In JBrenn/TopoSUB: TopoSUB tool

This R Markdown document is made interactive using Shiny. To learn more, see Interactive Documents.

if(!require("TopoSUB"))
{
  if(!require("devtools"))
  {
    install.packages("devtools")
    require("devtools")
  }
  install_github("TopoSUB", "JBrenn")
  require("TopoSUB")
}

if(!require("dygraphs"))
{
  install.packages("dygraphs")
  require("dygraphs")
}

if(!require("hydroGOF"))
{
  install.packages("hydroGOF")
  require("hydroGOF")
}

if(!require("ggplot2"))
{
  install.packages("ggplot2")
  require("ggplot2")
}

if(!require("leaflet"))
{
  install.packages("leaflet")
  require("leaflet")
}

if(!require("raster"))
{
  install.packages("leaflet")
  require("raster")
}

#wpath <- "/run/user/1000/gvfs/smb-share:server=sdcalp01.eurac.edu,share=data2/Simulations/Simulation_GEOtop_1_225_ZH/Vinschgau/SimTraining/BrJ/Mazia/toposub/sim/1d/1d_002/000000/"
wpath <- "/home/jbr/Schreibtisch/test_sim/"

Mapping Observation Stations

# read meta data
meta_data <- TopoSUB_getCluster4verification(wpath)

renderLeaflet({

  # simulation domain - landform file
  landform_asc <- dir(wpath)[grepl("landform", dir(wpath))]
  landform_rst <- raster(file.path(wpath, landform_asc))

  #landform_rst[landform_rst==landform_rst[1]] <- NA
    # reclass to NA values
  landform_rst <- reclassify(landform_rst, cbind(landform_rst[1], NA))
    # project
  crs(landform_rst) <- CRS("+proj=utm +zone=32 ellps=WGS84")

  col <- grey.colors(n = length(meta_data$lng), start=.25, end=.25)
  colIT <- which(meta_data$name==input$station)
  col[colIT] <- "#bd0026"

  rad <- rep(5, length(meta_data$lng))
  rad[colIT] <- 10

  leaflet() %>%
   addProviderTiles("Acetate.terrain") %>%
   addTiles(options = providerTileOptions(opacity = 0.75)) %>%  # Add default OpenStreetMap map tiles
   addRasterImage(landform_rst, opacity = 0.35) %>%
   addCircleMarkers(lng=meta_data$lng, lat=meta_data$lat, popup=paste(meta_data$name, ", ",  meta_data$h, "m a.s.l"), color = col, radius=rad, fill = F)
  })

inputPanel(    
    selectInput(inputId = "station", label = "discover station", choices = as.character(meta_data$name), selected = "I1")
    )

var2select <- reactive({
  x = readLines(file.path(wpath,"obs",paste(input$station,".csv",sep="")), n=1)
  return(unlist(strsplit(x, ","))[-1])
})

renderUI(   
  selectInput(inputId = "variable", label = "discover variable", choices = var2select()))

Table on Station Meta Data

renderDataTable({

meta_data$Deltah <- meta_data$h - meta_data$dem    
meta_data[,c(2,3,19,1,10:15,18)]

}, options = list(pageLength=5, lengthMenu=c(5, 10, 15, 20)))

Time Series Plot

renderDygraph({

    data2plot <- TopoSUB_verificationData(wpath, input$station, input$variable, meta_data)

    dygraph(data2plot) %>%
      dyRangeSelector() %>%
      dyRoller() 

#    %>%
#      dySeries(name = "additional var", axis = "y2", stepPlot = TRUE, fillGraph = TRUE, label = #paste("[-",add_units,"]",sep=""))

#  }

})

Summary Table on Goodness of Fit (GOF)

renderDataTable({

  data2plot <- TopoSUB_verificationData(wpath, input$station, input$variable, meta_data)

  gofs <- gof(sim = data2plot$Simulation, obs=data2plot$Observation, na.rm = T)
  gofs <- as.data.frame(gofs)
  names(gofs) <- "YEAR"
  gofs$GOF <- dimnames(gofs)[[1]]

  mon <- as.numeric(format(time(data2plot), "%m"))

  datadjf <-  data2plot[mon==12 | mon==1 | mon==2,]
  gofs$DJF <-  c(gof(sim = datadjf$Simulation, obs=datadjf$Observation, na.rm = T))
  datamam <-  data2plot[mon==3 | mon==4 | mon==5,]
  gofs$MAM <-  c(gof(sim = datamam$Simulation, obs=datamam$Observation, na.rm = T))
  datajja <-  data2plot[mon==6 | mon==7 | mon==8,]
  gofs$JJA <-  c(gof(sim = datajja$Simulation, obs=datajja$Observation, na.rm = T))
  datason <-  data2plot[mon==9 | mon==10 | mon==11,]
  gofs$SON <-  c(gof(sim = datason$Simulation, obs=datason$Observation, na.rm = T))

  gofs <- gofs[,c(2,3,4,5,6,1)]

}, options = list(pageLength=5, lengthMenu=c(5, 10, 15, 20)))