### install package dependencies
cran_pkgs <- c("gdata", "remotes", "swmmr", "sessioninfo", "xts")
install.packages(pkgs = cran_pkgs, repos = "https://cran.rstudio.com")
#> Installing packages into 'C:/Users/mrustl/Documents/R/win-library/4.0'
#> (as 'lib' is unspecified)
#> package 'gdata' successfully unpacked and MD5 sums checked
#> package 'remotes' successfully unpacked and MD5 sums checked
#> package 'swmmr' successfully unpacked and MD5 sums checked
#> package 'sessioninfo' successfully unpacked and MD5 sums checked
#> package 'xts' successfully unpacked and MD5 sums checked
#> 
#> The downloaded binary packages are in
#>  C:\Users\mrustl\AppData\Local\Temp\Rtmp4UTqOi\downloaded_packages

remotes::install_github("kwb-r/kwb.utils")
#> Using github PAT from envvar GITHUB_PAT
#> Skipping install of 'kwb.utils' from a github remote, the SHA1 (c9f447ab) has not changed since last install.
#>   Use `force = TRUE` to force installation

### Download "EPA SWMM 5.1.015.7z" from KWB cloud 
### "https://cloud.kompetenz-wasser.de/index.php/f/182136"
### and extract to "C:/_UserProg/EPA SWMM 5.1.015/"

### define paths
paths_list <- list(
  root_data = "//medusa/projekte$/WWT_Department/Projects/KEYS/Data-Work packages/WP1_sponge_city_elements/_DataAnalysis",
  root_swmm = "C:/_UserProg/",
  swmm_version = "5.1.015",
  swmm_exe = "<root_swmm>/EPA SWMM <swmm_version>/swmm5.exe",
  lid_models = "<root_data>/LIDmodels",
  green_roof = "<lid_models>/greenRoof"
)

### at kwb:
paths <- kwb.utils::resolve(paths_list)

### at home:
if (FALSE) {
paths <- kwb.utils::resolve(paths_list,   
                            root_data = "C:/kwb/projects/keys/data/_DataAnalysis")
}

# create swmm model


# read swmm model


# define parameter intervals


# run sensitivity analysis, input time series ideally should cover several years


# write out results


# plot results


# annual VRR vs weather vs. lid parameters



# read swmm input file
swmm_inp <- file.path(paths$green_roof, "BWSTI_Zone1/BWSTI_Zone1.inp")
swmm_inp
#> [1] "//medusa/projekte$/WWT_Department/Projects/KEYS/Data-Work packages/WP1_sponge_city_elements/_DataAnalysis/LIDmodels/greenRoof/BWSTI_Zone1/BWSTI_Zone1.inp"
input <- swmmr::read_inp(x = swmm_inp)
#> Warning: Expected 9 pieces. Additional pieces discarded in 1 rows [1].
summary(input)
#> 
#> ** summary of swmm model structure ** 
#> infiltration   :     horton 
#> flow_units     :        lps 
#> flow_routing   :    kinwave 
#> start_date     : 04/06/2014 
#> end_date       : 11/21/2016 
#> raingages      :          1 
#> subcatchments  :          1 
#> aquifers       :          0 
#> snowpacks      :          1 
#> junctions      :          0 
#> outfalls       :          1 
#> dividers       :          0 
#> storage        :          0 
#> conduits       :          0 
#> pumps          :          0 
#> orifices       :          0 
#> weirs          :          0 
#> outlets        :          0 
#> controls       :          0 
#> pollutants     :          0 
#> landuses       :          0 
#> lid_controls   :          1 
#> treatment      :          0 
#> *************************************

# length of the loop
l <- 1000

# data frame to write parameter values 
cal_names<-c("Run", "Soil_Thickness", "Porosity", "Field_Capacity", "Wilting_Point", "Conductivity", "Conductivity_Slope", "Suction_Head", # Soil Parameters
             "Drain_Thickness", "Void_Fraction", "Roughness", # Drainage Mat Parameters 
             "Sum_R"# Total Runoff
             )

cal_results <- data.frame(matrix(
  data = NA,
  ncol = length(cal_names),
  nrow = l))

cal_col <- colnames(cal_results)

cal_results <- gdata::rename.vars(cal_results,cal_col, cal_names)
#> 
#> Changing in cal_results                                                                           
#> From: X1  X2             X3       X4             X5            X6          
#> To:   Run Soil_Thickness Porosity Field_Capacity Wilting_Point Conductivity
#>                                                                              
#> From: X7                 X8           X9              X10           X11      
#> To:   Conductivity_Slope Suction_Head Drain_Thickness Void_Fraction Roughness
#>            
#> From: X12  
#> To:   Sum_R


# time period
seq <- seq.POSIXt(ISOdate(2008,4,30,00,05,tz="UTC"),
                  ISOdate(2019,10,15,23,00,tz="UTC"),
                  by="5 min")


# runoff results of the green roof
for (i in 1:l){
  
  # set parameters for simulation
  input$lid_controls$Par1[3] <- runif(n = 1, min = 80, max = 120) # Soil Thickness
  input$lid_controls$Par2[3] <- runif(n = 1, min = 0.45, max = 0.65) # Porosity
  input$lid_controls$Par3[3] <- runif(n = 1, min = 0.35, max = 0.55) # Field Capacity
  input$lid_controls$Par4[3] <- runif(n = 1, min = 0.05, max = 0.20) # Wilting Point
  input$lid_controls$Par5[3] <- runif(n = 1, min = 50, max = 350) # Conductivity
  input$lid_controls$Par6[3] <- runif(n = 1, min = 30, max = 55) # Conductivity Slope
  input$lid_controls$Par7[3] <- runif(n = 1, min = 50, max = 100) # Suction Head
  input$lid_controls$Par1[4] <- runif(n = 1, min = 10, max = 50) # Storage Thickness
  input$lid_controls$Par2[4] <- runif(n = 1, min = 0.3, max = 0.5) # Void Fraction
  input$lid_controls$Par3[4] <- runif(n = 1, min = 0.01, max = 0.03) # Roughness
  
  # write values in the dataframe
  cal_results$Run[i] <- i
  
  cal_results$Soil_Thickness[i] <- input$lid_controls$Par1[3]
  cal_results$Porosity[i] <- input$lid_controls$Par2[3]
  cal_results$Field_Capacity[i] <- input$lid_controls$Par3[3] 
  cal_results$Wilting_Point[i] <- input$lid_controls$Par4[3]
  cal_results$Conductivity[i] <- input$lid_controls$Par5[3]
  cal_results$Conductivity_Slope[i] <- input$lid_controls$Par6[3]
  cal_results$Suction_Head[i] <- input$lid_controls$Par7[3]
  cal_results$Drain_Thickness[i] <- input$lid_controls$Par1[4]
  cal_results$Void_Fraction[i] <- input$lid_controls$Par2[4]
  cal_results$Roughness[i] <- input$lid_controls$Par3[4]
  
  # save the changed input file
  swmmr::write_inp(input,"Validation_Beijing.inp") 
  
  # run swimm with changed input file
  files <- swmmr::run_swmm(inp = "Validation_Beijing.inp",
                           exec = paths$swmm_exe) 
  
  # read out results for itype 3 = system and vIndex 4 = runoff 
  results <- swmmr::read_out(files$out, iType = 3, vIndex = c(4)) 
  
  # change timezone to UTC
  xts::tzone(results$system_variable$total_runoff) <- "UTC"
  
  # write model runoff in data frame
  runoff_sim <- list()

  # extract model runoff of entire simulation period
  runoff_sim <- data.frame(matrix(
    data = NA,
    ncol = 1,
    nrow = length(seq)))
  
  colnames(runoff_sim)<-"sim" 
  
  runoff_sim$DateTime <- seq
  
  runoff_sim$sim <- (as.numeric(coredata(results$system_variable$total_runoff)))*300/65
  
  ### calculate sum of Runoff
  sum_sim <- sum(runoff_sim$sim) 
  
  cal_results$Sum_R[i] <- sum_sim
 
  print(paste("Run",i,"of",l,"finished"),sep=" ")
}

#o  Retrieving project data
#... EPA-SWMM 5.1 (Build 5.1.15)
#... EPA-SWMM completed in 0.00 seconds. There are errors.

#> Warning in normalizePath(path.expand(path), winslash, mustWork): path[1]="./
#> Validation_Beijing.out": Das System kann die angegebene Datei nicht finden
#> Warning in swmmr::read_out(files$out, iType = 3, vIndex = c(4)): error reading
#> out file
#> Error in attr(x, "tzone") <- value: Versuch ein Attribut von NULL zu setzen

paths$swmm_exe
#[1] "C:/_UserProg/EPA SWMM 5.1.015/swmm5.exe"

sessioninfo::session_info()
#> - Session info ---------------------------------------------------------------
#>  setting  value                       
#>  version  R version 4.0.2 (2020-06-22)
#>  os       Windows 10 x64              
#>  system   i386, mingw32               
#>  ui       RTerm                       
#>  language (EN)                        
#>  collate  German_Germany.1252         
#>  ctype    German_Germany.1252         
#>  tz       Europe/Berlin               
#>  date     2020-11-13                  
#> 
#> - Packages -------------------------------------------------------------------
#>  package     * version date       lib source                          
#>  assertthat    0.2.1   2019-03-21 [1] CRAN (R 4.0.2)                  
#>  cli           2.1.0   2020-10-12 [1] CRAN (R 4.0.3)                  
#>  crayon        1.3.4   2017-09-16 [1] CRAN (R 4.0.2)                  
#>  curl          4.3     2019-12-02 [1] CRAN (R 4.0.2)                  
#>  digest        0.6.27  2020-10-24 [1] CRAN (R 4.0.3)                  
#>  dplyr         1.0.2   2020-08-18 [1] CRAN (R 4.0.3)                  
#>  ellipsis      0.3.1   2020-05-15 [1] CRAN (R 4.0.2)                  
#>  evaluate      0.14    2019-05-28 [1] CRAN (R 4.0.2)                  
#>  fansi         0.4.1   2020-01-08 [1] CRAN (R 4.0.2)                  
#>  gdata         2.18.0  2017-06-06 [1] CRAN (R 4.0.3)                  
#>  generics      0.1.0   2020-10-31 [1] CRAN (R 4.0.3)                  
#>  glue          1.4.2   2020-08-27 [1] CRAN (R 4.0.2)                  
#>  gtools        3.8.2   2020-03-31 [1] CRAN (R 4.0.3)                  
#>  highr         0.8     2019-03-20 [1] CRAN (R 4.0.2)                  
#>  hms           0.5.3   2020-01-08 [1] CRAN (R 4.0.2)                  
#>  htmltools     0.5.0   2020-06-16 [1] CRAN (R 4.0.2)                  
#>  knitr         1.30    2020-09-22 [1] CRAN (R 4.0.2)                  
#>  kwb.utils     0.7.0   2020-10-05 [1] Github (kwb-r/kwb.utils@c9f447a)
#>  lattice       0.20-41 2020-04-02 [1] CRAN (R 4.0.3)                  
#>  lifecycle     0.2.0   2020-03-06 [1] CRAN (R 4.0.2)                  
#>  magrittr      1.5     2014-11-22 [1] CRAN (R 4.0.2)                  
#>  pillar        1.4.6   2020-07-10 [1] CRAN (R 4.0.2)                  
#>  pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.0.2)                  
#>  purrr         0.3.4   2020-04-17 [1] CRAN (R 4.0.2)                  
#>  R6            2.5.0   2020-10-28 [1] CRAN (R 4.0.3)                  
#>  Rcpp          1.0.5   2020-07-06 [1] CRAN (R 4.0.2)                  
#>  readr         1.4.0   2020-10-05 [1] CRAN (R 4.0.2)                  
#>  remotes       2.2.0   2020-07-21 [1] CRAN (R 4.0.3)                  
#>  rlang         0.4.8   2020-10-08 [1] CRAN (R 4.0.3)                  
#>  rmarkdown     2.4     2020-09-30 [1] CRAN (R 4.0.2)                  
#>  sessioninfo   1.1.1   2018-11-05 [1] CRAN (R 4.0.3)                  
#>  stringi       1.5.3   2020-09-09 [1] CRAN (R 4.0.2)                  
#>  stringr       1.4.0   2019-02-10 [1] CRAN (R 4.0.3)                  
#>  swmmr         0.9.1   2020-03-02 [1] CRAN (R 4.0.3)                  
#>  tibble        3.0.4   2020-10-12 [1] CRAN (R 4.0.3)                  
#>  tidyr         1.1.2   2020-08-27 [1] CRAN (R 4.0.2)                  
#>  tidyselect    1.1.0   2020-05-11 [1] CRAN (R 4.0.2)                  
#>  vctrs         0.3.4   2020-08-29 [1] CRAN (R 4.0.2)                  
#>  withr         2.3.0   2020-09-22 [1] CRAN (R 4.0.2)                  
#>  xfun          0.18    2020-09-29 [1] CRAN (R 4.0.2)                  
#>  xts           0.12.1  2020-09-09 [1] CRAN (R 4.0.3)                  
#>  yaml          2.2.1   2020-02-01 [1] CRAN (R 4.0.2)                  
#>  zoo           1.8-8   2020-05-02 [1] CRAN (R 4.0.2)                  
#> 
#> [1] C:/Users/mrustl/Documents/R/win-library/4.0
#> [2] C:/Program Files/R/R-4.0.2/library