Rene

Hi Shaini, with 'writeformat' in info.txt, you can define how dates in output files are printed, with or without hyphens. HYPEtools default is to assume with hyphens, corresponding to HYPE's own default behaviour. You can change that with argument 'dt.format': ReadBasinOutput("~/Downloads/0000001_000001.txt", dt.format = "%Y%m%d") /rené

Dear Sertac, I can (briefly) answer your question, but please note that this forum is meant for questions regarding HYPE itself, i.e. model code and running the model. For future questions regarding data published on hypeweb.smhi.se, please use the contact form here: https://hypeweb.smhi.se/contact-us/ As to your question: The ECVs are climate model results for the reference period. They are not actual historical time series, rather realisations of time series with similar statistical properties...

Actually, after re-reading the documentation for optpar.txt, it would be possible to automate the parameter class detection: If upper and lower boundaries are not identical in there, the parameter is calibrated. There is already a function 'OptimisedClasses()' in HYPEtools, which extracts that information. There is some coding necessary to match parameter names in bestsims with actual parameter names (they get these '.n' indices if there is more than one class calibrated in a run). but that is absolutely...

There is no fully automated tool for either of these tasks, but you can use some functions of the HYPEtools R package as building blocks for a semi-automation script in R. https://github.com/rcapell/HYPEtools/wiki For respar.txt, things are fairly straightforward, because parameter values for all classes are given in the file. This R code snippet imports respar and par files, replaces respar results in par, and exports the updated file. library(HYPEtools) # import respar file, omitting first row...

Actually, after re-reading the documentation for optpar.txt, it would be possible to automate the parameter class detection: If upper and lower boundaries are not identical in there, the parameter is calibrated. There is already a function 'OptimisedClasses()' in HYPEtools, which extracts that information. There is some coding necessary to match parameter names in bestsims with actual parameter names (they get these '.n' indices if there is more than one class calibrated in a run). but that is absolutely...

There is no fully automated tool for either of that tasks, but you can use some functions of the HYPEtools R package as building blocks for a semi-automation script in R. https://github.com/rcapell/HYPEtools/wiki For respar.txt, things are fairly straightforward, because parameter values for all classes are given in the file. This R code snippet imports respar and par files, replaces respar results in par, and exports the updated file. library(HYPEtools) # import respar file, omitting first row with...

Hi Salim, thanks for the feedback! The formula in the wiki is now corrected. /Re...