modules::import(DT)
modules::import(shiny)
modules::import(shinyBS)
modules::import(shinyjs)
modules::import(shinydashboard)
modules::import(htmltools)
modules::import(DiagrammeR)
modules::import(magrittr)
modules::import(plotly)
modules::import(kableExtra)
modules::import(Rgraphviz)
modules::import(knitr)
modules::import(shinycssloaders)
modules::import(googleway)
modules::import(Rgraphviz)
modules::import(bnlearn)

parser <- modules::use('Parses.R')

layers <- c("Pressures to Bio-Assemblages", "Bio-Assemblages to Output Processes", "Output Processes to Eco-system services")
transitions <- c("Pressures to Bio-Assemblages", "Pressures to Output Processes", "Pressures to Eco-system services")
addResourcePath("js", "./www/js")

ui<-dashboardPage(
      dashboardHeader(title = "JNCC MESO online"),
      dashboardSidebar(
        sidebarMenu(id = "tabs",
            menuItem("Pressure Test", tabName = "1", icon = icon("arrow-down")),
            menuItem("Bayesian Network", tabName = "2", icon = icon("atom")),
            menuItem("Habitats", tabName = "3", icon = icon("atlas")),
            selectInput("modelSelect", "Select MESO model", choices=c(""), selected=NULL, multiple=FALSE),
            selectInput("layerSelect", "Select Transition", 
                        choices=transitions, 
                        selected=NULL, multiple=FALSE)
        )
      ),
      dashboardBody(
        tabItems(
          tabItem(tabName = "1",
                 fluidRow(
                  column(width=2,
                    h4('Pressure Test'),
                    radioButtons("pressure1", 'Sediment type', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure2", 'Seabed type', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure3", 'Material extraction', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure4", 'Abrasion of seabed', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure5", 'Penetration of seabed', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure6", 'Siltation', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure7", 'Wave exposure', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure8", 'Suspended sediment', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure9", 'Generic contamination', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure10", 'Deoxygenation', choices=c('On', 'Off'), inline=TRUE),
                    radioButtons("pressure11", 'Removal of target species', choices=c('On', 'Off'), inline=TRUE),
                    actionButton("calcAB", "Calc")
                  ),
                  column(width=10, 
                    h4('Effect on bio-assemblage'),
                    plotlyOutput("layer1", height="270px") %>% withSpinner(),
                    h4('Effect on Output Processes'),
                    plotlyOutput("layer2", height="270px") %>% withSpinner(),
                    h4('Effect on Eco-system services'),
                    plotlyOutput("layer3", height="270px") %>% withSpinner()
                  )
               )
          ),
          tabItem(tabName = "2",h4("Bayesian Network"),
              fluidPage(
                fluidRow(
                  plotOutput("bbnGraphPlot")
                ),
                fluidRow(
                  column(
                    width=6,
                    h4('Ecoservice nodes'),
                    DT::dataTableOutput('nodeTable')
                  ),
                  column(
                    width=6,
                    h4('Ecoservice influences'),
                    DT::dataTableOutput('edgeTable')
                  )
                )
              )
          ),
          tabItem(tabName = "3",h4("Habitats"),
              fluidPage(
                google_mapOutput(outputId = "map", width = "100%", height = "750px")
              )
          )
        )
      )
)

server <- function(input, output, session) {
  #SERVER Constants
 
  print('Loading data')
  
  set_key("AIzaSyAw8_btgGN1drf8qhCxNcotP6r11qEXA_M")
  dataStorage <- 'data/'
  
  models<-NULL
  
  getAvailableModels <- function() {
    fileList <- list.files(dataStorage, pattern='.xlsx')
    
    print(fileList)
    modelList <- list()
    cnt<-1
    
    for (idx in 1:length(fileList)) {
      print(paste('attempting to load', paste0(dataStorage, fileList[idx])))
      tmp <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
      
      if (!is.null(tmp)) {
        modelList[[cnt]] <- tmp
        
        #tidy up the list for displaying
        
        models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
        print(paste('Model file successfully loaded', fileList[idx]))
        cnt=cnt+1
        
        
      }
    }
    
    updateSelectInput(session, "modelSelect", choices=models)
    
    return(modelList)
  }
  
  
  .selections <- reactiveValues(model=1, layer=1)
  
  #parse on load sheets in the input sheet folder
  modelList <- getAvailableModels()
  
  calcLikelihood <- function(layer) {
    
    isolate({
      if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else layerStr ='es'
      nodeList <- modelList[[.selections$model]][[.selections$layer]]$nodes
      str(nodeList)
      nodeNames <- nodeList$name[startsWith(nodeList$code, layerStr)]
      mean = runif(length(nodeNames), min=-1, max=1)
      sd = runif(length(nodeNames), min=-0.25, max=0.25)
      
      df <- data.frame(
        nodeNames = nodeNames,
        range = c((mean - (3*sd)), (mean - (2*sd)), (mean - sd), mean,
                  (mean + sd), (mean + (2*sd)), (mean + (3*sd))),
        stringsAsFactors=FALSE
      )
      print(df)
    })
    return(
      df
    )
    # isolate({   
    #   
    #   if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else if (layer==3) layerStr='es'
    #     
    #   layerRange <- which(startsWith(modelList[[.selections$model]][[layer]]$nodes, layerStr))
    #   distList <- modelList[[.selections$model]][[layer]]$summDist[,layerRange]
    #   nodeNames <- modelList[[.selections$model]][[layer]]$nodes$name[layerRange]
    #     
    #   }
      
    #   print(paste('Length of layer & node names',layer, length(nodeNames)))
    #
    #   distList <- modelList[[.selections$model]][[layer]]$summDist
    #   colNames <- c('min', 'q1', 'q1', 'mean', 'q3', 'q3', 'max')
    #   distM <- matrix(data=NA, nrow=ncol(distList), ncol=length(colNames))
    #   
    #   print(paste('Length of distributions',nrow(distM)))

    #   for (col in 1:ncol(distList)) {
    #     valsAsStrs <- unlist(strsplit(distList[,col], ":"))
    #     valIdxs <- seq(from=2, to=length(valsAsStrs), by=2)
    #     distVals <- as.numeric(valsAsStrs[valIdxs])
    #     distM[col,] <- c(distVals[1], distVals[2], distVals[2], distVals[4], distVals[5], distVals[5], distVals[6])
    #   }
    # })
    #
    #  df <- data.frame(
    #        nodeNames = nodeNames,
    #        dist = distM,
    #        stringsAsFactors=FALSE
    #      )
    #  print(df)
    # 
    # return(
    #   df   
    #    )
  }
  
  
  .likelihoods <-reactiveValues(
    p_ba = calcLikelihood(1),
    ba_os = calcLikelihood(2),
    os_es = calcLikelihood(3)
  )
 
  observeEvent(input$modelSelect, {
    .selections$model <<- match(input$modelSelect, models)
    #print(.selections$model)
  })
  
  observeEvent(input$layerSelect, {
    .selections$layer <<- match(input$layerSelect, transitions)
    #print(.selections$layer)
  })
  
  observeEvent(input$calcAB, {
    #print(paste('Action button pressed', input$calcAB))
    
      .likelihoods$p_ba <<- calcLikelihood(1)
      .likelihoods$ba_os <<- calcLikelihood(2)
      .likelihoods$os_es <<- calcLikelihood(3)

  })
  
  output$map <- renderGoogle_map({
    google_map(location = c(55, 0), zoom = 7)
  })
  
 
  
  
  output$nodeTable <- DT::renderDataTable(
    
    modelList[[.selections$model]][[.selections$layer]]$nodes,
      selection = 'single',options = list(searching = TRUE, pageLength = 10),server = TRUE, escape = FALSE,rownames= TRUE
    )

  output$edgeTable <- DT::renderDataTable(
    
    modelList[[.selections$model]][[.selections$layer]]$edges,
      selection = 'single',options = list(searching = TRUE, pageLength = 10),server = TRUE, escape = FALSE,rownames= TRUE
  )
  
  output$bbnGraphPlot <- renderPlot({
    graphviz.plot(modelList[[.selections$model]][[.selections$layer]]$net)
  })
  
  output$layer1 <- renderPlotly({
    plot_ly(.likelihoods$p_ba, y = ~range, color = ~nodeNames, type = "box")
  })

  output$layer2 <- renderPlotly({
    #print(.likelihoods)
    
    if (.selections$layer>1) {
      plot_ly(.likelihoods$ba_os, y = ~range, color = ~nodeNames, type = "box")
    }
  })

  output$layer3 <- renderPlotly({
    
    if (.selections$layer>2) {
      plot_ly(.likelihoods$os_es, y = ~range, color = ~nodeNames, type = "box")
    }
  })
}

shinyApp(ui, server)