Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

parsing error of section OUTFALLS when name of time series exceeds 10 characters #34

Open
laltuashu opened this issue May 16, 2019 · 5 comments

Comments

@laltuashu
Copy link

laltuashu commented May 16, 2019

Hello all,
When I run the model using parallel mode in DEoptim, the error is as given as below
"Error in checkForRemoteErrors(val) : 4 nodes produced errors; first error: cannot coerce class 'c("xts", "zoo")' to a data.frame".

,But when I run the model without parallel mode in DEoptim, I got the errors as below

Error in [.xts(x, , 2) : subscript out of bounds
In addition: Warning messages:
1: In normalizePath(path.expand(path), winslash, mustWork) :
path[1]="C:/Users/ratnakar/AppData/Local/Temp/RtmpkNMgOy/fileca41c942dd9.out": The system cannot find the file specified
2: In read_out(file = swmm_files$out, iType = 1, object_name = "621", :
error reading out file

Any suggestions in this regard will be highly appreciated.

Regards,
Ashu

@dleutnant
Copy link
Owner

@laltuashu I'm able to reproduce the error. A quick fix would be to limit the length of the name of your outfall time series to 10 characters, i.e. "outfall_timeseries1" becomes "outfall_ti".

@dleutnant dleutnant changed the title Error during calibration parsing error of section OUTFALLS when name of time series exceeds 10 characters May 20, 2019
@laltuashu
Copy link
Author

Hello @dleutnant Sir,
Thank you for the guidance. The error is resolved now, but the simulation time is too high.
Is there any way to increase the speed of calibration execution?

Regards,
Ashu

@dleutnant
Copy link
Owner

You could 1) modify your model structure and setup (model size, time steps, simulation duration, etc.) with respect to your modeling objective and/or 2) use more computational resources if the algorithm supports parallel execution.

@dleutnant
Copy link
Owner

...of course the choice of optimization algorithm also might improve your calibration efficiency.

@dleutnant dleutnant reopened this May 24, 2019
@laltuashu
Copy link
Author

Thank you sir for your suggestion.
I will certainly try ways to reduce simulation time.

With Regards,
Ashu

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants