modules::import(DT)
modules::import(shiny)
modules::import(shinyBS)
modules::import(shinyjs)
modules::import(shinydashboard)
modules::import(shinydashboardPlus)
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(bnlearn)
modules::import(visNetwork)
modules::import(RColorBrewer)
modules::import(zip)
modules::import(processx)
modules::import(openxlsx)


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

layers <- c("Pressures to Bio-Assemblages", "Bio-Assemblages to Output Processes", "Output Processes to Ecosystem services")
transitions <- c("Pressures to Bio-Assemblages", "Pressures to Output Processes", "Pressures to Ecosystem services")
impacts <- c('Very High', '>=High', '>=Medium', '>=Low', 'All')
thresholds <- c(0.97, 0.9, 0.45, 0.17, 0)
impLabels <- c('Very High', 'High', 'Medium', 'Low', 'Very Low')

legends <- c('Pressures',
             'Suspension feeders',
             'Mobile and burrow dwellers',
             'Predators',
             'Epifauna and algae',
             'Functional groups',
             'Output processes',
             'Output enablers',
             'Ecosystem services')

addResourcePath("js", "./www/js")

ui<-dashboardPage(
      dashboardHeader(title = "JNCC MESO online", 
        tags$li(
          id = "dropdownHelp",
          class = "dropdown",
          tags$head(
            tags$script(
              paste0(
                "$(document).ready(function(){",
                "  $('#dropdownHelp')",
                "  .find('ul')",
                "  .click(function(e) { e.stopPropagation(); });",
                "});"
              )
            )
          ),
          tags$a(
            href = "javascript:void(0);",
            class = "dropdown-toggle",
            `data-toggle` = "dropdown",
            icon("question")
          ),
          tags$ul(
            class = "dropdown-menu",
            style = "left: auto; right: 0; min-width: 200px",
            tags$li(
              tags$div(
                style = "margin-left: auto; margin-right: auto; width: 90%;",
                tags$a(
                  href = "Manual.pdf",
                  target = "_BLANK",
                  "Open user guide in tab"
                )
              )
            ),
            tags$li(
              tags$div(
                style = "margin-left: auto; margin-right: auto; width: 90%;",
                tags$a(
                  href = "Report.pdf",
                  target = "_BLANK",
                  "Open Final Report in tab"
                )
              )
            )
          )
        )
      ),
      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")),
            #menuItem("Ingestion", tabName = "3", icon = icon("utensils")),
            selectInput("modelSelect", "Select MESO model", choices=c(""), selected=NULL, multiple=FALSE),
            downloadButton("download", "", icon=icon("download")),
            uiOutput("pressureList")
            #selectInput("layerSelect", "Select Transition", 
            #            choices=transitions, 
            #            selected=NULL, multiple=FALSE)
        )
      ),
      dashboardBody(
        tabItems(
          tabItem(tabName = "1", h2('Impact Distribution'),
            fluidRow(
              column(
                width=6, 
                h4('Effect on bio-assemblage')
              ),
              column(
                width=1,
                actionButton("layer1Slider", "1", icon=icon("sliders-h"))
              ),
              column(
                width=5,
                strong("Customise sensitivity weightings")
              )
            ),
            plotlyOutput("layer1", height="270px") %>% withSpinner(),
            h4('Effect on Output Processes'),
            plotlyOutput("layer2", height="270px") %>% withSpinner(),
            h4('Effect on Ecosystem services'),
            plotlyOutput("layer3", height="270px") %>% withSpinner()
          ),
          tabItem(tabName = "2",h2("Bayesian Network"),
            fluidPage(
              p('Graphical output of the Bayesian Network.  Note: The graph will only draw if pressures are applied!'),
              fluidRow(
                column(
                  width=4,
                  checkboxInput("bbnDisplayNames", "Display Node names", value=FALSE)
                ),
                column(
                  width=4,
                  checkboxInput("bbnDisplayEdges", "Display edge status", value=FALSE)
                ),
                column(
                  width=4,
                  selectInput("bbnImpactSelect", "Impact Threshold", choices=impacts, selected='All')
                )
              ),
              fluidRow(
                visNetworkOutput("bbnGraphPlot", width = "100%", height = "1000px")
              ),
              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")
          #    )
          #), 
          tabItem(tabName = "3",h4("Ingestion"),
             fluidPage(
               p("Select a spreadsheet from your network for input into the JNCC Bayesian Network Analyser:"),
               fileInput("fileSelect", "Choose Excel Spreadsheet File (xlsx format)", multiple = FALSE, accept = "xlsx"),
               fluidRow(renderUI('status')),
               actionButton('loadAB', 'Load') # icon='upload')
             )
           )
        )
      )
)

server <- function(input, output, session) {
  #SERVER Constants
 
  print('Loading data')
  
  #set_key("AIzaSyAw8_btgGN1drf8qhCxNcotP6r11qEXA_M")
  dataStorage <- 'data/'
  
  models<-NULL
  pressures <- NULL
  
  .loadStatus <- reactiveValues(
    valid = c(p=FALSE, ba=FALSE, op=FALSE, es=FALSE),
    msgs = NULL
  )
  
  .likelihoods <-reactiveValues(
    p_ba = NULL,
    ba_os = NULL,
    os_es = NULL,
    p_es = NULL
  )
  
  setPressures <- function(newPressures) {
    pressures <<- newPressures
  }

 
  .resistanceScores <- c(
    ins= -0.01,
    hr = -0.2,
    mr = -0.75,
    lr = -0.95,
    nr = -0.99,
    ssgr = 0,
    pressSD = 0.5
  )
  
  .selections <- reactiveValues(
    model=1, 
    bbnImpact=1, 
    bbnNames=FALSE,
    bbnEdges=FALSE,
    pressStatus=NULL
  )
  
  getImpact <- function(v) {
    print(v)
    if ((v == "INS") || (v == "IV")) return(.resistanceScores[1])
    if ((v == "HR") || (v == "III")) return(.resistanceScores[2])
    if ((v == "MR") || (v == "II")) return(.resistanceScores[3])
    if ((v == "LR") || (v == "I")) return(.resistanceScores[4])
    if (v == "NR") return(.resistanceScores[5])
    as.numeric(v)
  }
  
  getAvailableModels <- function() {
    fileList <- list.files(dataStorage, pattern='.xlsx')
    
    modelList <- list()
    cnt<-1
    
    for (idx in 1:length(fileList)) {
      print(paste('attempting to load', paste0(dataStorage, fileList[idx])))
      
      tmp <- parser$parseSheet(paste0(dataStorage, fileList[idx]))
      print(tmp)
      tmp$edges$values <- sapply(tmp$edges$impact, getImpact)
      
      if (!is.null(tmp)) {
        modelList[[cnt]] <- tmp
        models <<- c(models, substr(fileList[idx], 1, (nchar(fileList[idx])-5)))
        print(paste('Model file successfully loaded', fileList[idx]))
        #save(tmp, file='tmp.RData')
        cnt=cnt+1
      }
    }
    updateSelectInput(session, "modelSelect", choices=models)
    return(modelList)
  }
  
  #parse on load sheets in the input sheet folder - replace with R Data
  modelList <- getAvailableModels()
  
  
  calcLikelihood <- function(layer, pressStatus) {
    
    isolate({

      #if (layer==1) layerStr='ba' else if (layer==2) layerStr='op' else if (layer==3) layerStr='es'

      
      
      #layerRange <- which(startsWith(thisModel$nodes$code, layerStr))
   
      #nodeCodes <-thisModel$nodes$code[layerRange]
      #nodeNames <- thisModel$nodes$name[layerRange]
      
      thisModel <- modelList[[.selections$model]]
      
      modelList[[.selections$model]]$edges$values <<- sapply(thisModel$edges$impact, getImpact)
      modelList[[.selections$model]]$nodes$growth <<- .resistanceScores['ssgr']
      modelList[[.selections$model]]$nodes$confidence <<- .resistanceScores['pressSD']
      
      thisModel <- modelList[[.selections$model]]
      

      MEANPOS=1
      MEANNEG=0
      
      expr <- "list("
      for (p in 1:nrow(pressStatus)) {
        if (pressStatus$status[p] == 'On') {
          threshold = MEANPOS
        } else {
          threshold = MEANNEG
        }
          
        expr <- paste0(expr, "\"", pressStatus$code[p], "\"=", threshold, ", ")
      }
      expr <-substr(expr, 1, nchar(expr)-2)
      expr<-paste0(expr, ')')
      
      thisNet <- parser$buildGraph(thisModel, desc=list(inputCode='p', outputCodes=c('ba', 'op', 'es')))

      sampleDists <- cpdist(
        fitted = thisNet$cfit,
        nodes = bnlearn::nodes(thisNet$cfit),
        evidence = eval(parse(text = expr)),
        method = "lw",
        n = 10000,
        debug=TRUE
      )
    })
    
    print(sampleDists)
    
    #displayCols <- match(nodeCodes, colnames(sampleDists))
    sampleDists <- sampleDists[,match(thisModel$nodes$code, colnames(sampleDists))]
    means <- apply(sampleDists, 2, mean)
    stdDev <- apply(sampleDists, 2, sd)
    
    print(paste('Building likelihoods from model, sample dists', length(thisModel$nodes$name), length(sampleDists)))
    
    return(data.frame(
           name = thisModel$nodes$name,
           code = thisModel$nodes$code,
           layer = thisModel$nodes$layer,
           range = c(
                  apply(sampleDists, 2, min),
                  means - 2*stdDev,
                  means - stdDev,
                  means,
                  means + stdDev,
                  means + 2*stdDev,
                  apply(sampleDists, 2, max)
           ),
           stringsAsFactors=FALSE
         ))
  }
  
    
  observeEvent(input$modelSelect, {
    .selections$model <<- match(input$modelSelect, models)
  })
  
  observeEvent(reactiveValuesToList(input), {
    isolate(myList <- reactiveValuesToList(input))
    matches <- match(pressures$code,  names(myList))

    if (length(matches)>0) {
      status <-NULL
      for (n in 1:length(matches))  status[n] = myList[[matches[n]]]
      
      newStatus <- data.frame(code=pressures$code, status=status, stringsAsFactors = FALSE)
      
      if (!identical(newStatus, .selections$pressStatus)) {
        print('Running calc')
        #.likelihoods$p_ba <<- calcLikelihood(1,  newStatus)
        #.likelihoods$ba_os <<- calcLikelihood(2, newStatus)
        #.likelihoods$os_es <<- calcLikelihood(3, newStatus)
        .likelihoods$p_es <<- calcLikelihood(0, newStatus)
        #write.xlsx(.likelihoods$p_es, 'tmp.xlsx')
        .selections$pressStatus <<- newStatus
      }
      
    }
  })
  
  makeRadioButtons <- function(row) {
    radioButtons(row['code'], row['name'], choices=c('Off', 'On'), selected='Off', inline=TRUE)
  }
  
  output$pressureList <- renderUI({
    #isolate({
    if (!is.null(modelList[[.selections$model]]$nodes)) {
      pressCodes <- which(startsWith(modelList[[.selections$model]]$nodes$code, 'p'))
      pressures <- data.frame(code = modelList[[.selections$model]]$nodes$code[pressCodes], 
                     name = modelList[[.selections$model]]$nodes$name[pressCodes], stringsAsFactors=FALSE)
      setPressures(pressures)
      btnList <- apply(pressures, 1, makeRadioButtons)
    }
  })
  
  observeEvent(input$bbnImpactSelect, {
    #filter nodes and edges to 
    .selections$bbnImpact <- thresholds[match(input$bbnImpactSelect, impacts)]
    print(paste("Setting bbn impact", .selections$bbnImpact))
  })
  
  observeEvent(input$bbnDisplayNames, {
    .selections$bbnNames <- input$bbnDisplayNames
    print(.selections$bbnNames)
  })
  
  observeEvent(input$bbnDisplayEdges, {
    .selections$bbnEdges <- input$bbnDisplayEdges

  })
  
  
  observeEvent(input$layer1Slider, {
    showModal(
      modalDialog({
        tagList(
          sliderInput("l1VL", "Insensitive", 0.01, 0.2, abs(.resistanceScores[1]), step=0.01),
          sliderInput("l1L", "Low Sensitivity/High resistance", 0.15, 0.5, abs(.resistanceScores[2]), step=0.01),
          sliderInput("l1M", "Medium Sensitivity/Med resistance", 0.5, 0.75, abs(.resistanceScores[3]), step=0.01),
          sliderInput("l1H", "High Sensitivity/Low resistance", 0.75, 1.0, abs(.resistanceScores[4]), step=0.01),
          sliderInput("l1VH", "Very High Sensitivity/No resistance", 0.9, 1.0, abs(.resistanceScores[5]), step=0.01),
          sliderInput("ssgr", "Steady state growth rate", -0.1, 0.1,.resistanceScores[6], step=0.01),
          sliderInput("l1PressSD", "Pressure Std Dev", 0.1, 1.0, .resistanceScores[7], step=0.01)
        )
      }, 
      title='Layer 1 controls', 
      footer=tagList(
        modalButton("Cancel"),
        actionButton("modalOK", "OK")
      ),
      size='s')
    )
  })
  
  observeEvent(input$modalOK, {
    print('Modal ok pressed')

    .resistanceScores['nr'] <<- -input$l1VH
    .resistanceScores['lr'] <<- -input$l1H
    .resistanceScores['mr'] <<- -input$l1M
    .resistanceScores['hr'] <<- -input$l1L
    .resistanceScores['ins'] <<- -input$l1VL
    .resistanceScores['ssgr'] <<- input$ssgr
    .resistanceScores['pressSD'] <<- input$l1PressSD
    
    print('Running calc')
    #.likelihoods$p_ba <<- calcLikelihood(1,  .selections$pressStatus)
    #.likelihoods$ba_os <<- calcLikelihood(2, .selections$pressStatus)
    #.likelihoods$os_es <<- calcLikelihood(3, .selections$pressStatus)
    .likelihoods$p_es <<- calcLikelihood(0, .selections$pressStatus)
    removeModal()

  })
  
  
  output$nodeTable <- DT::renderDataTable(
    
    modelList[[.selections$model]]$nodes,
      selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
    )

  output$edgeTable <- DT::renderDataTable(
    
    modelList[[.selections$model]]$edges,
      selection = 'single',options = list(searching = TRUE, pageLength = 10, editable=TRUE),server = TRUE, escape = FALSE,rownames= TRUE
  )
  
  getLabel <- function(value) {
    sign <- ifelse(value<0, "-", "+")
    idx <- min(which((abs(value)>=thresholds)==TRUE))
    return(paste0(sign, impLabels[idx]))
  }
  
  makeBbnGraph <- function(model) {
    nodes <- model$nodes
    
    if (.selections$bbnEdges) {labels <- sapply(model$edges$values, getLabel)} else {labels <- rep("", nrow(model$edges))}
    
    edges <- data.frame(
      id = rownames(model$edges),
      from=match(model$edges$input, nodes$code),
      to=match(model$edges$output, nodes$code),
      values=model$edges$values,
      label=labels,
      arrows="to",
      stringsAsFactors=FALSE
    ) 
    if (.selections$bbnNames) {labels <- nodes$name} else {labels <- nodes$code}
    
    nodeSpacing <- ifelse(.selections$bbnNames, 600, 150)
    
    palette <- brewer.pal(length(legends), "RdYlGn")
    
    nodes <- data.frame(
      id = rownames(nodes),
      label = labels,
      level = nodes$layer,
      group = nodes$layer,
      color = palette[as.integer(nodes$layer)],
      code = nodes$code,
      stringsAsFactors=FALSE
    ) 
    
    edges <- edges[(abs(edges$values)>=.selections$bbnImpact),]
    
    nodeNet <- nodes[(nodes$code %in% .selections$pressStatus$code[.selections$pressStatus$status %in% c('On')]),]
    
    #save(nodes, edges, nodeNet, file = 'tmp.RData')
    
    if (nrow(nodeNet)>0) {
      #do pressures 
      edgeNet <- edges[edges$from %in% nodeNet$id, ]
      idx = 1
      repeat {
        nodesToAdd <- nodes[nodes$id %in% edgeNet$to, ]
        nodesToAdd <- nodesToAdd[!(nodesToAdd$id %in% nodeNet$id),]
        
        edgesToAdd <- edges[edges$from %in% nodesToAdd$id, ]
        edgesToAdd <- edgesToAdd[!(edgesToAdd$id %in% edgeNet$id),]
        
        idx <- idx + 1
        if ((idx>20) || ((nrow(nodesToAdd)==0) && (nrow(edgesToAdd)==0))) break
        nodeNet <- rbind(nodeNet, nodesToAdd)
        edgeNet <- rbind(edgeNet, edgesToAdd)
        
      } #until finished
    } else edgeNet <- edges
    
    legendDF <- data.frame(
      id = 1:length(legends),
      label = legends,
      color = palette,
      stringsAsFactors = FALSE
    )
    
    visNetwork(nodeNet, edgeNet, width = "100%", main='Bayesian Belief Network', submain=input$modelSelect) %>% 
      visExport() %>%
      visLegend(useGroups=FALSE, addNodes=legendDF) %>%
      visHierarchicalLayout(nodeSpacing=nodeSpacing, direction='LR') %>%
      visOptions(highlightNearest = TRUE) #%>%
      #visInteraction(navigationButtons = TRUE, dragNodes = TRUE, dragView = TRUE, zoomView = TRUE)
  }
  
  output$bbnGraphPlot <- renderVisNetwork({
    makeBbnGraph(modelList[[.selections$model]])
  })
  
  #observe({
  #  visNetworkProxy("bbnGraphPlot") %>%
  #    visStabilize(iterations=10)
  #})
  
  getModelName <- function() {
    paste0('data/', input$modelSelect, '.xlsx')
  }
  
  genPlot <- function(boxPlot, title) {
    if (nrow(boxPlot)>0) {
      
      palette <- brewer.pal(length(legends), "RdYlGn")
      #print(palette)
      
      colours <- palette[as.integer(boxPlot$Group)]
      
      #print(paste('Box plot, colours', nrow(boxPlot), length(colours)))
      #cat(colours)
      xform <- list(categoryorder = "array",
                    categoryarray = boxPlot[,1],
                    zerolinewidth=10)
      # 
      plot_ly(boxPlot, x = boxPlot[,1], y = ~Range,   color = colours,  colors = palette, type = "box") %>%
        layout(xaxis = xform, showlegend=FALSE, title=title)
      
    } 
  }
  
  prepPlot <- function(code="ba", name="Bio-Assemblage") {
    if (!is.null(.likelihoods$p_es)) {
      inScope <- startsWith(.likelihoods$p_es$code, code)
      thisPlot <- .likelihoods$p_es[inScope, c(1,3,4)]
      colnames(thisPlot) <- c(name, "Group", "Range")
      title <- paste(input$modelSelect, name, 'Box Plot')
      genPlot(thisPlot, title)
    }
  }
  
  output$layer1 <- renderPlotly({
    prepPlot("ba", "Bio-Assemblage")
  })
  
  output$layer2 <- renderPlotly({
    prepPlot("op", "Output Processes")
  })
  
  output$layer3 <- renderPlotly({
    prepPlot("es", "Ecosystem Services")
  })
  
  
  export <- function(model) {
    
    #Get the network graph
    l1 <- orca(prepPlot("ba", "Bio-Assemblage"), 'tmp/layer1.png')
    l2 <- orca(prepPlot("op", "Output Processes"),'tmp/layer2.png')
    l3 <- orca(prepPlot("es", "Ecosystem Services"), 'tmp/layer3.png')

    #Save pressure list, confidence levels, node and edge tables in xlsx
    l <- list(
      pressures = .selections$pressStatus, 
      nodes = model$nodes,
      edges = model$edges,
      settings = as.data.frame(cbind(names(.resistanceScores), .resistanceScores), stringsAsFactors=FALSE)
    )
    
    xl <- write.xlsx(l, 'tmp/dataset.xlsx')
    
    print('saving xlsx file export tmp/dataset.xlsx')
   
    zipFile <- zipr(paste0('tmp/MESO-', format(Sys.time(), "%m%d_%H%M"), '.zip'), c('tmp/layer1.png', 'tmp/layer2.png', 'tmp/layer3.png', 'tmp/dataset.xlsx'))
    
    print(paste('zip file complete', zipFile))
    
    return(zipFile)
  }
  
  
  
  
  
  output$linkBackgroundData <- downloadHandler(
    filename = getModelName(),
    content = function(file) {
      file.copy(getModelName(), file)
    },
    contentType = "application/xlsx"
  )
  
  output$download <-downloadHandler(
    filename = paste0('MESO-', format(Sys.time(), "%m%d_%H%M"), '.zip'),
    content = function(file) {
      fName <- export(modelList[[.selections$model]])
      file.copy(fName, file)
    },
    contentType = "application/zip"
  )

  
}

shinyApp(ui, server)