though the same issues have occurred using many different settings here).
  3. Request Trace Plots at Preferences.
  4. Click analyse.
  5. GUI asks if I want to rerun with more iterations. Command Window shows much information, including the error reported at 'Actual Behaviour', and states that the model has converged.

Screenshot

era_start

Ensuring dependents are found in the Matlab path
ERA Toolbox files found
CmdStan, MatlabProcessManager, and MatlabStan files found
There is a new version of CmdStan available
MatlabProcessManager version is current
Checking the installed version of MatlabStan is not currently supported

ERP Reliability Analysis Toolbox Version 0.5.2

You are running the most up-to-date version of the toolbox

Dependents will not be updated

Loading Data...

Ensuring dependents are found in the Matlab path
ERA Toolbox files found
CmdStan, MatlabProcessManager, and MatlabStan files found
There is a new version of CmdStan available
MatlabProcessManager version is current
Checking the installed version of MatlabStan is not currently supported

ERP Reliability Analysis Toolbox Version 0.5.2

You are running the most up-to-date version of the toolbox

Dependents will not be updated

Loading Data...
This may take awhile depending on the amount of data...

Preparing data for analysis...

Model is being run in cmdstan

This may take a while depending on the amount of data
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-1.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-1.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-1.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-1.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
Inference for Stan model: cmdstan07_Oct_2021_model
4 chains: each with iter=(5000,5000,5000,5000); warmup=(0,0,0,0); thin=(1,1,1,1); 20000 iterations saved.
Warmup took (12, 12, 12, 12) seconds, 49 seconds total
Sampling took (27, 26, 26, 26) seconds, 1.8 minutes total
                    Mean     MCSE   StdDev        5%       50%       95%    N_Eff  N_Eff/s    R_hat
lp__           -9.1e+003 1.9e-001 1.2e+001 -9.2e+003 -9.1e+003 -9.1e+003 4.1e+003 3.9e+001 1.0e+000
accept_stat__   8.8e-001 6.9e-003 1.1e-001  6.6e-001  9.2e-001  1.0e+000 2.6e+002 2.5e+000 1.0e+000
stepsize__      3.1e-001 1.1e-002 1.6e-002  2.9e-001  3.2e-001  3.3e-001 2.0e+000 1.9e-002 2.6e+012
treedepth__     4.0e+000      nan 3.1e-013  4.0e+000  4.0e+000  4.0e+000      nan      nan      nan
n_leapfrog__    1.5e+001      nan 4.6e-013  1.5e+001  1.5e+001  1.5e+001      nan      nan      nan
divergent__     0.0e+000      nan 0.0e+000  0.0e+000  0.0e+000  0.0e+000      nan      nan      nan
energy__        9.2e+003 2.4e-001 1.5e+001  9.2e+003  9.2e+003  9.2e+003 4.0e+003 3.8e+001 1.0e+000
mu             -8.4e+000 6.3e-003 5.4e-001 -9.2e+000 -8.4e+000 -7.5e+000 7.4e+003 7.0e+001 1.0e+000
sig_u           5.6e+000 5.5e-003 4.5e-001  4.9e+000  5.5e+000  6.3e+000 6.7e+003 6.3e+001 1.0e+000
sig_e           1.2e+001 8.3e-004 1.6e-001  1.2e+001  1.2e+001  1.3e+001 3.8e+004 3.6e+002 1.0e+000
u_raw[1]       -7.2e-002 1.8e-003 3.0e-001 -5.6e-001 -7.1e-002  4.1e-001 2.8e+004 2.6e+002 1.0e+000
u_raw[2]        5.3e-001 2.7e-003 5.5e-001 -3.7e-001  5.3e-001  1.4e+000 4.1e+004 3.9e+002 1.0e+000
u_raw[3]        4.5e-002 4.3e-003 9.3e-001 -1.5e+000  4.1e-002  1.6e+000 4.7e+004 4.4e+002 1.0e+000
u_raw[4]       -1.1e+000 2.4e-003 4.7e-001 -1.8e+000 -1.1e+000 -3.0e-001 3.7e+004 3.5e+002 1.0e+000
u_raw[5]        6.5e-001 2.0e-003 3.5e-001  8.5e-002  6.4e-001  1.2e+000 3.0e+004 2.9e+002 1.0e+000
u_raw[6]        5.2e-001 3.5e-003 7.5e-001 -7.1e-001  5.2e-001  1.7e+000 4.7e+004 4.4e+002 1.0e+000
u_raw[7]        1.2e+000 2.5e-003 5.1e-001  3.4e-001  1.2e+000  2.0e+000 4.0e+004 3.8e+002 1.0e+000
u_raw[8]       -1.4e+000 2.7e-003 5.5e-001 -2.4e+000 -1.4e+000 -5.6e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[9]        3.4e-001 2.4e-003 4.8e-001 -4.3e-001  3.4e-001  1.1e+000 3.9e+004 3.7e+002 1.0e+000
u_raw[10]      -4.0e-002 2.4e-003 4.9e-001 -8.4e-001 -4.0e-002  7.7e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[11]      -1.9e+000 2.9e-003 5.8e-001 -2.9e+000 -1.9e+000 -9.6e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[12]       2.0e-001 2.2e-003 4.1e-001 -4.8e-001  2.0e-001  8.8e-001 3.6e+004 3.4e+002 1.0e+000
u_raw[13]       3.7e-001 3.3e-003 7.1e-001 -8.0e-001  3.6e-001  1.5e+000 4.5e+004 4.3e+002 1.0e+000
u_raw[14]      -1.3e+000 2.3e-003 3.9e-001 -2.0e+000 -1.3e+000 -7.1e-001 2.9e+004 2.7e+002 1.0e+000
u_raw[15]       3.3e-002 3.9e-003 8.4e-001 -1.3e+000  3.1e-002  1.4e+000 4.7e+004 4.4e+002 1.0e+000
u_raw[16]       7.1e-001 2.2e-003 4.1e-001  3.8e-002  7.1e-001  1.4e+000 3.7e+004 3.5e+002 1.0e+000
u_raw[17]      -1.5e-001 4.1e-003 9.2e-001 -1.7e+000 -1.5e-001  1.3e+000 5.0e+004 4.7e+002 1.0e+000
u_raw[18]      -2.0e-001 2.9e-003 6.0e-001 -1.2e+000 -2.0e-001  7.8e-001 4.4e+004 4.1e+002 1.0e+000
u_raw[19]      -7.5e-001 2.8e-003 6.0e-001 -1.7e+000 -7.5e-001  2.3e-001 4.5e+004 4.3e+002 1.0e+000
u_raw[20]       5.6e-001 2.6e-003 5.3e-001 -3.0e-001  5.6e-001  1.4e+000 4.0e+004 3.8e+002 1.0e+000
u_raw[21]       1.0e+000 3.3e-003 6.9e-001 -1.4e-001  1.0e+000  2.2e+000 4.5e+004 4.3e+002 1.0e+000
u_raw[22]      -1.1e+000 2.0e-003 3.4e-001 -1.7e+000 -1.1e+000 -5.7e-001 2.8e+004 2.7e+002 1.0e+000
u_raw[23]       1.5e-001 2.5e-003 5.1e-001 -6.9e-001  1.5e-001  9.8e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[24]       1.5e+000 2.3e-003 3.4e-001  9.1e-001  1.5e+000  2.0e+000 2.3e+004 2.1e+002 1.0e+000
u_raw[25]      -7.3e-001 2.3e-003 4.4e-001 -1.5e+000 -7.3e-001 -2.1e-002 3.7e+004 3.5e+002 1.0e+000
u_raw[26]       5.9e-001 3.2e-003 6.8e-001 -5.3e-001  5.8e-001  1.7e+000 4.6e+004 4.4e+002 1.0e+000
u_raw[27]       1.6e-001 2.9e-003 6.0e-001 -8.2e-001  1.6e-001  1.1e+000 4.2e+004 4.0e+002 1.0e+000
u_raw[28]       1.2e+000 2.9e-003 6.3e-001  1.8e-001  1.2e+000  2.2e+000 4.6e+004 4.4e+002 1.0e+000
u_raw[29]       1.3e+000 2.0e-003 2.4e-001  8.8e-001  1.3e+000  1.7e+000 1.5e+004 1.4e+002 1.0e+000
u_raw[30]       1.1e+000 2.6e-003 5.3e-001  2.6e-001  1.1e+000  2.0e+000 4.1e+004 3.9e+002 1.0e+000
u_raw[31]      -8.0e-001 3.4e-003 7.5e-001 -2.0e+000 -8.0e-001  4.4e-001 4.8e+004 4.6e+002 1.0e+000
u_raw[32]       8.8e-003 3.1e-003 6.8e-001 -1.1e+000  8.1e-003  1.1e+000 4.8e+004 4.5e+002 1.0e+000
u_raw[33]       5.5e-001 2.2e-003 4.2e-001 -1.4e-001  5.5e-001  1.2e+000 3.7e+004 3.5e+002 1.0e+000
u_raw[34]      -5.3e-002 1.7e-003 2.9e-001 -5.3e-001 -5.2e-002  4.3e-001 3.0e+004 2.8e+002 1.0e+000
u_raw[35]      -3.2e-001 2.0e-003 3.8e-001 -9.5e-001 -3.2e-001  3.0e-001 3.6e+004 3.4e+002 1.0e+000
u_raw[36]      -3.3e-001 2.2e-003 4.3e-001 -1.0e+000 -3.3e-001  3.7e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[37]       7.4e-001 2.3e-003 4.3e-001  3.6e-002  7.4e-001  1.4e+000 3.5e+004 3.3e+002 1.0e+000
u_raw[38]       3.4e-001 2.3e-003 4.5e-001 -4.1e-001  3.4e-001  1.1e+000 3.8e+004 3.6e+002 1.0e+000
u_raw[39]      -6.5e-001 4.3e-003 9.1e-001 -2.1e+000 -6.5e-001  8.7e-001 4.4e+004 4.2e+002 1.0e+000
u_raw[40]      -7.0e-001 2.0e-003 3.6e-001 -1.3e+000 -7.0e-001 -1.2e-001 3.1e+004 3.0e+002 1.0e+000
u_raw[41]      -1.0e+000 3.6e-003 8.0e-001 -2.4e+000 -1.0e+000  2.7e-001 5.1e+004 4.8e+002 1.0e+000
u_raw[42]      -3.3e-001 2.5e-003 5.1e-001 -1.1e+000 -3.3e-001  5.0e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[43]      -7.6e-001 2.1e-003 4.0e-001 -1.4e+000 -7.6e-001 -1.0e-001 3.5e+004 3.4e+002 1.0e+000
u_raw[44]      -1.6e+000 3.5e-003 7.4e-001 -2.9e+000 -1.6e+000 -4.1e-001 4.3e+004 4.1e+002 1.0e+000
u_raw[45]       8.4e-003 2.7e-003 5.2e-001 -8.3e-001  6.9e-003  8.5e-001 3.8e+004 3.6e+002 1.0e+000
u_raw[46]       6.6e-001 3.0e-003 6.2e-001 -3.7e-001  6.6e-001  1.7e+000 4.4e+004 4.1e+002 1.0e+000
u_raw[47]      -1.4e+000 2.7e-003 5.5e-001 -2.3e+000 -1.4e+000 -4.9e-001 4.3e+004 4.1e+002 1.0e+000
u_raw[48]      -6.1e-002 2.4e-003 4.7e-001 -8.3e-001 -6.3e-002  7.1e-001 3.8e+004 3.6e+002 1.0e+000
u_raw[49]      -4.8e-002 3.6e-003 7.5e-001 -1.3e+000 -5.2e-002  1.2e+000 4.3e+004 4.1e+002 1.0e+000
u_raw[50]      -4.6e-001 2.3e-003 4.7e-001 -1.2e+000 -4.6e-001  3.2e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[51]       5.7e-002 2.4e-003 4.6e-001 -7.1e-001  5.9e-002  8.2e-001 3.8e+004 3.6e+002 1.0e+000
u_raw[52]      -2.0e-002 2.6e-003 5.3e-001 -8.8e-001 -1.8e-002  8.4e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[53]      -3.0e-002 2.8e-003 5.8e-001 -9.9e-001 -3.0e-002  9.2e-001 4.2e+004 4.0e+002 1.0e+000
u_raw[54]      -1.1e+000 2.9e-003 5.8e-001 -2.0e+000 -1.1e+000 -1.2e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[55]      -1.5e-001 3.1e-003 6.8e-001 -1.3e+000 -1.6e-001  9.7e-001 4.8e+004 4.5e+002 1.0e+000
u_raw[56]       6.6e-003 1.9e-003 3.7e-001 -6.0e-001  6.1e-003  6.1e-001 3.6e+004 3.5e+002 1.0e+000
u_raw[57]       1.9e+000 2.4e-003 2.8e-001  1.5e+000  1.9e+000  2.4e+000 1.3e+004 1.3e+002 1.0e+000
u_raw[58]       1.5e+000 2.3e-003 3.6e-001  9.5e-001  1.5e+000  2.1e+000 2.3e+004 2.2e+002 1.0e+000
u_raw[59]       7.6e-001 2.0e-003 3.5e-001  1.9e-001  7.5e-001  1.3e+000 3.2e+004 3.1e+002 1.0e+000
u_raw[60]       4.6e-001 3.4e-003 7.0e-001 -7.0e-001  4.5e-001  1.6e+000 4.4e+004 4.2e+002 1.0e+000
u_raw[61]       8.1e-001 3.8e-003 8.4e-001 -5.9e-001  8.1e-001  2.2e+000 5.0e+004 4.7e+002 1.0e+000
u_raw[62]       6.0e-001 3.7e-003 8.0e-001 -7.0e-001  6.1e-001  1.9e+000 4.7e+004 4.4e+002 1.0e+000
u_raw[63]       7.8e-001 2.6e-003 5.0e-001 -3.7e-002  7.7e-001  1.6e+000 3.7e+004 3.5e+002 1.0e+000
u_raw[64]      -8.6e-001 3.5e-003 7.6e-001 -2.1e+000 -8.7e-001  4.0e-001 4.7e+004 4.5e+002 1.0e+000
u_raw[65]       1.8e-001 2.3e-003 4.4e-001 -5.3e-001  1.8e-001  9.0e-001 3.7e+004 3.6e+002 1.0e+000
u_raw[66]       3.7e-001 2.2e-003 4.1e-001 -3.0e-001  3.6e-001  1.0e+000 3.6e+004 3.4e+002 1.0e+000
u_raw[67]       1.3e+000 2.4e-003 4.4e-001  5.6e-001  1.3e+000  2.0e+000 3.3e+004 3.1e+002 1.0e+000
u_raw[68]       6.1e-001 2.2e-003 4.3e-001 -1.0e-001  6.0e-001  1.3e+000 4.0e+004 3.8e+002 1.0e+000
u_raw[69]       2.1e-001 2.3e-003 4.6e-001 -5.3e-001  2.1e-001  9.7e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[70]       5.0e-002 2.6e-003 5.2e-001 -8.0e-001  4.8e-002  9.0e-001 4.1e+004 3.9e+002 1.0e+000
u_raw[71]      -2.2e-002 4.3e-003 9.2e-001 -1.5e+000 -2.5e-002  1.5e+000 4.6e+004 4.4e+002 1.0e+000
u_raw[72]       1.2e+000 2.2e-003 3.9e-001  5.2e-001  1.1e+000  1.8e+000 3.0e+004 2.9e+002 1.0e+000
u_raw[73]       6.7e-001 1.9e-003 3.3e-001  1.4e-001  6.7e-001  1.2e+000 2.8e+004 2.7e+002 1.0e+000
u_raw[74]       6.0e-001 3.9e-003 8.3e-001 -7.7e-001  6.0e-001  2.0e+000 4.6e+004 4.4e+002 1.0e+000
u_raw[75]       9.4e-002 2.3e-003 4.5e-001 -6.3e-001  9.4e-002  8.3e-001 3.7e+004 3.5e+002 1.0e+000
u_raw[76]      -1.9e-001 2.0e-003 3.6e-001 -8.0e-001 -1.9e-001  3.9e-001 3.4e+004 3.2e+002 1.0e+000
u_raw[77]       2.7e-002 1.9e-003 3.6e-001 -5.6e-001  2.6e-002  6.1e-001 3.4e+004 3.2e+002 1.0e+000
u_raw[78]      -5.3e-001 3.7e-003 7.9e-001 -1.8e+000 -5.3e-001  7.8e-001 4.4e+004 4.2e+002 1.0e+000
u_raw[79]      -5.8e-001 2.2e-003 4.2e-001 -1.3e+000 -5.8e-001  1.1e-001 3.7e+004 3.5e+002 1.0e+000
u_raw[80]       5.9e-001 2.7e-003 5.7e-001 -3.4e-001  5.9e-001  1.5e+000 4.5e+004 4.3e+002 1.0e+000
u_raw[81]       7.8e-002 4.3e-003 9.3e-001 -1.4e+000  7.8e-002  1.6e+000 4.6e+004 4.3e+002 1.0e+000
u_raw[82]       5.9e-001 2.4e-003 4.8e-001 -2.0e-001  5.9e-001  1.4e+000 4.0e+004 3.8e+002 1.0e+000
u_raw[83]       6.2e-001 2.4e-003 4.5e-001 -1.2e-001  6.1e-001  1.4e+000 3.6e+004 3.5e+002 1.0e+000
u_raw[84]      -1.3e-001 3.1e-003 7.0e-001 -1.3e+000 -1.3e-001  1.0e+000 5.0e+004 4.7e+002 1.0e+000
u_raw[85]      -6.4e-001 2.9e-003 6.0e-001 -1.6e+000 -6.4e-001  3.3e-001 4.4e+004 4.2e+002 1.0e+000
u_raw[86]      -1.0e+000 2.1e-003 3.9e-001 -1.7e+000 -1.0e+000 -3.8e-001 3.4e+004 3.2e+002 1.0e+000
u_raw[87]       8.1e-001 2.6e-003 5.0e-001 -2.0e-002  8.1e-001  1.6e+000 3.8e+004 3.7e+002 1.0e+000
u_raw[88]       6.9e-002 2.3e-003 4.2e-001 -6.3e-001  6.6e-002  7.6e-001 3.5e+004 3.3e+002 1.0e+000
u_raw[89]       4.6e-001 1.8e-003 3.0e-001 -4.2e-002  4.6e-001  9.7e-001 2.8e+004 2.7e+002 1.0e+000
u_raw[90]      -1.6e+000 3.2e-003 6.7e-001 -2.7e+000 -1.6e+000 -5.2e-001 4.4e+004 4.2e+002 1.0e+000
u_raw[91]      -1.4e+000 2.2e-003 3.9e-001 -2.0e+000 -1.4e+000 -7.2e-001 3.1e+004 2.9e+002 1.0e+000
u_raw[92]       4.8e-001 2.1e-003 4.2e-001 -2.0e-001  4.8e-001  1.2e+000 3.7e+004 3.6e+002 1.0e+000
u_raw[93]      -1.3e+000 2.6e-003 5.0e-001 -2.2e+000 -1.3e+000 -5.2e-001 3.7e+004 3.6e+002 1.0e+000
u_raw[94]      -3.3e+000 2.9e-003 4.8e-001 -4.1e+000 -3.3e+000 -2.5e+000 2.7e+004 2.6e+002 1.0e+000
u_raw[95]       1.4e+000 2.7e-003 5.3e-001  4.8e-001  1.3e+000  2.2e+000 3.9e+004 3.7e+002 1.0e+000
u_raw[96]      -1.8e+000 2.4e-003 4.4e-001 -2.5e+000 -1.8e+000 -1.1e+000 3.3e+004 3.1e+002 1.0e+000
u_raw[97]       5.7e-001 1.9e-003 3.2e-001  5.0e-002  5.6e-001  1.1e+000 2.6e+004 2.5e+002 1.0e+000
u_raw[98]       4.3e-001 2.6e-003 4.9e-001 -3.6e-001  4.4e-001  1.2e+000 3.6e+004 3.4e+002 1.0e+000
u_raw[99]       1.3e+000 2.4e-003 4.8e-001  5.5e-001  1.3e+000  2.1e+000 3.9e+004 3.7e+002 1.0e+000
u_raw[100]     -1.3e+000 3.3e-003 6.7e-001 -2.4e+000 -1.3e+000 -2.3e-001 4.2e+004 4.0e+002 1.0e+000
u_raw[101]      2.1e-001 2.7e-003 5.4e-001 -6.8e-001  2.1e-001  1.1e+000 4.1e+004 3.9e+002 1.0e+000
u_raw[102]      2.2e+000 2.6e-003 4.6e-001  1.4e+000  2.2e+000  2.9e+000 3.1e+004 2.9e+002 1.0e+000
u_raw[103]     -2.6e-001 2.7e-003 6.1e-001 -1.3e+000 -2.6e-001  7.3e-001 5.0e+004 4.7e+002 1.0e+000
u_raw[104]      1.4e+000 2.0e-003 2.4e-001  9.7e-001  1.4e+000  1.8e+000 1.4e+004 1.3e+002 1.0e+000
u_raw[105]     -3.1e-001 2.9e-003 6.2e-001 -1.3e+000 -3.1e-001  7.0e-001 4.4e+004 4.2e+002 1.0e+000
u_raw[106]      3.4e-002 2.1e-003 4.0e-001 -6.1e-001  3.2e-002  6.9e-001 3.7e+004 3.5e+002 1.0e+000
u_raw[107]      5.6e-001 2.4e-003 4.7e-001 -2.1e-001  5.6e-001  1.3e+000 3.9e+004 3.7e+002 1.0e+000
u_raw[108]      5.0e-001 2.6e-003 5.1e-001 -3.5e-001  5.0e-001  1.3e+000 3.8e+004 3.6e+002 1.0e+000
u_raw[109]      4.9e-001 2.1e-003 4.1e-001 -1.8e-001  4.9e-001  1.2e+000 3.6e+004 3.4e+002 1.0e+000
u_raw[110]     -3.1e-001 2.6e-003 5.4e-001 -1.2e+000 -3.1e-001  5.6e-001 4.2e+004 4.0e+002 1.0e+000
u_raw[111]      5.6e-001 2.5e-003 5.1e-001 -2.8e-001  5.6e-001  1.4e+000 4.1e+004 3.9e+002 1.0e+000
u_raw[112]      7.4e-002 2.6e-003 5.4e-001 -8.2e-001  7.1e-002  9.6e-001 4.3e+004 4.1e+002 1.0e+000
u_raw[113]      6.9e-001 2.0e-003 3.6e-001  1.1e-001  6.9e-001  1.3e+000 3.3e+004 3.2e+002 1.0e+000
u_raw[114]      4.0e-001 4.2e-003 9.2e-001 -1.1e+000  4.0e-001  1.9e+000 4.9e+004 4.6e+002 1.0e+000
u_raw[115]      7.0e-002 2.3e-003 4.4e-001 -6.6e-001  7.0e-002  8.0e-001 3.7e+004 3.5e+002 1.0e+000
u_raw[116]     -4.5e-001 2.2e-003 4.2e-001 -1.1e+000 -4.4e-001  2.5e-001 3.7e+004 3.6e+002 1.0e+000
u_raw[117]     -5.1e-001 2.0e-003 3.7e-001 -1.1e+000 -5.1e-001  9.7e-002 3.5e+004 3.3e+002 1.0e+000
u_raw[118]      7.1e-001 3.5e-003 7.5e-001 -5.3e-001  7.1e-001  1.9e+000 4.5e+004 4.3e+002 1.0e+000
u_raw[119]      5.4e-001 2.0e-003 3.8e-001 -8.6e-002  5.4e-001  1.2e+000 3.5e+004 3.3e+002 1.0e+000
u_raw[120]     -1.1e+000 2.2e-003 4.0e-001 -1.7e+000 -1.1e+000 -4.3e-001 3.3e+004 3.2e+002 1.0e+000
u_raw[121]     -6.0e-001 2.3e-003 4.5e-001 -1.3e+000 -6.0e-001  1.3e-001 3.8e+004 3.6e+002 1.0e+000
u_raw[122]      1.0e+000 2.4e-003 4.6e-001  2.4e-001  9.9e-001  1.8e+000 3.6e+004 3.4e+002 1.0e+000
u_raw[123]     -1.8e-001 2.5e-003 5.1e-001 -1.0e+000 -1.7e-001  6.6e-001 4.3e+004 4.1e+002 1.0e+000
u_raw[124]     -1.3e+000 2.8e-003 5.8e-001 -2.3e+000 -1.3e+000 -3.7e-001 4.5e+004 4.3e+002 1.0e+000
u_raw[125]     -7.0e-002 3.7e-003 7.9e-001 -1.4e+000 -7.2e-002  1.2e+000 4.6e+004 4.4e+002 1.0e+000
u_raw[126]     -6.7e-001 2.5e-003 4.9e-001 -1.5e+000 -6.7e-001  1.3e-001 3.9e+004 3.7e+002 1.0e+000
u_raw[127]      3.4e-001 3.0e-003 6.2e-001 -6.8e-001  3.4e-001  1.4e+000 4.3e+004 4.0e+002 1.0e+000
u_raw[128]      7.2e-002 2.2e-003 4.1e-001 -6.0e-001  7.0e-002  7.4e-001 3.6e+004 3.4e+002 1.0e+000
u_raw[129]      1.6e-001 2.4e-003 4.9e-001 -6.5e-001  1.6e-001  9.6e-001 4.2e+004 4.0e+002 1.0e+000
u_raw[130]      2.5e-001 2.0e-003 3.5e-001 -3.3e-001  2.5e-001  8.2e-001 3.1e+004 3.0e+002 1.0e+000
u_raw[131]      2.6e-001 3.1e-003 6.5e-001 -8.1e-001  2.6e-001  1.3e+000 4.5e+004 4.2e+002 1.0e+000
u_raw[132]      8.1e-002 3.8e-003 8.4e-001 -1.3e+000  7.6e-002  1.5e+000 4.8e+004 4.6e+002 1.0e+000
u_raw[133]     -5.1e-001 4.0e-003 8.6e-001 -1.9e+000 -5.0e-001  9.1e-001 4.6e+004 4.4e+002 1.0e+000
u_raw[134]     -4.5e-001 2.8e-003 5.7e-001 -1.4e+000 -4.5e-001  4.9e-001 4.3e+004 4.1e+002 1.0e+000
u_raw[135]      6.2e-001 3.4e-003 7.2e-001 -5.7e-001  6.2e-001  1.8e+000 4.5e+004 4.2e+002 1.0e+000
u_raw[136]     -1.1e+000 2.2e-003 4.0e-001 -1.8e+000 -1.1e+000 -4.5e-001 3.3e+004 3.1e+002 1.0e+000
u_raw[137]      1.4e+000 2.3e-003 3.2e-001  9.1e-001  1.4e+000  2.0e+000 2.0e+004 1.9e+002 1.0e+000
u_raw[138]     -2.1e-001 1.9e-003 3.6e-001 -8.0e-001 -2.1e-001  3.8e-001 3.4e+004 3.2e+002 1.0e+000
u_raw[139]     -3.3e-001 2.1e-003 3.9e-001 -9.7e-001 -3.3e-001  3.1e-001 3.5e+004 3.3e+002 1.0e+000
u_raw[140]      2.5e-001 2.3e-003 4.7e-001 -5.1e-001  2.5e-001  1.0e+000 4.0e+004 3.8e+002 1.0e+000
u_raw[141]     -1.3e+000 2.8e-003 5.7e-001 -2.3e+000 -1.3e+000 -3.7e-001 4.1e+004 3.9e+002 1.0e+000
u_raw[142]     -9.9e-001 2.7e-003 5.5e-001 -1.9e+000 -9.9e-001 -1.1e-001 4.2e+004 4.0e+002 1.0e+000
u_raw[143]      8.6e-002 2.0e-003 3.7e-001 -5.3e-001  8.2e-002  7.0e-001 3.4e+004 3.3e+002 1.0e+000
u_raw[144]      5.5e-002 4.2e-003 9.2e-001 -1.5e+000  5.1e-002  1.6e+000 4.8e+004 4.6e+002 1.0e+000
u_raw[145]     -4.5e-001 2.4e-003 4.7e-001 -1.2e+000 -4.5e-001  3.3e-001 4.0e+004 3.8e+002 1.0e+000
u_raw[146]     -1.8e+000 2.2e-003 3.4e-001 -2.4e+000 -1.8e+000 -1.3e+000 2.3e+004 2.2e+002 1.0e+000
u_raw[147]     -6.4e-001 2.7e-003 5.8e-001 -1.6e+000 -6.4e-001  3.1e-001 4.5e+004 4.3e+002 1.0e+000
u_raw[148]     -5.0e-001 2.9e-003 6.0e-001 -1.5e+000 -5.0e-001  4.9e-001 4.3e+004 4.0e+002 1.0e+000
u_raw[149]      3.5e-001 2.2e-003 4.1e-001 -3.2e-001  3.4e-001  1.0e+000 3.6e+004 3.4e+002 1.0e+000
u_raw[150]      1.6e-001 2.9e-003 6.2e-001 -8.6e-001  1.6e-001  1.2e+000 4.5e+004 4.3e+002 1.0e+000
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u[2]           -5.4e+000 1.4e-002 3.0e+000 -1.0e+001 -5.4e+000 -4.8e-001 4.5e+004 4.3e+002 1.0e+000
u[3]           -8.1e+000 2.5e-002 5.2e+000 -1.7e+001 -8.1e+000  3.9e-001 4.4e+004 4.2e+002 1.0e+000
u[4]           -1.4e+001 1.2e-002 2.5e+000 -1.8e+001 -1.4e+001 -1.0e+001 4.5e+004 4.3e+002 1.0e+000
u[5]           -4.8e+000 8.7e-003 1.8e+000 -7.8e+000 -4.8e+000 -1.7e+000 4.5e+004 4.3e+002 1.0e+000
u[6]           -5.5e+000 2.0e-002 4.1e+000 -1.2e+001 -5.5e+000  1.4e+000 4.5e+004 4.2e+002 1.0e+000
u[7]           -1.9e+000 1.3e-002 2.8e+000 -6.5e+000 -1.9e+000  2.6e+000 4.6e+004 4.4e+002 1.0e+000
u[8]           -1.6e+001 1.5e-002 3.0e+000 -2.1e+001 -1.6e+001 -1.1e+001 4.2e+004 4.0e+002 1.0e+000
u[9]           -6.5e+000 1.2e-002 2.6e+000 -1.1e+001 -6.5e+000 -2.3e+000 4.7e+004 4.5e+002 1.0e+000
u[10]          -8.6e+000 1.2e-002 2.6e+000 -1.3e+001 -8.6e+000 -4.2e+000 4.7e+004 4.4e+002 1.0e+000
u[11]          -1.9e+001 1.7e-002 3.2e+000 -2.4e+001 -1.9e+001 -1.4e+001 3.8e+004 3.6e+002 1.0e+000
u[12]          -7.3e+000 1.1e-002 2.2e+000 -1.1e+001 -7.3e+000 -3.6e+000 4.5e+004 4.3e+002 1.0e+000
u[13]          -6.3e+000 1.9e-002 4.0e+000 -1.3e+001 -6.4e+000  2.6e-001 4.5e+004 4.3e+002 1.0e+000
u[14]          -1.6e+001 1.0e-002 2.1e+000 -1.9e+001 -1.6e+001 -1.2e+001 4.3e+004 4.1e+002 1.0e+000
u[15]          -8.2e+000 2.2e-002 4.7e+000 -1.6e+001 -8.2e+000 -5.8e-001 4.5e+004 4.2e+002 1.0e+000
u[16]          -4.4e+000 1.0e-002 2.2e+000 -8.1e+000 -4.4e+000 -7.4e-001 4.9e+004 4.7e+002 1.0e+000
u[17]          -9.2e+000 2.4e-002 5.1e+000 -1.8e+001 -9.2e+000 -8.6e-001 4.6e+004 4.4e+002 1.0e+000
u[18]          -9.5e+000 1.5e-002 3.3e+000 -1.5e+001 -9.5e+000 -4.1e+000 4.6e+004 4.4e+002 1.0e+000
u[19]          -1.3e+001 1.5e-002 3.3e+000 -1.8e+001 -1.3e+001 -7.1e+000 4.6e+004 4.3e+002 1.0e+000
u[20]          -5.3e+000 1.4e-002 2.9e+000 -1.0e+001 -5.3e+000 -5.0e-001 4.5e+004 4.3e+002 1.0e+000
u[21]          -2.8e+000 1.9e-002 3.8e+000 -9.1e+000 -2.8e+000  3.6e+000 4.2e+004 4.0e+002 1.0e+000
u[22]          -1.5e+001 8.3e-003 1.8e+000 -1.7e+001 -1.5e+001 -1.2e+001 4.5e+004 4.3e+002 1.0e+000
u[23]          -7.5e+000 1.3e-002 2.8e+000 -1.2e+001 -7.5e+000 -3.0e+000 4.6e+004 4.4e+002 1.0e+000
u[24]          -2.5e-001 8.3e-003 1.8e+000 -3.1e+000 -2.4e-001  2.6e+000 4.5e+004 4.3e+002 1.0e+000
u[25]          -1.2e+001 1.1e-002 2.4e+000 -1.6e+001 -1.2e+001 -8.5e+000 4.6e+004 4.3e+002 1.0e+000
u[26]          -5.1e+000 1.8e-002 3.8e+000 -1.1e+001 -5.1e+000  1.2e+000 4.6e+004 4.4e+002 1.0e+000
u[27]          -7.5e+000 1.5e-002 3.3e+000 -1.3e+001 -7.5e+000 -2.1e+000 4.6e+004 4.3e+002 1.0e+000
u[28]          -1.6e+000 1.7e-002 3.5e+000 -7.3e+000 -1.6e+000  4.1e+000 4.5e+004 4.2e+002 1.0e+000
u[29]          -1.4e+000 5.6e-003 1.1e+000 -3.2e+000 -1.4e+000  4.3e-001 3.9e+004 3.7e+002 1.0e+000
u[30]          -2.2e+000 1.4e-002 2.9e+000 -6.9e+000 -2.2e+000  2.6e+000 4.3e+004 4.1e+002 1.0e+000
u[31]          -1.3e+001 2.0e-002 4.1e+000 -2.0e+001 -1.3e+001 -6.0e+000 4.5e+004 4.3e+002 1.0e+000
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u[33]          -5.3e+000 1.0e-002 2.3e+000 -9.1e+000 -5.3e+000 -1.6e+000 4.9e+004 4.6e+002 1.0e+000
u[34]          -8.7e+000 7.0e-003 1.5e+000 -1.1e+001 -8.7e+000 -6.1e+000 4.9e+004 4.7e+002 1.0e+000
u[35]          -1.0e+001 9.7e-003 2.1e+000 -1.4e+001 -1.0e+001 -6.8e+000 4.5e+004 4.3e+002 1.0e+000
u[36]          -1.0e+001 1.1e-002 2.3e+000 -1.4e+001 -1.0e+001 -6.4e+000 4.9e+004 4.7e+002 1.0e+000
u[37]          -4.3e+000 1.1e-002 2.3e+000 -8.1e+000 -4.3e+000 -4.7e-001 4.6e+004 4.4e+002 1.0e+000
u[38]          -6.5e+000 1.2e-002 2.5e+000 -1.1e+001 -6.5e+000 -2.4e+000 4.5e+004 4.3e+002 1.0e+000
u[39]          -1.2e+001 2.6e-002 5.1e+000 -2.0e+001 -1.2e+001 -3.6e+000 3.9e+004 3.7e+002 1.0e+000
u[40]          -1.2e+001 9.1e-003 1.9e+000 -1.5e+001 -1.2e+001 -9.1e+000 4.4e+004 4.2e+002 1.0e+000
u[41]          -1.4e+001 2.1e-002 4.5e+000 -2.2e+001 -1.4e+001 -6.8e+000 4.5e+004 4.3e+002 1.0e+000
u[42]          -1.0e+001 1.3e-002 2.8e+000 -1.5e+001 -1.0e+001 -5.7e+000 4.5e+004 4.3e+002 1.0e+000
u[43]          -1.3e+001 1.0e-002 2.1e+000 -1.6e+001 -1.3e+001 -9.0e+000 4.6e+004 4.4e+002 1.0e+000
u[44]          -1.7e+001 2.2e-002 4.2e+000 -2.4e+001 -1.7e+001 -1.1e+001 3.6e+004 3.4e+002 1.0e+000
u[45]          -8.3e+000 1.4e-002 2.8e+000 -1.3e+001 -8.3e+000 -3.7e+000 4.2e+004 4.0e+002 1.0e+000
u[46]          -4.7e+000 1.6e-002 3.4e+000 -1.0e+001 -4.7e+000  9.5e-001 4.4e+004 4.2e+002 1.0e+000
u[47]          -1.6e+001 1.5e-002 3.0e+000 -2.1e+001 -1.6e+001 -1.1e+001 4.2e+004 4.0e+002 1.0e+000
u[48]          -8.7e+000 1.2e-002 2.6e+000 -1.3e+001 -8.7e+000 -4.5e+000 4.3e+004 4.1e+002 1.0e+000
u[49]          -8.6e+000 2.0e-002 4.1e+000 -1.5e+001 -8.7e+000 -1.9e+000 4.3e+004 4.1e+002 1.0e+000
u[50]          -1.1e+001 1.2e-002 2.6e+000 -1.5e+001 -1.1e+001 -6.6e+000 4.7e+004 4.5e+002 1.0e+000
u[51]          -8.1e+000 1.2e-002 2.5e+000 -1.2e+001 -8.1e+000 -3.9e+000 4.5e+004 4.2e+002 1.0e+000
u[52]          -8.5e+000 1.4e-002 2.9e+000 -1.3e+001 -8.5e+000 -3.7e+000 4.4e+004 4.2e+002 1.0e+000
u[53]          -8.5e+000 1.5e-002 3.2e+000 -1.4e+001 -8.5e+000 -3.3e+000 4.5e+004 4.3e+002 1.0e+000
u[54]          -1.4e+001 1.6e-002 3.2e+000 -2.0e+001 -1.4e+001 -9.0e+000 3.9e+004 3.7e+002 1.0e+000
u[55]          -9.2e+000 1.7e-002 3.8e+000 -1.5e+001 -9.2e+000 -3.1e+000 4.9e+004 4.6e+002 1.0e+000
u[56]          -8.3e+000 8.9e-003 2.0e+000 -1.2e+001 -8.3e+000 -5.0e+000 5.0e+004 4.8e+002 1.0e+000
u[57]           2.1e+000 6.8e-003 1.3e+000  4.5e-002  2.1e+000  4.2e+000 3.5e+004 3.3e+002 1.0e+000
u[58]           5.6e-002 8.6e-003 1.8e+000 -2.9e+000  6.0e-002  3.1e+000 4.5e+004 4.3e+002 1.0e+000
u[59]          -4.2e+000 8.5e-003 1.9e+000 -7.2e+000 -4.2e+000 -1.1e+000 4.8e+004 4.6e+002 1.0e+000
u[60]          -5.8e+000 1.9e-002 3.9e+000 -1.2e+001 -5.9e+000  6.2e-001 4.3e+004 4.1e+002 1.0e+000
u[61]          -3.8e+000 2.2e-002 4.7e+000 -1.2e+001 -3.9e+000  4.0e+000 4.4e+004 4.2e+002 1.0e+000
u[62]          -5.0e+000 2.1e-002 4.4e+000 -1.2e+001 -5.0e+000  2.4e+000 4.4e+004 4.1e+002 1.0e+000
u[63]          -4.1e+000 1.3e-002 2.7e+000 -8.6e+000 -4.1e+000  4.1e-001 4.4e+004 4.2e+002 1.0e+000
u[64]          -1.3e+001 2.0e-002 4.3e+000 -2.0e+001 -1.3e+001 -6.1e+000 4.6e+004 4.4e+002 1.0e+000
u[65]          -7.4e+000 1.1e-002 2.4e+000 -1.1e+001 -7.4e+000 -3.5e+000 4.6e+004 4.3e+002 1.0e+000
u[66]          -6.3e+000 1.1e-002 2.2e+000 -9.9e+000 -6.4e+000 -2.7e+000 4.4e+004 4.2e+002 1.0e+000
u[67]          -1.3e+000 1.1e-002 2.4e+000 -5.2e+000 -1.3e+000  2.6e+000 4.2e+004 4.0e+002 1.0e+000
u[68]          -5.0e+000 1.0e-002 2.3e+000 -8.9e+000 -5.0e+000 -1.1e+000 5.1e+004 4.9e+002 1.0e+000
u[69]          -7.2e+000 1.1e-002 2.5e+000 -1.1e+001 -7.2e+000 -3.1e+000 4.9e+004 4.6e+002 1.0e+000
u[70]          -8.1e+000 1.3e-002 2.8e+000 -1.3e+001 -8.1e+000 -3.5e+000 4.5e+004 4.3e+002 1.0e+000
u[71]          -8.5e+000 2.4e-002 5.1e+000 -1.7e+001 -8.5e+000 -3.9e-002 4.4e+004 4.2e+002 1.0e+000
u[72]          -2.0e+000 9.6e-003 2.1e+000 -5.4e+000 -2.0e+000  1.4e+000 4.6e+004 4.4e+002 1.0e+000
u[73]          -4.6e+000 8.0e-003 1.7e+000 -7.5e+000 -4.6e+000 -1.8e+000 4.6e+004 4.4e+002 1.0e+000
u[74]          -5.0e+000 2.3e-002 4.7e+000 -1.3e+001 -5.0e+000  2.6e+000 4.1e+004 3.9e+002 1.0e+000
u[75]          -7.8e+000 1.2e-002 2.4e+000 -1.2e+001 -7.9e+000 -3.9e+000 4.3e+004 4.1e+002 1.0e+000
u[76]          -9.4e+000 9.1e-003 2.0e+000 -1.3e+001 -9.4e+000 -6.2e+000 4.6e+004 4.4e+002 1.0e+000
u[77]          -8.2e+000 9.0e-003 1.9e+000 -1.1e+001 -8.2e+000 -5.1e+000 4.4e+004 4.2e+002 1.0e+000
u[78]          -1.1e+001 2.1e-002 4.4e+000 -1.9e+001 -1.1e+001 -4.1e+000 4.2e+004 4.0e+002 1.0e+000
u[79]          -1.2e+001 1.0e-002 2.3e+000 -1.5e+001 -1.2e+001 -7.8e+000 4.7e+004 4.5e+002 1.0e+000
u[80]          -5.1e+000 1.4e-002 3.1e+000 -1.0e+001 -5.1e+000  5.4e-002 4.9e+004 4.7e+002 1.0e+000
u[81]          -7.9e+000 2.5e-002 5.2e+000 -1.6e+001 -8.0e+000  5.8e-001 4.4e+004 4.2e+002 1.0e+000
u[82]          -5.1e+000 1.2e-002 2.6e+000 -9.4e+000 -5.1e+000 -8.1e-001 4.8e+004 4.5e+002 1.0e+000
u[83]          -5.0e+000 1.2e-002 2.5e+000 -9.0e+000 -5.0e+000 -8.7e-001 4.4e+004 4.2e+002 1.0e+000
u[84]          -9.1e+000 1.7e-002 3.9e+000 -1.5e+001 -9.1e+000 -2.7e+000 5.0e+004 4.8e+002 1.0e+000
u[85]          -1.2e+001 1.6e-002 3.3e+000 -1.7e+001 -1.2e+001 -6.6e+000 4.5e+004 4.2e+002 1.0e+000
u[86]          -1.4e+001 9.6e-003 2.1e+000 -1.7e+001 -1.4e+001 -1.1e+001 4.7e+004 4.5e+002 1.0e+000
u[87]          -3.9e+000 1.3e-002 2.8e+000 -8.4e+000 -3.9e+000  6.4e-001 4.6e+004 4.3e+002 1.0e+000
u[88]          -8.0e+000 1.1e-002 2.3e+000 -1.2e+001 -8.0e+000 -4.2e+000 4.4e+004 4.2e+002 1.0e+000
u[89]          -5.8e+000 7.6e-003 1.6e+000 -8.5e+000 -5.8e+000 -3.2e+000 4.5e+004 4.3e+002 1.0e+000
u[90]          -1.7e+001 1.9e-002 3.8e+000 -2.4e+001 -1.7e+001 -1.1e+001 4.0e+004 3.8e+002 1.0e+000
u[91]          -1.6e+001 1.0e-002 2.1e+000 -1.9e+001 -1.6e+001 -1.2e+001 4.3e+004 4.1e+002 1.0e+000
u[92]          -5.7e+000 1.0e-002 2.3e+000 -9.4e+000 -5.7e+000 -2.0e+000 4.7e+004 4.5e+002 1.0e+000
u[93]          -1.6e+001 1.3e-002 2.7e+000 -2.0e+001 -1.6e+001 -1.1e+001 4.4e+004 4.1e+002 1.0e+000
u[94]          -2.7e+001 1.4e-002 2.5e+000 -3.1e+001 -2.7e+001 -2.2e+001 3.4e+004 3.2e+002 1.0e+000
u[95]          -8.7e-001 1.5e-002 2.9e+000 -5.7e+000 -8.8e-001  4.0e+000 4.0e+004 3.8e+002 1.0e+000
u[96]          -1.8e+001 1.1e-002 2.4e+000 -2.2e+001 -1.8e+001 -1.4e+001 4.4e+004 4.2e+002 1.0e+000
u[97]          -5.2e+000 8.0e-003 1.7e+000 -8.0e+000 -5.2e+000 -2.5e+000 4.3e+004 4.1e+002 1.0e+000
u[98]          -6.0e+000 1.3e-002 2.7e+000 -1.0e+001 -6.0e+000 -1.5e+000 4.2e+004 4.0e+002 1.0e+000
u[99]          -9.9e-001 1.2e-002 2.6e+000 -5.3e+000 -9.9e-001  3.3e+000 4.6e+004 4.4e+002 1.0e+000
u[100]         -1.6e+001 1.9e-002 3.8e+000 -2.2e+001 -1.6e+001 -9.7e+000 3.9e+004 3.8e+002 1.0e+000
u[101]         -7.2e+000 1.4e-002 3.0e+000 -1.2e+001 -7.2e+000 -2.3e+000 4.6e+004 4.4e+002 1.0e+000
u[102]          3.7e+000 1.2e-002 2.5e+000 -4.4e-001  3.6e+000  7.7e+000 4.3e+004 4.1e+002 1.0e+000
u[103]         -9.8e+000 1.5e-002 3.4e+000 -1.5e+001 -9.8e+000 -4.4e+000 5.3e+004 5.0e+002 1.0e+000
u[104]         -8.7e-001 5.8e-003 1.1e+000 -2.7e+000 -8.7e-001  9.1e-001 3.6e+004 3.4e+002 1.0e+000
u[105]         -1.0e+001 1.6e-002 3.4e+000 -1.6e+001 -1.0e+001 -4.5e+000 4.6e+004 4.4e+002 1.0e+000
u[106]         -8.2e+000 1.0e-002 2.2e+000 -1.2e+001 -8.2e+000 -4.6e+000 4.6e+004 4.4e+002 1.0e+000
u[107]         -5.3e+000 1.2e-002 2.5e+000 -9.4e+000 -5.3e+000 -1.1e+000 4.9e+004 4.6e+002 1.0e+000
u[108]         -5.6e+000 1.4e-002 2.8e+000 -1.0e+001 -5.6e+000 -9.4e-001 4.3e+004 4.1e+002 1.0e+000
u[109]         -5.7e+000 9.8e-003 2.2e+000 -9.3e+000 -5.7e+000 -2.1e+000 5.0e+004 4.8e+002 1.0e+000
u[110]         -1.0e+001 1.4e-002 2.9e+000 -1.5e+001 -1.0e+001 -5.3e+000 4.6e+004 4.4e+002 1.0e+000
u[111]         -5.3e+000 1.3e-002 2.8e+000 -9.9e+000 -5.3e+000 -7.1e-001 4.5e+004 4.3e+002 1.0e+000
u[112]         -8.0e+000 1.4e-002 3.0e+000 -1.3e+001 -8.0e+000 -3.1e+000 4.7e+004 4.5e+002 1.0e+000
u[113]         -4.5e+000 8.8e-003 1.9e+000 -7.7e+000 -4.5e+000 -1.3e+000 4.8e+004 4.5e+002 1.0e+000
u[114]         -6.1e+000 2.4e-002 5.2e+000 -1.5e+001 -6.2e+000  2.4e+000 4.5e+004 4.3e+002 1.0e+000
u[115]         -8.0e+000 1.1e-002 2.4e+000 -1.2e+001 -8.0e+000 -4.0e+000 4.6e+004 4.3e+002 1.0e+000
u[116]         -1.1e+001 1.0e-002 2.3e+000 -1.5e+001 -1.1e+001 -7.1e+000 4.7e+004 4.5e+002 1.0e+000
u[117]         -1.1e+001 9.3e-003 2.0e+000 -1.5e+001 -1.1e+001 -7.9e+000 4.7e+004 4.5e+002 1.0e+000
u[118]         -4.4e+000 2.0e-002 4.2e+000 -1.1e+001 -4.4e+000  2.4e+000 4.4e+004 4.1e+002 1.0e+000
u[119]         -5.4e+000 9.2e-003 2.1e+000 -8.8e+000 -5.4e+000 -2.0e+000 5.0e+004 4.8e+002 1.0e+000
u[120]         -1.4e+001 1.0e-002 2.1e+000 -1.8e+001 -1.4e+001 -1.1e+001 4.1e+004 3.9e+002 1.0e+000
u[121]         -1.2e+001 1.1e-002 2.5e+000 -1.6e+001 -1.2e+001 -7.7e+000 4.6e+004 4.4e+002 1.0e+000
u[122]         -2.9e+000 1.1e-002 2.5e+000 -6.9e+000 -2.8e+000  1.2e+000 4.7e+004 4.5e+002 1.0e+000
u[123]         -9.3e+000 1.3e-002 2.8e+000 -1.4e+001 -9.3e+000 -4.8e+000 4.8e+004 4.6e+002 1.0e+000
u[124]         -1.6e+001 1.5e-002 3.2e+000 -2.1e+001 -1.6e+001 -1.0e+001 4.5e+004 4.3e+002 1.0e+000
u[125]         -8.8e+000 2.1e-002 4.4e+000 -1.6e+001 -8.8e+000 -1.5e+000 4.4e+004 4.2e+002 1.0e+000
u[126]         -1.2e+001 1.3e-002 2.7e+000 -1.6e+001 -1.2e+001 -7.7e+000 4.4e+004 4.2e+002 1.0e+000
u[127]         -6.5e+000 1.6e-002 3.4e+000 -1.2e+001 -6.5e+000 -8.4e-001 4.5e+004 4.3e+002 1.0e+000
u[128]         -8.0e+000 1.0e-002 2.2e+000 -1.2e+001 -8.0e+000 -4.4e+000 4.6e+004 4.3e+002 1.0e+000
u[129]         -7.5e+000 1.2e-002 2.7e+000 -1.2e+001 -7.5e+000 -3.1e+000 4.7e+004 4.5e+002 1.0e+000
u[130]         -7.0e+000 8.8e-003 1.9e+000 -1.0e+001 -7.0e+000 -3.9e+000 4.5e+004 4.3e+002 1.0e+000
u[131]         -6.9e+000 1.7e-002 3.6e+000 -1.3e+001 -6.9e+000 -9.9e-001 4.5e+004 4.3e+002 1.0e+000
u[132]         -7.9e+000 2.2e-002 4.7e+000 -1.6e+001 -8.0e+000 -1.4e-001 4.6e+004 4.4e+002 1.0e+000
u[133]         -1.1e+001 2.3e-002 4.8e+000 -1.9e+001 -1.1e+001 -3.4e+000 4.4e+004 4.2e+002 1.0e+000
u[134]         -1.1e+001 1.4e-002 3.2e+000 -1.6e+001 -1.1e+001 -5.7e+000 4.8e+004 4.5e+002 1.0e+000
u[135]         -4.9e+000 1.9e-002 4.0e+000 -1.2e+001 -4.9e+000  1.7e+000 4.4e+004 4.2e+002 1.0e+000
u[136]         -1.5e+001 1.0e-002 2.2e+000 -1.8e+001 -1.4e+001 -1.1e+001 4.3e+004 4.1e+002 1.0e+000
u[137]         -4.8e-001 8.2e-003 1.7e+000 -3.2e+000 -4.7e-001  2.3e+000 4.0e+004 3.8e+002 1.0e+000
u[138]         -9.5e+000 8.6e-003 1.9e+000 -1.3e+001 -9.5e+000 -6.4e+000 4.9e+004 4.6e+002 1.0e+000
u[139]         -1.0e+001 1.0e-002 2.1e+000 -1.4e+001 -1.0e+001 -6.7e+000 4.5e+004 4.3e+002 1.0e+000
u[140]         -7.0e+000 1.2e-002 2.6e+000 -1.1e+001 -7.0e+000 -2.8e+000 4.7e+004 4.5e+002 1.0e+000
u[141]         -1.6e+001 1.6e-002 3.2e+000 -2.1e+001 -1.6e+001 -1.0e+001 4.0e+004 3.8e+002 1.0e+000
u[142]         -1.4e+001 1.4e-002 3.0e+000 -1.9e+001 -1.4e+001 -8.9e+000 4.7e+004 4.4e+002 1.0e+000
u[143]         -7.9e+000 9.4e-003 2.0e+000 -1.1e+001 -7.9e+000 -4.6e+000 4.4e+004 4.2e+002 1.0e+000
u[144]         -8.1e+000 2.4e-002 5.1e+000 -1.7e+001 -8.1e+000  4.4e-001 4.5e+004 4.3e+002 1.0e+000
u[145]         -1.1e+001 1.2e-002 2.6e+000 -1.5e+001 -1.1e+001 -6.6e+000 4.7e+004 4.4e+002 1.0e+000
u[146]         -1.8e+001 8.5e-003 1.7e+000 -2.1e+001 -1.8e+001 -1.6e+001 4.1e+004 3.9e+002 1.0e+000
u[147]         -1.2e+001 1.4e-002 3.2e+000 -1.7e+001 -1.2e+001 -6.7e+000 4.9e+004 4.7e+002 1.0e+000
u[148]         -1.1e+001 1.6e-002 3.3e+000 -1.7e+001 -1.1e+001 -5.7e+000 4.5e+004 4.3e+002 1.0e+000
u[149]         -6.4e+000 1.1e-002 2.2e+000 -1.0e+001 -6.5e+000 -2.8e+000 4.4e+004 4.2e+002 1.0e+000
u[150]         -7.5e+000 1.6e-002 3.4e+000 -1.3e+001 -7.5e+000 -1.8e+000 4.7e+004 4.5e+002 1.0e+000
Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at 
convergence, R_hat=1).

Model converged

Saving Processed Data...

Ensuring dependents are found in the Matlab path
ERA Toolbox files found
CmdStan, MatlabProcessManager, and MatlabStan files found
There is a new version of CmdStan available
MatlabProcessManager version is current
Checking the installed version of MatlabStan is not currently supported

ERP Reliability Analysis Toolbox Version 0.5.2

You are running the most up-to-date version of the toolbox

Dependents will not be updated

Loading Data...
This may take awhile depending on the amount of data...

Preparing data for analysis...

Model is being run in cmdstan

This may take a while depending on the amount of data
Having a problem getting stan version.
This is likely a problem with Java running out of file descriptors
Trying again.
output-2.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-2.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-2.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-2.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-2.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-2.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-2.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-2.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
output-2.csv: Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
output-2.csv: Exception: normal_lpdf: Location parameter[1] is -inf, but must be finite!  (in 'C:/Users/mwkde/Downloads/Temp_StanFiles/cmdstan07-Oct-2021.stan' at line 19)
output-2.csv: If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
output-2.csv: but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
Inference for Stan model: cmdstan07_Oct_2021_model
4 chains: each with iter=(5000,5000,5000,5000); warmup=(0,0,0,0); thin=(1,1,1,1); 20000 iterations saved.
Warmup took (14, 14, 14, 14) seconds, 55 seconds total
Sampling took (33, 32, 33, 33) seconds, 2.2 minutes total
                    Mean     MCSE  StdDev        5%       50%       95%    N_Eff  N_Eff/s    R_hat
lp__           -9.1e+003 1.8e-001      12 -9.2e+003 -9.1e+003 -9.1e+003 4.4e+003 3.4e+001 1.0e+000
accept_stat__   8.6e-001 3.5e-003    0.13  6.0e-001  8.9e-001  1.0e+000 1.4e+003 1.0e+001 1.0e+000
stepsize__      3.4e-001 1.2e-002   0.017  3.1e-001  3.5e-001  3.6e-001 2.0e+000 1.5e-002 3.1e+012
treedepth__     4.0e+000      nan   0.046  4.0e+000  4.0e+000  4.0e+000      nan      nan      nan
n_leapfrog__    1.5e+001      nan    0.52  1.5e+001  1.5e+001  1.5e+001      nan      nan      nan
divergent__     0.0e+000      nan    0.00  0.0e+000  0.0e+000  0.0e+000      nan      nan      nan
energy__        9.2e+003 2.3e-001      15  9.2e+003  9.2e+003  9.2e+003 4.3e+003 3.3e+001 1.0e+000
mu             -8.4e+000 6.1e-003    0.55 -9.3e+000 -8.4e+000 -7.5e+000 8.1e+003 6.2e+001 1.0e+000
sig_u           5.6e+000 5.3e-003    0.45  4.9e+000  5.6e+000  6.3e+000 7.1e+003 5.5e+001 1.0e+000
sig_e           1.2e+001 8.3e-004    0.16  1.2e+001  1.2e+001  1.3e+001 3.9e+004 3.0e+002 1.0e+000
u_raw[1]       -7.3e-002 1.7e-003    0.30 -5.7e-001 -7.0e-002  4.1e-001 3.1e+004 2.4e+002 1.0e+000
u_raw[2]        5.3e-001 2.7e-003    0.55 -3.8e-001  5.3e-001  1.4e+000 4.3e+004 3.3e+002 1.0e+000
u_raw[3]        5.2e-002 4.3e-003    0.90 -1.4e+000  5.4e-002  1.5e+000 4.5e+004 3.4e+002 1.0e+000
u_raw[4]       -1.1e+000 2.3e-003    0.47 -1.8e+000 -1.1e+000 -3.0e-001 3.9e+004 3.0e+002 1.0e+000
u_raw[5]        6.5e-001 1.9e-003    0.35  7.9e-002  6.4e-001  1.2e+000 3.4e+004 2.6e+002 1.0e+000
u_raw[6]        5.2e-001 3.3e-003    0.74 -7.0e-001  5.2e-001  1.7e+000 5.0e+004 3.8e+002 1.0e+000
u_raw[7]        1.2e+000 2.5e-003    0.50  3.4e-001  1.2e+000  2.0e+000 3.9e+004 3.0e+002 1.0e+000
u_raw[8]       -1.4e+000 2.5e-003    0.53 -2.3e+000 -1.4e+000 -5.9e-001 4.4e+004 3.4e+002 1.0e+000
u_raw[9]        3.4e-001 2.3e-003    0.47 -4.2e-001  3.4e-001  1.1e+000 4.0e+004 3.1e+002 1.0e+000
u_raw[10]      -3.6e-002 2.4e-003    0.48 -8.3e-001 -3.5e-002  7.6e-001 4.1e+004 3.1e+002 1.0e+000
u_raw[11]      -1.9e+000 2.8e-003    0.58 -2.9e+000 -1.9e+000 -9.9e-001 4.4e+004 3.3e+002 1.0e+000
u_raw[12]       2.0e-001 2.1e-003    0.41 -4.8e-001  2.0e-001  8.6e-001 4.0e+004 3.1e+002 1.0e+000
u_raw[13]       3.7e-001 3.2e-003    0.70 -7.8e-001  3.7e-001  1.5e+000 5.0e+004 3.8e+002 1.0e+000
u_raw[14]      -1.3e+000 2.1e-003    0.39 -2.0e+000 -1.3e+000 -7.1e-001 3.4e+004 2.6e+002 1.0e+000
u_raw[15]       3.3e-002 3.8e-003    0.85 -1.4e+000  3.4e-002  1.4e+000 5.1e+004 3.9e+002 1.0e+000
u_raw[16]       7.1e-001 2.1e-003    0.42  2.4e-002  7.0e-001  1.4e+000 4.0e+004 3.0e+002 1.0e+000
u_raw[17]      -1.5e-001 4.1e-003    0.92 -1.7e+000 -1.6e-001  1.4e+000 5.1e+004 3.9e+002 1.0e+000
u_raw[18]      -2.0e-001 2.7e-003    0.60 -1.2e+000 -2.0e-001  7.9e-001 4.9e+004 3.8e+002 1.0e+000
u_raw[19]      -7.5e-001 2.8e-003    0.60 -1.7e+000 -7.5e-001  2.4e-001 4.5e+004 3.5e+002 1.0e+000
u_raw[20]       5.6e-001 2.5e-003    0.52 -3.0e-001  5.5e-001  1.4e+000 4.4e+004 3.4e+002 1.0e+000
u_raw[21]       1.0e+000 3.1e-003    0.67 -9.9e-002  1.0e+000  2.1e+000 4.8e+004 3.7e+002 1.0e+000
u_raw[22]      -1.1e+000 2.0e-003    0.34 -1.7e+000 -1.1e+000 -5.6e-001 2.9e+004 2.3e+002 1.0e+000
u_raw[23]       1.5e-001 2.5e-003    0.50 -6.7e-001  1.5e-001  9.8e-001 4.2e+004 3.2e+002 1.0e+000
u_raw[24]       1.5e+000 2.1e-003    0.34  9.2e-001  1.5e+000  2.0e+000 2.6e+004 2.0e+002 1.0e+000
u_raw[25]      -7.4e-001 2.1e-003    0.43 -1.4e+000 -7.3e-001 -4.6e-002 4.0e+004 3.0e+002 1.0e+000
u_raw[26]       5.9e-001 3.1e-003    0.68 -5.3e-001  5.9e-001  1.7e+000 4.8e+004 3.7e+002 1.0e+000
u_raw[27]       1.5e-001 2.7e-003    0.60 -8.3e-001  1.5e-001  1.1e+000 4.8e+004 3.7e+002 1.0e+000
u_raw[28]       1.2e+000 2.9e-003    0.63  1.8e-001  1.2e+000  2.2e+000 4.5e+004 3.5e+002 1.0e+000
u_raw[29]       1.3e+000 1.9e-003    0.24  8.7e-001  1.3e+000  1.7e+000 1.6e+004 1.3e+002 1.0e+000
u_raw[30]       1.1e+000 2.6e-003    0.53  2.5e-001  1.1e+000  2.0e+000 4.1e+004 3.1e+002 1.0e+000
u_raw[31]      -8.0e-001 3.5e-003    0.74 -2.0e+000 -7.9e-001  4.2e-001 4.4e+004 3.4e+002 1.0e+000
u_raw[32]       1.2e-002 3.1e-003    0.66 -1.1e+000  1.8e-002  1.1e+000 4.5e+004 3.4e+002 1.0e+000
u_raw[33]       5.4e-001 2.2e-003    0.41 -1.2e-001  5.4e-001  1.2e+000 3.7e+004 2.8e+002 1.0e+000
u_raw[34]      -5.7e-002 1.7e-003    0.29 -5.4e-001 -5.6e-002  4.3e-001 2.9e+004 2.2e+002 1.0e+000
u_raw[35]      -3.3e-001 2.0e-003    0.38 -9.5e-001 -3.3e-001  2.9e-001 3.7e+004 2.9e+002 1.0e+000
u_raw[36]      -3.3e-001 2.2e-003    0.43 -1.0e+000 -3.3e-001  3.7e-001 3.7e+004 2.9e+002 1.0e+000
u_raw[37]       7.4e-001 2.2e-003    0.43  2.9e-002  7.3e-001  1.5e+000 3.9e+004 3.0e+002 1.0e+000
u_raw[38]       3.4e-001 2.2e-003    0.44 -3.9e-001  3.4e-001  1.1e+000 4.1e+004 3.2e+002 1.0e+000
u_raw[39]      -6.5e-001 4.2e-003    0.93 -2.2e+000 -6.5e-001  8.7e-001 4.9e+004 3.8e+002 1.0e+000
u_raw[40]      -7.1e-001 2.0e-003    0.36 -1.3e+000 -7.0e-001 -1.3e-001 3.1e+004 2.4e+002 1.0e+000
u_raw[41]      -1.0e+000 3.5e-003    0.78 -2.3e+000 -1.0e+000  2.3e-001 4.9e+004 3.8e+002 1.0e+000
u_raw[42]      -3.2e-001 2.4e-003    0.50 -1.1e+000 -3.2e-001  5.0e-001 4.2e+004 3.2e+002 1.0e+000
u_raw[43]      -7.6e-001 2.0e-003    0.39 -1.4e+000 -7.5e-001 -1.2e-001 3.7e+004 2.8e+002 1.0e+000
u_raw[44]      -1.6e+000 3.2e-003    0.74 -2.8e+000 -1.6e+000 -4.3e-001 5.4e+004 4.1e+002 1.0e+000
u_raw[45]       6.2e-003 2.5e-003    0.53 -8.6e-001  4.6e-003  8.8e-001 4.4e+004 3.3e+002 1.0e+000
u_raw[46]       6.5e-001 2.9e-003    0.62 -3.6e-001  6.5e-001  1.7e+000 4.7e+004 3.6e+002 1.0e+000
u_raw[47]      -1.4e+000 2.6e-003    0.54 -2.3e+000 -1.4e+000 -4.9e-001 4.4e+004 3.4e+002 1.0e+000
u_raw[48]      -6.5e-002 2.2e-003    0.47 -8.5e-001 -6.2e-002  7.0e-001 4.8e+004 3.6e+002 1.0e+000
u_raw[49]      -4.9e-002 3.2e-003    0.74 -1.3e+000 -4.2e-002  1.1e+000 5.2e+004 4.0e+002 1.0e+000
u_raw[50]      -4.6e-001 2.3e-003    0.47 -1.2e+000 -4.6e-001  3.2e-001 4.3e+004 3.3e+002 1.0e+000
u_raw[51]       6.2e-002 2.3e-003    0.47 -7.1e-001  6.1e-002  8.3e-001 4.0e+004 3.0e+002 1.0e+000
u_raw[52]      -2.1e-002 2.6e-003    0.54 -9.0e-001 -2.3e-002  8.6e-001 4.2e+004 3.2e+002 1.0e+000
u_raw[53]      -3.1e-002 2.7e-003    0.58 -9.8e-001 -3.1e-002  9.2e-001 4.6e+004 3.5e+002 1.0e+000
u_raw[54]      -1.1e+000 2.7e-003    0.58 -2.0e+000 -1.1e+000 -1.2e-001 4.5e+004 3.5e+002 1.0e+000
u_raw[55]      -1.6e-001 3.1e-003    0.68 -1.3e+000 -1.6e-001  9.5e-001 4.7e+004 3.6e+002 1.0e+000
u_raw[56]       2.4e-003 1.9e-003    0.38 -6.2e-001  6.0e-004  6.2e-001 3.8e+004 2.9e+002 1.0e+000
u_raw[57]       1.9e+000 2.3e-003    0.28  1.4e+000  1.9e+000  2.4e+000 1.5e+004 1.1e+002 1.0e+000
u_raw[58]       1.5e+000 2.2e-003    0.35  9.6e-001  1.5e+000  2.1e+000 2.6e+004 2.0e+002 1.0e+000
u_raw[59]       7.5e-001 2.0e-003    0.35  1.8e-001  7.5e-001  1.3e+000 3.2e+004 2.4e+002 1.0e+000
u_raw[60]       4.6e-001 3.2e-003    0.71 -6.9e-001  4.6e-001  1.6e+000 4.8e+004 3.7e+002 1.0e+000
u_raw[61]       8.1e-001 3.9e-003    0.84 -5.9e-001  8.1e-001  2.2e+000 4.7e+004 3.6e+002 1.0e+000
u_raw[62]       6.0e-001 3.5e-003    0.79 -7.0e-001  6.0e-001  1.9e+000 5.0e+004 3.8e+002 1.0e+000
u_raw[63]       7.7e-001 2.4e-003    0.49 -4.0e-002  7.7e-001  1.6e+000 4.2e+004 3.2e+002 1.0e+000
u_raw[64]      -8.6e-001 3.5e-003    0.74 -2.1e+000 -8.7e-001  3.5e-001 4.6e+004 3.5e+002 1.0e+000
u_raw[65]       1.8e-001 2.2e-003    0.44 -5.4e-001  1.8e-001  9.0e-001 4.0e+004 3.0e+002 1.0e+000
u_raw[66]       3.7e-001 2.1e-003    0.40 -3.0e-001  3.7e-001  1.0e+000 3.9e+004 3.0e+002 1.0e+000
u_raw[67]       1.3e+000 2.4e-003    0.44  5.6e-001  1.3e+000  2.0e+000 3.5e+004 2.6e+002 1.0e+000
u_raw[68]       6.0e-001 2.2e-003    0.43 -1.1e-001  6.0e-001  1.3e+000 3.9e+004 3.0e+002 1.0e+000
u_raw[69]       2.1e-001 2.3e-003    0.45 -5.2e-001  2.1e-001  9.6e-001 3.8e+004 2.9e+002 1.0e+000
u_raw[70]       4.5e-002 2.5e-003    0.52 -8.1e-001  4.6e-002  9.0e-001 4.4e+004 3.3e+002 1.0e+000
u_raw[71]      -2.2e-002 4.2e-003    0.91 -1.5e+000 -1.5e-002  1.5e+000 4.6e+004 3.5e+002 1.0e+000
u_raw[72]       1.1e+000 2.2e-003    0.38  5.2e-001  1.1e+000  1.8e+000 3.2e+004 2.4e+002 1.0e+000
u_raw[73]       6.7e-001 1.8e-003    0.33  1.4e-001  6.7e-001  1.2e+000 3.2e+004 2.4e+002 1.0e+000
u_raw[74]       6.1e-001 3.8e-003    0.84 -7.7e-001  6.0e-001  2.0e+000 4.9e+004 3.8e+002 1.0e+000
u_raw[75]       9.5e-002 2.2e-003    0.44 -6.4e-001  9.8e-002  8.2e-001 3.9e+004 3.0e+002 1.0e+000
u_raw[76]      -1.9e-001 2.0e-003    0.36 -7.9e-001 -2.0e-001  4.0e-001 3.3e+004 2.5e+002 1.0e+000
u_raw[77]       2.4e-002 1.9e-003    0.36 -5.7e-001  2.5e-002  6.2e-001 3.5e+004 2.7e+002 1.0e+000
u_raw[78]      -5.3e-001 3.5e-003    0.78 -1.8e+000 -5.3e-001  7.4e-001 5.0e+004 3.8e+002 1.0e+000
u_raw[79]      -5.8e-001 2.1e-003    0.41 -1.3e+000 -5.7e-001  1.0e-001 3.8e+004 2.9e+002 1.0e+000
u_raw[80]       5.8e-001 2.7e-003    0.56 -3.4e-001  5.9e-001  1.5e+000 4.3e+004 3.3e+002 1.0e+000
u_raw[81]       7.8e-002 4.2e-003    0.92 -1.4e+000  8.1e-002  1.6e+000 4.7e+004 3.6e+002 1.0e+000
u_raw[82]       5.9e-001 2.4e-003    0.48 -2.0e-001  5.9e-001  1.4e+000 4.0e+004 3.1e+002 1.0e+000
u_raw[83]       6.2e-001 2.3e-003    0.46 -1.3e-001  6.1e-001  1.4e+000 4.0e+004 3.1e+002 1.0e+000
u_raw[84]      -1.3e-001 3.3e-003    0.71 -1.3e+000 -1.3e-001  1.0e+000 4.7e+004 3.6e+002 1.0e+000
u_raw[85]      -6.5e-001 2.8e-003    0.60 -1.6e+000 -6.5e-001  3.5e-001 4.8e+004 3.6e+002 1.0e+000
u_raw[86]      -1.0e+000 2.1e-003    0.38 -1.7e+000 -1.0e+000 -3.9e-001 3.5e+004 2.7e+002 1.0e+000
u_raw[87]       8.1e-001 2.4e-003    0.50 -1.1e-002  8.0e-001  1.6e+000 4.3e+004 3.3e+002 1.0e+000
u_raw[88]       6.8e-002 2.2e-003    0.41 -6.1e-001  6.8e-002  7.5e-001 3.4e+004 2.6e+002 1.0e+000
u_raw[89]       4.6e-001 1.8e-003    0.30 -4.2e-002  4.6e-001  9.6e-001 3.0e+004 2.3e+002 1.0e+000
u_raw[90]      -1.6e+000 3.2e-003    0.67 -2.7e+000 -1.6e+000 -5.1e-001 4.5e+004 3.4e+002 1.0e+000
u_raw[91]      -1.4e+000 2.2e-003    0.39 -2.0e+000 -1.4e+000 -7.2e-001 3.1e+004 2.4e+002 1.0e+000
u_raw[92]       4.7e-001 2.2e-003    0.42 -2.1e-001  4.7e-001  1.2e+000 3.6e+004 2.8e+002 1.0e+000
u_raw[93]      -1.3e+000 2.5e-003    0.50 -2.2e+000 -1.3e+000 -5.3e-001 3.8e+004 2.9e+002 1.0e+000
u_raw[94]      -3.3e+000 2.9e-003    0.48 -4.1e+000 -3.3e+000 -2.5e+000 2.7e+004 2.1e+002 1.0e+000
u_raw[95]       1.4e+000 2.5e-003    0.53  5.0e-001  1.4e+000  2.2e+000 4.4e+004 3.4e+002 1.0e+000
u_raw[96]      -1.8e+000 2.3e-003    0.43 -2.5e+000 -1.8e+000 -1.1e+000 3.7e+004 2.8e+002 1.0e+000
u_raw[97]       5.6e-001 1.8e-003    0.32  4.9e-002  5.6e-001  1.1e+000 3.1e+004 2.3e+002 1.0e+000
u_raw[98]       4.3e-001 2.5e-003    0.50 -3.9e-001  4.3e-001  1.3e+000 4.1e+004 3.1e+002 1.0e+000
u_raw[99]       1.3e+000 2.5e-003    0.48  5.6e-001  1.3e+000  2.1e+000 3.7e+004 2.8e+002 1.0e+000
u_raw[100]     -1.3e+000 3.1e-003    0.67 -2.4e+000 -1.3e+000 -2.1e-001 4.6e+004 3.5e+002 1.0e+000
u_raw[101]      2.1e-001 2.6e-003    0.55 -6.9e-001  2.1e-001  1.1e+000 4.4e+004 3.4e+002 1.0e+000
u_raw[102]      2.2e+000 2.6e-003    0.46  1.4e+000  2.2e+000  2.9e+000 3.2e+004 2.4e+002 1.0e+000
u_raw[103]     -2.7e-001 2.7e-003    0.60 -1.3e+000 -2.7e-001  7.1e-001 4.8e+004 3.7e+002 1.0e+000
u_raw[104]      1.4e+000 1.9e-003    0.24  9.6e-001  1.3e+000  1.8e+000 1.6e+004 1.2e+002 1.0e+000
u_raw[105]     -3.2e-001 2.9e-003    0.62 -1.4e+000 -3.2e-001  7.1e-001 4.6e+004 3.5e+002 1.0e+000
u_raw[106]      3.2e-002 2.1e-003    0.39 -6.2e-001  3.3e-002  6.8e-001 3.6e+004 2.8e+002 1.0e+000
u_raw[107]      5.6e-001 2.3e-003    0.46 -2.0e-001  5.6e-001  1.3e+000 3.9e+004 3.0e+002 1.0e+000
u_raw[108]      4.9e-001 2.5e-003    0.51 -3.6e-001  4.9e-001  1.3e+000 4.1e+004 3.2e+002 1.0e+000
u_raw[109]      4.8e-001 2.1e-003    0.41 -1.9e-001  4.8e-001  1.2e+000 4.0e+004 3.0e+002 1.0e+000
u_raw[110]     -3.1e-001 2.5e-003    0.53 -1.2e+000 -3.1e-001  5.7e-001 4.4e+004 3.4e+002 1.0e+000
u_raw[111]      5.5e-001 2.5e-003    0.50 -2.8e-001  5.5e-001  1.4e+000 4.0e+004 3.0e+002 1.0e+000
u_raw[112]      7.0e-002 2.5e-003    0.54 -8.2e-001  7.1e-002  9.6e-001 4.6e+004 3.5e+002 1.0e+000
u_raw[113]      6.9e-001 2.0e-003    0.36  1.1e-001  6.9e-001  1.3e+000 3.3e+004 2.5e+002 1.0e+000
u_raw[114]      3.9e-001 4.2e-003    0.92 -1.1e+000  3.9e-001  1.9e+000 4.7e+004 3.6e+002 1.0e+000
u_raw[115]      7.4e-002 2.2e-003    0.44 -6.5e-001  7.4e-002  8.0e-001 4.2e+004 3.2e+002 1.0e+000
u_raw[116]     -4.5e-001 2.1e-003    0.42 -1.1e+000 -4.5e-001  2.3e-001 3.9e+004 3.0e+002 1.0e+000
u_raw[117]     -5.1e-001 2.0e-003    0.38 -1.1e+000 -5.1e-001  1.1e-001 3.5e+004 2.6e+002 1.0e+000
u_raw[118]      7.1e-001 3.3e-003    0.75 -5.2e-001  7.1e-001  1.9e+000 5.1e+004 3.9e+002 1.0e+000
u_raw[119]      5.4e-001 2.0e-003    0.37 -7.1e-002  5.3e-001  1.2e+000 3.7e+004 2.8e+002 1.0e+000
u_raw[120]     -1.1e+000 2.1e-003    0.40 -1.7e+000 -1.1e+000 -4.3e-001 3.6e+004 2.8e+002 1.0e+000
u_raw[121]     -6.1e-001 2.3e-003    0.45 -1.4e+000 -6.1e-001  1.3e-001 3.9e+004 3.0e+002 1.0e+000
u_raw[122]      1.0e+000 2.3e-003    0.45  2.5e-001  1.0e+000  1.7e+000 3.8e+004 2.9e+002 1.0e+000
u_raw[123]     -1.7e-001 2.4e-003    0.51 -1.0e+000 -1.7e-001  6.5e-001 4.4e+004 3.4e+002 1.0e+000
u_raw[124]     -1.3e+000 2.7e-003    0.58 -2.3e+000 -1.3e+000 -3.7e-001 4.6e+004 3.5e+002 1.0e+000
u_raw[125]     -6.3e-002 3.5e-003    0.78 -1.4e+000 -6.5e-002  1.2e+000 4.9e+004 3.8e+002 1.0e+000
u_raw[126]     -6.8e-001 2.5e-003    0.48 -1.5e+000 -6.8e-001  1.1e-001 3.8e+004 2.9e+002 1.0e+000
u_raw[127]      3.4e-001 2.9e-003    0.63 -6.9e-001  3.4e-001  1.4e+000 4.7e+004 3.6e+002 1.0e+000
u_raw[128]      7.0e-002 2.0e-003    0.40 -5.9e-001  6.6e-002  7.3e-001 3.9e+004 3.0e+002 1.0e+000
u_raw[129]      1.6e-001 2.4e-003    0.49 -6.4e-001  1.6e-001  9.5e-001 4.1e+004 3.1e+002 1.0e+000
u_raw[130]      2.4e-001 1.8e-003    0.34 -3.2e-001  2.4e-001  8.1e-001 3.4e+004 2.6e+002 1.0e+000
u_raw[131]      2.7e-001 2.9e-003    0.64 -7.8e-001  2.7e-001  1.3e+000 4.9e+004 3.7e+002 1.0e+000
u_raw[132]      7.9e-002 3.8e-003    0.84 -1.3e+000  8.1e-002  1.5e+000 4.8e+004 3.7e+002 1.0e+000
u_raw[133]     -5.1e-001 3.9e-003    0.84 -1.9e+000 -5.1e-001  8.8e-001 4.6e+004 3.5e+002 1.0e+000
u_raw[134]     -4.5e-001 2.7e-003    0.58 -1.4e+000 -4.5e-001  4.9e-001 4.5e+004 3.4e+002 1.0e+000
u_raw[135]      6.2e-001 3.3e-003    0.71 -5.5e-001  6.2e-001  1.8e+000 4.7e+004 3.6e+002 1.0e+000
u_raw[136]     -1.1e+000 2.2e-003    0.41 -1.8e+000 -1.1e+000 -4.4e-001 3.4e+004 2.6e+002 1.0e+000
u_raw[137]      1.4e+000 2.1e-003    0.32  8.9e-001  1.4e+000  2.0e+000 2.3e+004 1.8e+002 1.0e+000
u_raw[138]     -2.1e-001 1.9e-003    0.36 -8.0e-001 -2.1e-001  3.8e-001 3.5e+004 2.6e+002 1.0e+000
u_raw[139]     -3.3e-001 2.1e-003    0.39 -9.8e-001 -3.3e-001  3.1e-001 3.5e+004 2.7e+002 1.0e+000
u_raw[140]      2.6e-001 2.2e-003    0.46 -5.0e-001  2.5e-001  1.0e+000 4.3e+004 3.3e+002 1.0e+000
u_raw[141]     -1.3e+000 2.7e-003    0.58 -2.3e+000 -1.3e+000 -3.8e-001 4.5e+004 3.4e+002 1.0e+000
u_raw[142]     -9.9e-001 2.6e-003    0.55 -1.9e+000 -9.9e-001 -8.1e-002 4.5e+004 3.5e+002 1.0e+000
u_raw[143]      8.4e-002 1.9e-003    0.37 -5.2e-001  8.6e-002  6.9e-001 3.6e+004 2.7e+002 1.0e+000
u_raw[144]      5.8e-002 4.0e-003    0.91 -1.4e+000  5.6e-002  1.6e+000 5.1e+004 3.9e+002 1.0e+000
u_raw[145]     -4.5e-001 2.3e-003    0.46 -1.2e+000 -4.5e-001  3.1e-001 4.0e+004 3.1e+002 1.0e+000
u_raw[146]     -1.8e+000 2.3e-003    0.34 -2.4e+000 -1.8e+000 -1.3e+000 2.3e+004 1.8e+002 1.0e+000
u_raw[147]     -6.5e-001 2.7e-003    0.58 -1.6e+000 -6.4e-001  3.0e-001 4.5e+004 3.5e+002 1.0e+000
u_raw[148]     -5.0e-001 2.8e-003    0.59 -1.5e+000 -5.0e-001  4.8e-001 4.4e+004 3.4e+002 1.0e+000
u_raw[149]      3.4e-001 2.0e-003    0.41 -3.3e-001  3.5e-001  1.0e+000 4.1e+004 3.1e+002 1.0e+000
u_raw[150]      1.6e-001 2.8e-003    0.62 -8.5e-001  1.6e-001  1.2e+000 4.9e+004 3.7e+002 1.0e+000
u[1]           -8.8e+000 7.3e-003     1.6 -1.1e+001 -8.8e+000 -6.2e+000 4.6e+004 3.5e+002 1.0e+000
u[2]           -5.4e+000 1.4e-002     3.0 -1.0e+001 -5.4e+000 -3.9e-001 4.7e+004 3.6e+002 1.0e+000
u[3]           -8.1e+000 2.4e-002     5.1 -1.6e+001 -8.0e+000  2.3e-001 4.3e+004 3.3e+002 1.0e+000
u[4]           -1.4e+001 1.2e-002     2.5 -1.8e+001 -1.4e+001 -1.0e+001 4.5e+004 3.4e+002 1.0e+000
u[5]           -4.8e+000 8.5e-003     1.9 -7.8e+000 -4.8e+000 -1.7e+000 4.8e+004 3.7e+002 1.0e+000
u[6]           -5.4e+000 1.9e-002     4.1 -1.2e+001 -5.4e+000  1.4e+000 4.7e+004 3.6e+002 1.0e+000
u[7]           -1.9e+000 1.3e-002     2.8 -6.5e+000 -1.9e+000  2.6e+000 4.5e+004 3.4e+002 1.0e+000
u[8]           -1.6e+001 1.4e-002     2.9 -2.1e+001 -1.6e+001 -1.2e+001 4.4e+004 3.3e+002 1.0e+000
u[9]           -6.5e+000 1.2e-002     2.6 -1.1e+001 -6.5e+000 -2.3e+000 4.8e+004 3.6e+002 1.0e+000
u[10]          -8.6e+000 1.2e-002     2.6 -1.3e+001 -8.6e+000 -4.2e+000 4.7e+004 3.6e+002 1.0e+000
u[11]          -1.9e+001 1.7e-002     3.2 -2.4e+001 -1.9e+001 -1.4e+001 3.8e+004 2.9e+002 1.0e+000
u[12]          -7.3e+000 9.7e-003     2.2 -1.1e+001 -7.3e+000 -3.6e+000 5.3e+004 4.0e+002 1.0e+000
u[13]          -6.3e+000 1.8e-002     3.9 -1.3e+001 -6.3e+000  1.1e-001 4.7e+004 3.6e+002 1.0e+000
u[14]          -1.6e+001 9.9e-003     2.1 -1.9e+001 -1.6e+001 -1.2e+001 4.4e+004 3.4e+002 1.0e+000
u[15]          -8.2e+000 2.2e-002     4.8 -1.6e+001 -8.2e+000 -3.5e-001 4.8e+004 3.7e+002 1.0e+000
u[16]          -4.4e+000 9.7e-003     2.3 -8.2e+000 -4.4e+000 -6.9e-001 5.4e+004 4.2e+002 1.0e+000
u[17]          -9.2e+000 2.4e-002     5.2 -1.8e+001 -9.2e+000 -6.9e-001 4.7e+004 3.6e+002 1.0e+000
u[18]          -9.5e+000 1.4e-002     3.3 -1.5e+001 -9.5e+000 -4.0e+000 5.3e+004 4.0e+002 1.0e+000
u[19]          -1.3e+001 1.5e-002     3.3 -1.8e+001 -1.3e+001 -7.1e+000 4.7e+004 3.6e+002 1.0e+000
u[20]          -5.3e+000 1.3e-002     2.8 -1.0e+001 -5.3e+000 -5.6e-001 4.9e+004 3.7e+002 1.0e+000
u[21]          -2.7e+000 1.7e-002     3.7 -8.9e+000 -2.8e+000  3.5e+000 4.6e+004 3.5e+002 1.0e+000
u[22]          -1.5e+001 8.4e-003     1.8 -1.7e+001 -1.5e+001 -1.2e+001 4.5e+004 3.5e+002 1.0e+000
u[23]          -7.5e+000 1.3e-002     2.7 -1.2e+001 -7.5e+000 -3.0e+000 4.7e+004 3.6e+002 1.0e+000
u[24]          -2.5e-001 8.4e-003     1.7 -3.1e+000 -2.5e-001  2.6e+000 4.3e+004 3.3e+002 1.0e+000
u[25]          -1.2e+001 1.1e-002     2.3 -1.6e+001 -1.2e+001 -8.7e+000 4.7e+004 3.6e+002 1.0e+000
u[26]          -5.1e+000 1.7e-002     3.8 -1.1e+001 -5.1e+000  1.1e+000 4.6e+004 3.5e+002 1.0e+000
u[27]          -7.5e+000 1.5e-002     3.3 -1.3e+001 -7.5e+000 -2.1e+000 5.0e+004 3.8e+002 1.0e+000
u[28]          -1.6e+000 1.7e-002     3.5 -7.3e+000 -1.6e+000  4.1e+000 4.4e+004 3.4e+002 1.0e+000
u[29]          -1.4e+000 5.8e-003     1.1 -3.2e+000 -1.4e+000  4.4e-001 3.7e+004 2.8e+002 1.0e+000
u[30]          -2.2e+000 1.4e-002     2.9 -7.0e+000 -2.2e+000  2.6e+000 4.6e+004 3.5e+002 1.0e+000
u[31]          -1.3e+001 2.0e-002     4.1 -2.0e+001 -1.3e+001 -6.0e+000 4.3e+004 3.3e+002 1.0e+000
u[32]          -8.3e+000 1.7e-002     3.7 -1.4e+001 -8.3e+000 -2.2e+000 4.7e+004 3.6e+002 1.0e+000
u[33]          -5.3e+000 1.0e-002     2.2 -9.0e+000 -5.4e+000 -1.7e+000 4.7e+004 3.6e+002 1.0e+000
u[34]          -8.7e+000 7.1e-003     1.5 -1.1e+001 -8.7e+000 -6.1e+000 4.7e+004 3.6e+002 1.0e+000
u[35]          -1.0e+001 9.2e-003     2.0 -1.4e+001 -1.0e+001 -6.8e+000 4.9e+004 3.7e+002 1.0e+000
u[36]          -1.0e+001 1.1e-002     2.3 -1.4e+001 -1.0e+001 -6.4e+000 4.4e+004 3.4e+002 1.0e+000
u[37]          -4.3e+000 1.1e-002     2.3 -8.1e+000 -4.3e+000 -4.1e-001 4.8e+004 3.7e+002 1.0e+000
u[38]          -6.5e+000 1.1e-002     2.4 -1.0e+001 -6.5e+000 -2.5e+000 5.0e+004 3.8e+002 1.0e+000
u[39]          -1.2e+001 2.4e-002     5.2 -2.1e+001 -1.2e+001 -3.4e+000 4.5e+004 3.5e+002 1.0e+000
u[40]          -1.2e+001 8.8e-003     1.9 -1.5e+001 -1.2e+001 -9.2e+000 4.6e+004 3.5e+002 1.0e+000
u[41]          -1.4e+001 2.1e-002     4.4 -2.1e+001 -1.4e+001 -7.0e+000 4.4e+004 3.4e+002 1.0e+000
u[42]          -1.0e+001 1.3e-002     2.8 -1.5e+001 -1.0e+001 -5.6e+000 4.8e+004 3.7e+002 1.0e+000
u[43]          -1.3e+001 9.4e-003     2.1 -1.6e+001 -1.3e+001 -9.1e+000 5.0e+004 3.8e+002 1.0e+000
u[44]          -1.8e+001 2.0e-002     4.2 -2.4e+001 -1.7e+001 -1.1e+001 4.3e+004 3.3e+002 1.0e+000
u[45]          -8.3e+000 1.3e-002     2.9 -1.3e+001 -8.3e+000 -3.5e+000 4.8e+004 3.7e+002 1.0e+000
u[46]          -4.7e+000 1.6e-002     3.4 -1.0e+001 -4.7e+000  9.0e-001 4.6e+004 3.5e+002 1.0e+000
u[47]          -1.6e+001 1.4e-002     3.0 -2.1e+001 -1.6e+001 -1.1e+001 4.5e+004 3.4e+002 1.0e+000
u[48]          -8.7e+000 1.1e-002     2.6 -1.3e+001 -8.7e+000 -4.5e+000 5.2e+004 4.0e+002 1.0e+000
u[49]          -8.6e+000 1.8e-002     4.1 -1.5e+001 -8.6e+000 -1.9e+000 5.1e+004 3.9e+002 1.0e+000
u[50]          -1.1e+001 1.2e-002     2.6 -1.5e+001 -1.1e+001 -6.7e+000 4.7e+004 3.6e+002 1.0e+000
u[51]          -8.0e+000 1.2e-002     2.5 -1.2e+001 -8.0e+000 -3.8e+000 4.6e+004 3.5e+002 1.0e+000
u[52]          -8.5e+000 1.4e-002     2.9 -1.3e+001 -8.5e+000 -3.6e+000 4.6e+004 3.5e+002 1.0e+000
u[53]          -8.5e+000 1.5e-002     3.2 -1.4e+001 -8.5e+000 -3.3e+000 4.7e+004 3.6e+002 1.0e+000
u[54]          -1.4e+001 1.5e-002     3.2 -2.0e+001 -1.4e+001 -9.0e+000 4.6e+004 3.5e+002 1.0e+000
u[55]          -9.3e+000 1.7e-002     3.7 -1.5e+001 -9.3e+000 -3.1e+000 4.8e+004 3.7e+002 1.0e+000
u[56]          -8.3e+000 9.2e-003     2.0 -1.2e+001 -8.4e+000 -5.0e+000 4.9e+004 3.7e+002 1.0e+000
u[57]           2.1e+000 7.0e-003     1.3  4.1e-002  2.1e+000  4.2e+000 3.3e+004 2.5e+002 1.0e+000
u[58]           7.4e-002 8.3e-003     1.8 -2.9e+000  5.6e-002  3.1e+000 4.7e+004 3.6e+002 1.0e+000
u[59]          -4.2e+000 8.6e-003     1.9 -7.2e+000 -4.2e+000 -1.1e+000 4.7e+004 3.6e+002 1.0e+000
u[60]          -5.8e+000 1.8e-002     3.9 -1.2e+001 -5.8e+000  6.8e-001 4.6e+004 3.5e+002 1.0e+000
u[61]          -3.8e+000 2.2e-002     4.7 -1.2e+001 -3.9e+000  4.1e+000 4.5e+004 3.5e+002 1.0e+000
u[62]          -5.0e+000 2.1e-002     4.4 -1.2e+001 -5.0e+000  2.3e+000 4.6e+004 3.5e+002 1.0e+000
u[63]          -4.1e+000 1.2e-002     2.7 -8.5e+000 -4.1e+000  4.1e-001 4.9e+004 3.7e+002 1.0e+000
u[64]          -1.3e+001 2.0e-002     4.2 -2.0e+001 -1.3e+001 -6.4e+000 4.4e+004 3.4e+002 1.0e+000
u[65]          -7.4e+000 1.1e-002     2.4 -1.1e+001 -7.4e+000 -3.5e+000 4.7e+004 3.6e+002 1.0e+000
u[66]          -6.3e+000 1.0e-002     2.2 -1.0e+001 -6.3e+000 -2.7e+000 4.8e+004 3.6e+002 1.0e+000
u[67]          -1.3e+000 1.1e-002     2.4 -5.2e+000 -1.3e+000  2.7e+000 4.4e+004 3.4e+002 1.0e+000
u[68]          -5.0e+000 1.1e-002     2.4 -8.9e+000 -5.0e+000 -1.1e+000 4.6e+004 3.5e+002 1.0e+000
u[69]          -7.2e+000 1.2e-002     2.5 -1.1e+001 -7.2e+000 -3.1e+000 4.6e+004 3.5e+002 1.0e+000
u[70]          -8.1e+000 1.3e-002     2.9 -1.3e+001 -8.1e+000 -3.4e+000 5.0e+004 3.8e+002 1.0e+000
u[71]          -8.5e+000 2.4e-002     5.1 -1.7e+001 -8.4e+000 -6.9e-002 4.3e+004 3.3e+002 1.0e+000
u[72]          -2.0e+000 9.5e-003     2.0 -5.3e+000 -2.0e+000  1.3e+000 4.7e+004 3.6e+002 1.0e+000
u[73]          -4.6e+000 7.8e-003     1.7 -7.4e+000 -4.6e+000 -1.8e+000 4.9e+004 3.7e+002 1.0e+000
u[74]          -5.0e+000 2.2e-002     4.7 -1.3e+001 -5.0e+000  2.8e+000 4.7e+004 3.6e+002 1.0e+000
u[75]          -7.8e+000 1.1e-002     2.4 -1.2e+001 -7.8e+000 -3.9e+000 4.8e+004 3.7e+002 1.0e+000
u[76]          -9.4e+000 9.1e-003     1.9 -1.3e+001 -9.4e+000 -6.2e+000 4.6e+004 3.5e+002 1.0e+000
u[77]          -8.2e+000 9.2e-003     1.9 -1.1e+001 -8.2e+000 -5.0e+000 4.5e+004 3.4e+002 1.0e+000
u[78]          -1.1e+001 2.0e-002     4.3 -1.8e+001 -1.1e+001 -4.3e+000 4.7e+004 3.6e+002 1.0e+000
u[79]          -1.2e+001 1.0e-002     2.2 -1.5e+001 -1.2e+001 -7.9e+000 4.7e+004 3.6e+002 1.0e+000
u[80]          -5.1e+000 1.4e-002     3.1 -1.0e+001 -5.1e+000 -4.4e-002 4.7e+004 3.6e+002 1.0e+000
u[81]          -7.9e+000 2.5e-002     5.1 -1.6e+001 -7.9e+000  6.1e-001 4.3e+004 3.3e+002 1.0e+000
u[82]          -5.1e+000 1.2e-002     2.6 -9.4e+000 -5.1e+000 -7.5e-001 4.8e+004 3.7e+002 1.0e+000
u[83]          -4.9e+000 1.1e-002     2.5 -9.0e+000 -4.9e+000 -8.4e-001 4.9e+004 3.7e+002 1.0e+000
u[84]          -9.1e+000 1.8e-002     3.9 -1.6e+001 -9.1e+000 -2.6e+000 4.6e+004 3.5e+002 1.0e+000
u[85]          -1.2e+001 1.5e-002     3.3 -1.7e+001 -1.2e+001 -6.5e+000 4.8e+004 3.7e+002 1.0e+000
u[86]          -1.4e+001 9.4e-003     2.0 -1.7e+001 -1.4e+001 -1.1e+001 4.8e+004 3.6e+002 1.0e+000
u[87]          -3.9e+000 1.3e-002     2.8 -8.4e+000 -3.9e+000  6.6e-001 4.8e+004 3.7e+002 1.0e+000
u[88]          -8.0e+000 1.0e-002     2.2 -1.2e+001 -8.0e+000 -4.3e+000 4.6e+004 3.5e+002 1.0e+000
u[89]          -5.8e+000 7.3e-003     1.6 -8.5e+000 -5.8e+000 -3.2e+000 4.8e+004 3.7e+002 1.0e+000
u[90]          -1.7e+001 1.9e-002     3.8 -2.4e+001 -1.7e+001 -1.1e+001 3.9e+004 3.0e+002 1.0e+000
u[91]          -1.6e+001 1.0e-002     2.1 -1.9e+001 -1.6e+001 -1.2e+001 4.2e+004 3.2e+002 1.0e+000
u[92]          -5.7e+000 1.1e-002     2.3 -9.5e+000 -5.7e+000 -2.0e+000 4.6e+004 3.5e+002 1.0e+000
u[93]          -1.6e+001 1.3e-002     2.7 -2.0e+001 -1.6e+001 -1.1e+001 4.1e+004 3.2e+002 1.0e+000
u[94]          -2.7e+001 1.4e-002     2.6 -3.1e+001 -2.7e+001 -2.3e+001 3.2e+004 2.5e+002 1.0e+000
u[95]          -8.0e-001 1.4e-002     2.9 -5.6e+000 -8.2e-001  4.0e+000 4.6e+004 3.5e+002 1.0e+000
u[96]          -1.8e+001 1.1e-002     2.3 -2.2e+001 -1.8e+001 -1.4e+001 4.6e+004 3.5e+002 1.0e+000
u[97]          -5.2e+000 7.6e-003     1.7 -8.0e+000 -5.2e+000 -2.5e+000 4.8e+004 3.7e+002 1.0e+000
u[98]          -6.0e+000 1.2e-002     2.7 -1.0e+001 -6.0e+000 -1.5e+000 4.8e+004 3.7e+002 1.0e+000
u[99]          -9.6e-001 1.2e-002     2.6 -5.2e+000 -9.8e-001  3.4e+000 4.6e+004 3.6e+002 1.0e+000
u[100]         -1.6e+001 1.8e-002     3.8 -2.2e+001 -1.6e+001 -9.5e+000 4.3e+004 3.3e+002 1.0e+000
u[101]         -7.2e+000 1.4e-002     3.0 -1.2e+001 -7.2e+000 -2.3e+000 4.9e+004 3.7e+002 1.0e+000
u[102]          3.7e+000 1.2e-002     2.5 -3.1e-001  3.7e+000  7.8e+000 3.9e+004 3.0e+002 1.0e+000
u[103]         -9.8e+000 1.5e-002     3.3 -1.5e+001 -9.9e+000 -4.4e+000 5.1e+004 3.9e+002 1.0e+000
u[104]         -8.7e-001 5.9e-003     1.1 -2.7e+000 -8.8e-001  9.4e-001 3.5e+004 2.7e+002 1.0e+000
u[105]         -1.0e+001 1.6e-002     3.5 -1.6e+001 -1.0e+001 -4.4e+000 4.8e+004 3.7e+002 1.0e+000
u[106]         -8.2e+000 1.0e-002     2.1 -1.2e+001 -8.2e+000 -4.7e+000 4.5e+004 3.4e+002 1.0e+000
u[107]         -5.3e+000 1.2e-002     2.5 -9.4e+000 -5.2e+000 -1.1e+000 4.8e+004 3.6e+002 1.0e+000
u[108]         -5.6e+000 1.3e-002     2.8 -1.0e+001 -5.6e+000 -9.8e-001 4.6e+004 3.5e+002 1.0e+000
u[109]         -5.7e+000 1.0e-002     2.2 -9.3e+000 -5.7e+000 -2.1e+000 4.9e+004 3.8e+002 1.0e+000
u[110]         -1.0e+001 1.4e-002     2.9 -1.5e+001 -1.0e+001 -5.2e+000 4.6e+004 3.5e+002 1.0e+000
u[111]         -5.3e+000 1.3e-002     2.8 -9.8e+000 -5.3e+000 -7.8e-001 4.2e+004 3.2e+002 1.0e+000
u[112]         -8.0e+000 1.4e-002     3.0 -1.3e+001 -8.0e+000 -3.1e+000 4.9e+004 3.7e+002 1.0e+000
u[113]         -4.5e+000 8.8e-003     1.9 -7.7e+000 -4.5e+000 -1.4e+000 4.6e+004 3.5e+002 1.0e+000
u[114]         -6.2e+000 2.5e-002     5.1 -1.5e+001 -6.2e+000  2.4e+000 4.3e+004 3.3e+002 1.0e+000
u[115]         -7.9e+000 1.1e-002     2.4 -1.2e+001 -8.0e+000 -4.0e+000 5.0e+004 3.8e+002 1.0e+000
u[116]         -1.1e+001 1.0e-002     2.3 -1.5e+001 -1.1e+001 -7.1e+000 4.8e+004 3.7e+002 1.0e+000
u[117]         -1.1e+001 9.5e-003     2.0 -1.5e+001 -1.1e+001 -7.8e+000 4.6e+004 3.5e+002 1.0e+000
u[118]         -4.4e+000 1.9e-002     4.2 -1.1e+001 -4.4e+000  2.4e+000 4.7e+004 3.6e+002 1.0e+000
u[119]         -5.4e+000 9.3e-003     2.0 -8.7e+000 -5.4e+000 -2.0e+000 4.7e+004 3.6e+002 1.0e+000
u[120]         -1.4e+001 9.8e-003     2.1 -1.8e+001 -1.4e+001 -1.1e+001 4.6e+004 3.5e+002 1.0e+000
u[121]         -1.2e+001 1.1e-002     2.5 -1.6e+001 -1.2e+001 -7.6e+000 4.6e+004 3.5e+002 1.0e+000
u[122]         -2.8e+000 1.2e-002     2.5 -6.9e+000 -2.8e+000  1.2e+000 4.6e+004 3.5e+002 1.0e+000
u[123]         -9.3e+000 1.2e-002     2.8 -1.4e+001 -9.3e+000 -4.8e+000 5.1e+004 3.9e+002 1.0e+000
u[124]         -1.6e+001 1.6e-002     3.3 -2.1e+001 -1.6e+001 -1.0e+001 4.2e+004 3.2e+002 1.0e+000
u[125]         -8.7e+000 2.0e-002     4.4 -1.6e+001 -8.7e+000 -1.6e+000 4.9e+004 3.8e+002 1.0e+000
u[126]         -1.2e+001 1.2e-002     2.6 -1.6e+001 -1.2e+001 -7.8e+000 4.5e+004 3.4e+002 1.0e+000
u[127]         -6.5e+000 1.6e-002     3.5 -1.2e+001 -6.4e+000 -7.8e-001 4.7e+004 3.6e+002 1.0e+000
u[128]         -8.0e+000 9.8e-003     2.2 -1.2e+001 -8.0e+000 -4.4e+000 4.9e+004 3.7e+002 1.0e+000
u[129]         -7.5e+000 1.2e-002     2.7 -1.2e+001 -7.5e+000 -3.1e+000 4.6e+004 3.5e+002 1.0e+000
u[130]         -7.0e+000 8.4e-003     1.8 -1.0e+001 -7.0e+000 -4.0e+000 4.8e+004 3.6e+002 1.0e+000
u[131]         -6.9e+000 1.6e-002     3.5 -1.3e+001 -6.9e+000 -1.0e+000 4.9e+004 3.8e+002 1.0e+000
u[132]         -7.9e+000 2.2e-002     4.7 -1.6e+001 -7.9e+000 -1.3e-001 4.5e+004 3.4e+002 1.0e+000
u[133]         -1.1e+001 2.3e-002     4.7 -1.9e+001 -1.1e+001 -3.5e+000 4.3e+004 3.3e+002 1.0e+000
u[134]         -1.1e+001 1.5e-002     3.2 -1.6e+001 -1.1e+001 -5.7e+000 4.8e+004 3.6e+002 1.0e+000
u[135]         -4.9e+000 1.9e-002     4.0 -1.1e+001 -4.9e+000  1.7e+000 4.5e+004 3.4e+002 1.0e+000
u[136]         -1.4e+001 1.1e-002     2.2 -1.8e+001 -1.5e+001 -1.1e+001 4.3e+004 3.3e+002 1.0e+000
u[137]         -4.8e-001 8.3e-003     1.7 -3.2e+000 -4.8e-001  2.3e+000 4.0e+004 3.0e+002 1.0e+000
u[138]         -9.5e+000 9.1e-003     1.9 -1.3e+001 -9.5e+000 -6.4e+000 4.5e+004 3.4e+002 1.0e+000
u[139]         -1.0e+001 1.0e-002     2.1 -1.4e+001 -1.0e+001 -6.7e+000 4.4e+004 3.4e+002 1.0e+000
u[140]         -6.9e+000 1.2e-002     2.5 -1.1e+001 -7.0e+000 -2.8e+000 4.6e+004 3.5e+002 1.0e+000
u[141]         -1.6e+001 1.5e-002     3.2 -2.1e+001 -1.6e+001 -1.0e+001 4.4e+004 3.3e+002 1.0e+000
u[142]         -1.4e+001 1.4e-002     3.0 -1.9e+001 -1.4e+001 -8.8e+000 4.8e+004 3.7e+002 1.0e+000
u[143]         -7.9e+000 8.9e-003     2.0 -1.1e+001 -7.9e+000 -4.6e+000 4.9e+004 3.8e+002 1.0e+000
u[144]         -8.0e+000 2.3e-002     5.1 -1.6e+001 -8.0e+000  3.7e-001 4.9e+004 3.7e+002 1.0e+000
u[145]         -1.1e+001 1.2e-002     2.5 -1.5e+001 -1.1e+001 -6.7e+000 4.6e+004 3.5e+002 1.0e+000
u[146]         -1.8e+001 8.9e-003     1.7 -2.1e+001 -1.8e+001 -1.6e+001 3.7e+004 2.8e+002 1.0e+000
u[147]         -1.2e+001 1.4e-002     3.2 -1.7e+001 -1.2e+001 -6.7e+000 5.0e+004 3.8e+002 1.0e+000
u[148]         -1.1e+001 1.5e-002     3.3 -1.7e+001 -1.1e+001 -5.7e+000 4.7e+004 3.6e+002 1.0e+000
u[149]         -6.4e+000 9.6e-003     2.2 -1.0e+001 -6.4e+000 -2.8e+000 5.3e+004 4.1e+002 1.0e+000
u[150]         -7.5e+000 1.5e-002     3.4 -1.3e+001 -7.5e+000 -1.9e+000 5.0e+004 3.8e+002 1.0e+000
Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at 
convergence, R_hat=1).

Model converged

Your Environment

peclayson commented 2 years ago

First off, I'm very sorry for the delay in my response. I lost track of this issue.

I have uploaded a fix e458a8c8ffe59ece32b67da2b144816e1a920463 that you can pull from master by downloading the zip or cloning the repo.

You don't need to worry about issue 1. This is Stan behavior that is expected.

Your models were in fact converging, but there was an issue pulling up and plotting the traceplots. It should work now.

Sorry again about the delay. Please let me know whether the fix works for you!

Have a good weekend, Peter

KrisDG9 commented 2 years ago

Hi Peter,

Thank you so much, I'll try out the fix asap! I've just started working on a new laptop and haven't installed the ERA Toolbox yet since it was such a pain last time (as the formal instructions state as well hahaha) but I'm planning on doing it this week.

Really happy to know the models were indeed converging properly already! For me that's the most important thing at the moment of course :)

Best,

Kristel

Op vr 4 feb. 2022 om 18:45 schreef Peter @.***>:

First off, I'm very sorry for the delay in my response. I lost track of this issue.

I have uploaded a fix e458a8c https://github.com/peclayson/ERA_Toolbox/commit/e458a8c8ffe59ece32b67da2b144816e1a920463 that you can pull from master by downloading the zip https://github.com/peclayson/ERA_Toolbox/zipball/master/ or cloning the repo.

You don't need to worry about issue 1. This is Stan behavior that is expected.

Your models were in fact converging, but there was an issue pulling up and plotting the traceplots. It should work now.

Sorry again about the delay. Please let me know whether the fix works for you!

Have a good weekend, Peter

— Reply to this email directly, view it on GitHub https://github.com/peclayson/ERA_Toolbox/issues/29#issuecomment-1030210803, or unsubscribe https://github.com/notifications/unsubscribe-auth/AKUCMGMQYRVF7YZN7FY6PKDUZQGDBANCNFSM5FRWL6CQ . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

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peclayson commented 2 years ago

Best of luck with the installation! It can be a pain in the neck :)

Please let me know if you have any questions.

Good luck, Peter