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# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#

library(shiny)
library(shinyjs) #http://deanattali.com/shinyjs/
library(data.table)
library(ggplot2)
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library(gplots) # for heatmap.2
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library(plotly)
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library(d3heatmap) # for interactive heatmap
library(dendextend) # for color_branches
library(RColorBrewer)
library(sparcl) # sparse hierarchical and k-means
library(scales) # for percentages on y scale
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# increase file upload limit
options(shiny.maxRequestSize = 30 * 1024 ^ 2)
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shinyServer(function(input, output, session) {
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  useShinyjs()
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  # This is only set at session start
  # we use this as a way to determine which input was
  # clicked in the dataInBoth reactive
  counter <- reactiveValues(
    # The value of inDataGen1,2 actionButton is the number of times they were pressed
    dataGen1     = isolate(input$inDataGen1),
    dataLoadNuc  = isolate(input$inButLoadNuc)
    #dataLoadStim = isolate(input$inButLoadStim)
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  )
  
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  ####
  ## UI for side panel
  
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  # FILE LOAD
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  # This button will reset the inFileLoad
  observeEvent(input$inButReset, {
    reset("inFileLoadNuc")  # reset is a shinyjs function
    #reset("inButLoadStim")  # reset is a shinyjs function
  })
  
  # generate random dataset 1
  dataGen1 <- eventReactive(input$inDataGen1, {
    cat("dataGen1\n")
    
    return(userDataGen())
  })
  
  # load main data file
  dataLoadNuc <- eventReactive(input$inButLoadNuc, {
    cat("dataLoadNuc\n")
    locFilePath = input$inFileLoadNuc$datapath
    
    counter$dataLoadNuc <- input$inButLoadNuc - 1
    
    if (is.null(locFilePath) || locFilePath == '')
      return(NULL)
    else {
      return(fread(locFilePath))
    }
  })
  
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  # This button will reset the inFileLoad
  observeEvent(input$butReset, {
    reset("inFileLoadNuc")  # reset is a shinyjs function
    #    reset("inFileStimLoad")  # reset is a shinyjs function
    
  })
  
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  # COLUMN SELECTION
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  output$varSelTrackLabel = renderUI({
    cat(file = stderr(), 'UI varSelTrackLabel\n')
    locCols = getDataNucCols()
    locColSel = locCols[locCols %like% 'rack'][1] # index 1 at the end in case more matches; select 1st
    
    cat(locColSel, '\n')
    selectInput(
      'inSelTrackLabel',
      'Select Track Label (e.g. objNuc_Track_ObjectsLabel):',
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  output$varSelTime = renderUI({
    cat(file = stderr(), 'UI varSelTime\n')
    locCols = getDataNucCols()
    locColSel = locCols[locCols %like% 'RealTime'][1] # index 1 at the end in case more matches; select 1st
    
    cat(locColSel, '\n')
    selectInput(
      'inSelTime',
      'Select time column (e.g. RealTime):',
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  # This is main field to select plot facet grouping
  # It's typically a column with the entire experimental description,
  # e.g. in Yannick's case it's Stim_All_Ch or Stim_All_S.
  # In Coralie's case it's a combination of 3 columns called Stimulation_...
  output$varSelGroup = renderUI({
    cat(file = stderr(), 'UI varSelGroup\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
      locColSel = locCols[locCols %like% 'ite']
      if (length(locColSel) == 0)
        locColSel = locCols[locCols %like% 'eries'][1] # index 1 at the end in case more matches; select 1st
      else if (length(locColSel) > 1) {
        locColSel = locColSel[1]
      }
      #    cat('UI varSelGroup::locColSel ', locColSel, '\n')
      selectInput(
        'inSelGroup',
        'Select one or more facet groupings (e.g. Site, Well, Channel):',
        locCols,
        width = '100%',
        selected = locColSel,
        multiple = TRUE
      )
    }
    
  })
  
  output$varSelSite = renderUI({
    cat(file = stderr(), 'UI varSelSite\n')
    locCols = getDataNucCols()
    locColSel = locCols[locCols %like% 'ite'][1] # index 1 at the end in case more matches; select 1st
    
    cat(locColSel, '\n')
    selectInput(
      'inSelSite',
      'Select FOV (e.g. Metadata_Site or Metadata_Series):',
      locCols,
      width = '100%',
      selected = locColSel
    )
  })
  
  
  
  
  output$varSelMeas1 = renderUI({
    cat(file = stderr(), 'UI varSelMeas1\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols)) {
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      locColSel = locCols[locCols %like% 'objCyto_Intensity_MeanIntensity_imErkCor.*' |
                            locCols %like% 'Ratio'][1] # index 1 at the end in case more matches; select 1st
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      #    cat(locColSel, '\n')
      selectInput(
        'inSelMeas1',
        'Select 1st measurement:',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
  
  output$varSelMeas2 = renderUI({
    cat(file = stderr(), 'UI varSelMeas2\n')
    locCols = getDataNucCols()
    
    if (!is.null(locCols) &&
        !(input$inSelMath %in% c('', '1 / '))) {
      locColSel = locCols[locCols %like% 'objNuc_Intensity_MeanIntensity_imErkCor.*'][1] # index 1 at the end in case more matches; select 1st
      #    cat(locColSel, '\n')
      selectInput(
        'inSelMeas2',
        'Select 2nd measurement',
        locCols,
        width = '100%',
        selected = locColSel
      )
    }
  })
  
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  # UI for trimming x-axis (time)
  output$uiSlTimeTrim = renderUI({
    cat(file = stderr(), 'UI uiSlTimeTrim\n')
    
    if (input$chBtimeTrim) {
      locTpts  = getDataTpts()
      
      if(is.null(locTpts))
        return(NULL)
      
      locRTmin = min(locTpts)
      locRTmax = max(locTpts)
      
      sliderInput(
        'slTimeTrim',
        label = 'Time range to include',
        min = locRTmin,
        max = locRTmax,
        value = c(locRTmin, locRTmax),
        step = 1
      )
      
    }
  })
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  # UI for normalization
  
  output$uiChBnorm = renderUI({
    cat(file = stderr(), 'UI uiChBnorm\n')
    
    if (input$chBnorm) {
      radioButtons(
        'rBnormMeth',
        label = 'Select method',
        choices = list('fold-change' = 'mean', 'z-score' = 'z.score')
      )
    }
  })
  
  output$uiSlNorm = renderUI({
    cat(file = stderr(), 'UI uiSlNorm\n')
    
    if (input$chBnorm) {
      locTpts  = getDataTpts()
      
      if(is.null(locTpts))
        return(NULL)
      
      locRTmin = min(locTpts)
      locRTmax = max(locTpts)
      
      sliderInput(
        'slNormRtMinMax',
        label = 'Time range for norm.',
        min = locRTmin,
        max = locRTmax,
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        value = c(locRTmin, 0.1 * locRTmax), 
        step = 1
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      )
    }
  })
  
  output$uiChBnormRobust = renderUI({
    cat(file = stderr(), 'UI uiChBnormRobust\n')
    
    if (input$chBnorm) {
      checkboxInput('chBnormRobust',
                    label = 'Robust stats',
                    FALSE)
    }
  })
  
  output$uiChBnormGroup = renderUI({
    cat(file = stderr(), 'UI uiChBnormGroup\n')
    
    if (input$chBnorm) {
      radioButtons('chBnormGroup',
                  label = 'Normalisation grouping',
                  choices = list('Entire dataset' = 'none', 'Per facet' = 'group', 'Per trajectory (Korean way)' = 'id'))
    }
  })
  
  
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  # UI for removing outliers
  
  output$uiSlOutliers = renderUI({
    cat(file = stderr(), 'UI uiSlOutliers\n')
    
    if (input$chBoutliers) {

      sliderInput(
        'slOutliersPerc',
        label = 'Percentage of middle data',
        min = 90,
        max = 100,
        value = 99, 
        step = 0.1
      )
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    }
  })
  
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  output$uiTxtOutliers = renderUI({
    if (input$chBoutliers) {
      
      p("Total tracks")
      
    }
    
  })
  
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  ####
  ## data processing
  
  # generate random dataset 1
  dataGen1 <- eventReactive(input$inDataGen1, {
    cat("dataGen1\n")
    
    return(userDataGen())
  })
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  dataInBoth <- reactive({
    # Without direct references to inDataGen1,2 and inFileLoad, inDataGen2
    #    does not trigger running this reactive once inDataGen1 is used.
    # This is one of the more nuanced areas of reactive programming in shiny
    #    due to the if else logic, it isn't fetched once inDataGen1 is available
    # The morale is use direct retrieval of inputs to guarantee they are available
    #    for if else logic checks!
    
    locInGen1 = input$inDataGen1
    locInLoadNuc = input$inButLoadNuc
    #locInLoadStim = input$inButLoadStim
    
    cat(
      "dataInBoth\ninGen1: ",
      locInGen1,
      "   prev=",
      isolate(counter$dataGen1),
      "\ninDataNuc: ",
      locInLoadNuc,
      "   prev=",
      isolate(counter$dataLoadNuc),
      # "\ninDataStim: ",
      # locInLoadStim,
      # "   prev=",
      # isolate(counter$dataLoadStim),
      "\n"
    )
    
    # isolate the checks of counter reactiveValues
    # as we set the values in this same reactive
    if (locInGen1 != isolate(counter$dataGen1)) {
      cat("dataInBoth if inDataGen1\n")
      dm = dataGen1()
      # no need to isolate updating the counter reactive values!
      counter$dataGen1 <- locInGen1
    } else if (locInLoadNuc != isolate(counter$dataLoadNuc)) {
      cat("dataInBoth if inDataLoadNuc\n")
      dm = dataLoadNuc()
      # no need to isolate updating the counter reactive values!
      counter$dataLoadNuc <- locInLoadNuc
    } else {
      cat("dataInBoth else\n")
      dm = NULL
    }
    return(dm)
  })
  
  # return column names of the main dt
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  getDataNucCols <- reactive({
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    cat(file = stderr(), 'getDataNucCols: in\n')
    loc.dt = dataInBoth()
    
    if (is.null(loc.dt))
      return(NULL)
    else
      return(colnames(loc.dt))
  })
  
  # return dt with an added column with unique track object label
  dataMod <- reactive({
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    cat(file = stderr(), 'dataMod\n')
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    loc.dt = dataInBoth()
    
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    if (is.null(loc.dt))
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      return(NULL)
    
    loc.dt[, trackObjectsLabelUni := paste(sprintf("%03d", get(input$inSelSite)),
                                           sprintf("%04d", get(input$inSelTrackLabel)),
                                           sep = "_")]
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    return(loc.dt)
  })
  
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  # return all unique track object labels (created in dataMod)
  # This will be used to display in UI for trajectory highlighting
  getDataTrackObjLabUni <- reactive({
    cat(file = stderr(), 'getDataTrackObjLabUni\n')
    loc.dt = dataMod()
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    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$trackObjectsLabelUni))
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  })
  
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  # return all unique track object labels (created in dataMod)
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  # This will be used to display in UI for trajectory highlighting
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  getDataTrackObjLabUni_afterTrim <- reactive({
    cat(file = stderr(), 'getDataTrackObjLabUni_afterTrim\n')
    loc.dt = data4trajPlot()
    
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt$id))
  })

  # return all unique time points (real time)
  # This will be used to display in UI for box-plot
  # These timepoints are from the original dt and aren't affected by trimming of x-axis
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  getDataTpts <- reactive({
    cat(file = stderr(), 'getDataTpts\n')
    loc.dt = dataMod()
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    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[[input$inSelTime]]))
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  })
  
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  # return dt with cell IDs and their corresponding condition name
  # The condition is the column defined by facet groupings
  getDataCond <- reactive({
    cat(file = stderr(), 'getDataCond\n')
    loc.dt = data4trajPlot()
    
    if (is.null(loc.dt))
      return(NULL)
    else
      return(unique(loc.dt[, .(id, group)]))
    
  })
  
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  # prepare data for plotting time courses
  # returns dt with these columns:
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  #    realtime - selected from input
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  #    y        - measurement selected from input
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  #               (can be a single column or result of an operation on two cols)
  #    id       - trackObjectsLabelUni (created in dataMod)
  #    group    - grouping variable for facetting from input
  #    mid.in   - column with trajectory selection status from the input file or
  #               highlight status from UI
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  data4trajPlot <- reactive({
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    cat(file = stderr(), 'data4trajPlot\n')
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    loc.dt = dataMod()
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    if (is.null(loc.dt))
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      return(NULL)
    
    
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    if (input$inSelMath == '')
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      loc.s.y = input$inSelMeas1
    else if (input$inSelMath == '1 / ')
      loc.s.y = paste0(input$inSelMath, input$inSelMeas1)
    else
      loc.s.y = paste0(input$inSelMeas1, input$inSelMath, input$inSelMeas2)
    
    # create expression for parsing
    # creates a merged column based on other columns from input
    # used for grouping of plot facets
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    if(length(input$inSelGroup) == 0)
      return(NULL)
    loc.s.gr = sprintf("paste(%s, sep=';')",
                       paste(input$inSelGroup, sep = '', collapse = ','))
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    loc.s.rt = input$inSelTime
    
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    # Assign tracks selected for highlighting in UI
    loc.tracks.highlight = input$inSelHighlight
    locBut = input$chBhighlightTraj
    
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    # if dataset contains column mid.in with trajectory filtering status,
    # then, include it in plotting
    if (sum(names(loc.dt) %in% 'mid.in') > 0) {
      loc.out = loc.dt[, .(
        y = eval(parse(text = loc.s.y)),
        id = trackObjectsLabelUni,
        group = eval(parse(text = loc.s.gr)),
        realtime = eval(parse(text = loc.s.rt)),
        mid.in = mid.in
      )]
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      # add 3rd level with status of track selection
      # to a column with trajectory filtering status
      if (locBut) {
        loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', mid.in)]
      }
      
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    } else {
      loc.out = loc.dt[, .(
        y = eval(parse(text = loc.s.y)),
        id = trackObjectsLabelUni,
        group = eval(parse(text = loc.s.gr)),
        realtime = eval(parse(text = loc.s.rt))
      )]
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      # add a column with status of track selection
      if (locBut) {
        loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', 'NOT SEL')]
      }
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    }
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    # remove NAs
    loc.out = loc.out[complete.cases(loc.out)]

    # Trim x-axis (time)
    if(input$chBtimeTrim) {
      loc.out = loc.out[realtime >= input$slTimeTrim[[1]] & realtime <= input$slTimeTrim[[2]] ]
    }
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    # Normalization
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    # F-n myNorm adds additional column with .norm suffix
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    if (input$chBnorm) {
      loc.out = myNorm(
        in.dt = loc.out,
        in.meas.col = 'y',
        in.rt.col = 'realtime',
        in.rt.min = input$slNormRtMinMax[1],
        in.rt.max = input$slNormRtMinMax[2],
        in.type = input$rBnormMeth,
        in.robust = input$chBnormRobust,
        in.by.cols = if(input$chBnormGroup %in% 'none') NULL else input$chBnormGroup
      )
      
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      # Column with normalized data is renamed to the original name
      # Further code assumes column name y produced by data4trajPlot
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      loc.out[, y := NULL]
      setnames(loc.out, 'y.norm', 'y')
    }
    
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    ##### MOD HERE
    ## display number of filtered tracks in textUI: uiTxtOutliers
    ## How? 
    ## 1. through reactive values?
    ## 2. through additional comumn to tag outliers?
    
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    # Remove outliers
    # 1. Scale all points (independently per track)
    # 2. Pick time points that exceed the bounds
    # 3. Identify IDs of outliers
    # 4. Select cells that don't have these IDs
    
    cat('Ncells orig = ', length(unique(loc.out$id)), '\n')
    
    if (input$chBoutliers) {
      loc.out[, y.sc := scale(y)]  
      loc.tmp = loc.out[ y.sc < quantile(y.sc, (1 - input$slOutliersPerc * 0.01)*0.5) | 
                           y.sc > quantile(y.sc, 1 - (1 - input$slOutliersPerc * 0.01)*0.5)]
      loc.out = loc.out[!(id %in% unique(loc.tmp$id))]
      loc.out[, y.sc := NULL]
    }
    
    cat('Ncells trim = ', length(unique(loc.out$id)), '\n')

    return(loc.out)
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  })
  
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  # prepare data for plotting boxplots
  # uses the same dt as for trajectory plotting
  # returns dt with these columns:
  data4boxPlot <- reactive({
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    cat(file = stderr(), 'data4boxPlot\n')
    
    loc.dt = data4trajPlot()
    if (is.null(loc.dt))
      return(NULL)
    
    loc.out = loc.dt[realtime %in% input$inSelTpts]
  })
  
  
  # prepare data for clustering
  # return a matrix with:
  # cells as columns
  # time points as rows
  data4clust <- reactive({
    cat(file = stderr(), 'data4clust\n')
    
    loc.dt = data4trajPlot()
    if (is.null(loc.dt))
      return(NULL)
    
    loc.out = dcast(loc.dt, id ~ realtime, value.var = 'y')
    loc.rownames = loc.out$id
    

    loc.out = as.matrix(loc.out[, -1])
    rownames(loc.out) = loc.rownames
    return(loc.out)
  })
  
  # prepare data for plotting timecourses facetted per cluster
  # uses the same dt as for trajectory plotting
  # returns dt with these columns:
  data4hierSparTrajPlot <- reactive({
    cat(file = stderr(), 'data4hierSparTrajPlot\n')
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    loc.dt = data4trajPlot()
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    if (is.null(loc.dt))
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      return(NULL)
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    loc.out = loc.dt[realtime %in% input$inSelTpts]
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  })
  
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  # get cell IDs with cluster assignments depending on dendrogram cut
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  getDataCl = function(in.dend, in.k, in.ids) {
    cat(file = stderr(), 'getDataCl \n')
    cat(in.k, '\n')
    loc.dt.cl = data.table(id = in.ids,
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                           cl = cutree(as.dendrogram(in.dend), k = in.k))
  }
  

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  getDataHierClReact = reactive({
    cat(file = stderr(), 'getDataHierClReact \n')
    cat(input$inPlotHierNclust, '\n')
    loc.dt.cl = data.table(id = getDataTrackObjLabUni_afterTrim(),
                           cl = cutree(userFitDendHier(), k = input$inPlotHierNclust))
    
    loc.dt.cl = merge(loc.dt.cl, getDataCond(), by = 'id')
  })
  
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  ####
  ## UI for trajectory plot
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  output$varSelHighlight = renderUI({
    cat(file = stderr(), 'UI varSelHighlight\n')
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    locBut = input$chBhighlightTraj
    if (!locBut)
      return(NULL)
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    loc.v = getDataTrackObjLabUni()
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    if (!is.null(loc.v)) {
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      selectInput(
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        'inSelHighlight',
        'Select one or more rajectories:',
        loc.v,
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        width = '100%',
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        multiple = TRUE
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      )
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    }
  })
  
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  output$uiPlotTraj = renderUI({
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    plotlyOutput(
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      "plotTrajPlotly",
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      width = paste0(input$inPlotTrajWidth, '%'),
      height = paste0(input$inPlotTrajHeight, 'px')
    )
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  })
  
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  output$plotTrajPlotly <- renderPlotly({
    # This is required to avoid
    # "Warning: Error in <Anonymous>: cannot open file 'Rplots.pdf'"
    # When running on a server. Based on:
    # https://github.com/ropensci/plotly/issues/494
    if (names(dev.cur()) != "null device")
      dev.off()
    pdf(NULL)
    
    loc.p = plotTraj()
    if(is.null(loc.p))
      return(NULL)
    
    return(plotly_build(loc.p))
  })
  
  # Trajectory plot - download pdf
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  callModule(downPlot, "downPlotTraj", 'tcourses.pdf', plotTraj, TRUE)
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  plotTraj <- function() {
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    cat(file = stderr(), 'plotTraj: in\n')
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    locBut = input$butPlotTraj
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    if (locBut == 0) {
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      cat(file = stderr(), 'plotTraj: Go button not pressed\n')
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      return(NULL)
    }
    
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    loc.dt = isolate(data4trajPlot())
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    cat("plotTraj: on to plot\n\n")
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    if (is.null(loc.dt)) {
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      cat(file = stderr(), 'plotTraj: dt is NULL\n')
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      return(NULL)
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    }
    
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    cat(file = stderr(), 'plotTraj: dt not NULL\n')
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    # Future: change such that a column with colouring status is chosen by the user
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    # colour trajectories, if dataset contains mi.din column
    # with filtering status of trajectory
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    if (sum(names(loc.dt) %in% 'mid.in') > 0)
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      loc.line.col.arg = 'mid.in'
    else
      loc.line.col.arg = NULL
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    p.out = myGgplotTraj(
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      dt.arg = loc.dt,
      x.arg = 'realtime',
      y.arg = 'y',
      group.arg = "id",
      facet.arg = 'group',
      facet.ncol.arg = input$inPlotTrajFacetNcol,
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      xlab.arg = 'Time (min)',
      line.col.arg = loc.line.col.arg
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    )
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    return(p.out)
  }
  
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  ####
  ## UI for box-plot
  
  output$varSelTpts = renderUI({
    cat(file = stderr(), 'UI varSelTpts\n')
    
    loc.v = getDataTpts()
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    if (!is.null(loc.v)) {
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      selectInput(
        'inSelTpts',
        'Select one or more timepoints:',
        loc.v,
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        width = '100%',
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        selected = 0,
        multiple = TRUE
      )
    }
  })
  
  # Boxplot - display
  output$outPlotBox = renderPlot({
    locBut = input$butPlotBox
    
    if (locBut == 0) {
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      cat(file = stderr(), 'plotBox: Go button not pressed\n')
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      return(NULL)
    }
    
    plotBox()
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  }, height = 800)
  
  # Boxplot - download pdf
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  callModule(downPlot, "downPlotBox", 'boxplot.pdf', plotBox, TRUE)

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  # Function instead of reactive as per:
  # http://stackoverflow.com/questions/26764481/downloading-png-from-shiny-r
  # This function is used to plot and to downoad a pdf
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  plotBox <- function() {
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    cat(file = stderr(), 'plotBox\n')
    
    loc.dt = data4boxPlot()
    
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    cat(file = stderr(), "plotBox: on to plot\n\n")
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    if (is.null(loc.dt)) {
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      cat(file = stderr(), 'plotBox: dt is NULL\n')
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      return(NULL)
    }
    
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    cat(file = stderr(), 'plotBox:dt not NULL\n')

    
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    ggplot(loc.dt, aes(x = as.factor(realtime), y = y)) +
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      geom_boxplot(
        aes(fill = group),
        #position = position_dodge(width = 1),
        notch = input$inPlotBoxNotches,
        outlier.colour = if(input$inPlotBoxOutliers) 'red' else NA
      ) +
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      scale_fill_discrete(name = '') +
      xlab('\nTime (min)') +
      ylab('') +
      theme_bw(base_size = 18, base_family = "Helvetica") +
      theme(
        panel.grid.minor = element_blank(),
        panel.grid.major = element_blank(),
        panel.border = element_blank(),
        axis.line.x = element_line(color = "black", size = 0.25),
        axis.line.y = element_line(color = "black", size = 0.25),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        strip.text.x = element_text(size = 14, face = "bold"),
        strip.text.y = element_text(size = 14, face = "bold"),
        strip.background = element_blank(),
        legend.key = element_blank(),
        legend.key.height = unit(1, "lines"),
        legend.key.width = unit(2, "lines"),
        legend.position = input$selPlotBoxLegendPos
      )
  }
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  ###### Scatter plot
  callModule(tabScatterPlot, 'tabScatter', data4trajPlot)
  
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  ##### Hierarchical clustering
  
  output$uiPlotHierClSel = renderUI({
    if(input$chBPlotHierClSel) {
      selectInput('inPlotHierClSel', 'Select clusters to display', 
                  choices = seq(1, input$inPlotHierNclust, 1),
                  multiple = TRUE, 
                  selected = 1)
    }
  })
  
  userFitDendHier <- reactive({
    dm.t = data4clust()
    if (is.null(dm.t)) {
      return()
    }
    
    cl.dist = dist(dm.t, method = s.cl.diss[as.numeric(input$selectPlotHierDiss)])
    cl.hc = hclust(cl.dist, method = s.cl.linkage[as.numeric(input$selectPlotHierLinkage)])
    cl.lev = rev(row.names(dm.t))
    
    dend <- as.dendrogram(cl.hc)
    dend <- color_branches(dend, k = input$inPlotHierNclust)
    
    return(dend)
  })
  
  # Function instead of reactive as per:
  # http://stackoverflow.com/questions/26764481/downloading-png-from-shiny-r
  # This function is used to plot and to downoad a pdf
  plotHier <- function() {
    
    loc.dm = data4clust()
    if (is.null(loc.dm))
      return(NULL)
    
    loc.dend <- userFitDendHier()
    if (is.null(loc.dend))
      return(NULL)
    
    if (input$inPlotHierRevPalette)
      my_palette <-
      rev(colorRampPalette(brewer.pal(9, input$selectPlotHierPalette))(n = 99))
    else
      my_palette <-
      colorRampPalette(brewer.pal(9, input$selectPlotHierPalette))(n = 99)
    
    
    col_labels <- get_leaves_branches_col(loc.dend)
    col_labels <- col_labels[order(order.dendrogram(loc.dend))]
    
    if (input$selectPlotHierDend) {
      assign("var.tmp.1", loc.dend)
      var.tmp.2 = "row"
    } else {
      assign("var.tmp.1", FALSE)
      var.tmp.2 = "none"
    }
    
    loc.p = heatmap.2(
      loc.dm,
      Colv = "NA",
      Rowv = var.tmp.1,
      srtCol = 90,
      dendrogram = var.tmp.2,
      trace = "none",
      key = input$selectPlotHierKey,
      margins = c(input$inPlotHierMarginX, input$inPlotHierMarginY),
      col = my_palette,
      na.col = grey(input$inPlotHierNAcolor),
      denscol = "black",
      density.info = "density",
      RowSideColors = col_labels,
      colRow = col_labels,
#      sepcolor = grey(input$inPlotHierGridColor),
#      colsep = 1:ncol(loc.dm),
#      rowsep = 1:nrow(loc.dm),
      cexRow = input$inPlotHierFontX,
      cexCol = input$inPlotHierFontY,
      main = paste(
        "Distance measure: ",
        s.cl.diss[as.numeric(input$selectPlotHierDiss)],
        "\nLinkage method: ",
        s.cl.linkage[as.numeric(input$selectPlotHierLinkage)]
      )
    )
    
    return(loc.p)
  }
  
  
  plotHierTraj <- function(){
    cat(file = stderr(), 'plotHierTraj: in\n')
    
    loc.dt = isolate(data4trajPlot())
    
    cat("plotHierTraj: on to plot\n\n")
    if (is.null(loc.dt)) {
      cat(file = stderr(), 'plotHierTraj: dt is NULL\n')
      return(NULL)
    }
    
    cat(file = stderr(), 'plotHierTraj: dt not NULL\n')
    
    # get cellIDs with cluster assignments based on dendrogram cut
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    loc.dt.cl = getDataCl(userFitDendHier(), input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim())
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    loc.dt = merge(loc.dt, loc.dt.cl, by = 'id')
    
    # display only selected clusters
    if(isolate(input$chBPlotHierClSel))
      loc.dt = loc.dt[cl %in% isolate(input$inPlotHierClSel)]
    
    # Future: change such that a column with colouring status is chosen by the user
    # colour trajectories, if dataset contains mi.din column
    # with filtering status of trajectory
    if (sum(names(loc.dt) %in% 'mid.in') > 0)
      loc.line.col.arg = 'mid.in'
    else
      loc.line.col.arg = NULL
    
    p.out = myGgplotTraj(
      dt.arg = loc.dt,
      x.arg = 'realtime',
      y.arg = 'y',
      group.arg = "id",
      facet.arg = 'cl',
      facet.ncol.arg = input$inPlotTrajFacetNcol,
      xlab.arg = 'Time (min)',
      line.col.arg = loc.line.col.arg
    )
    
    return(p.out)
  }
  
  
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  # download a list of cellIDs with cluster assihnments
  output$downCellCl <- downloadHandler(
    filename = function() {
      paste0('clust_hierch_data_',
             s.cl.diss[as.numeric(input$selectPlotHierDiss)],
             '_',
             s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.csv')
    },
    
    content = function(file) {
      write.csv(x = getDataCl(userFitDendHier(), input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim()), file = file, row.names = FALSE)
    }
  )
  
  output$downCellClSpar <- downloadHandler(
    filename = function() {
      paste0('clust_hierchSpar_data_',
             s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
             '_',
             s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.csv')
    },
    
    content = function(file) {
      write.csv(x = getDataCl(userFitDendHierSpar(), input$inPlotHierSparNclust, getDataTrackObjLabUni_afterTrim()), file = file, row.names = FALSE)
    }
  )
  
  
    # callModule(downCellCl, 'downDataHier', paste0('clust_hierch_data_',
    #                                               s.cl.diss[as.numeric(input$selectPlotHierDiss)],
    #                                               '_',
    #                                               s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.csv'),
    #            getDataCl(userFitDendHier, input$inPlotHierNclust, getDataTrackObjLabUni_afterTrim))
    # 
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    output$downloadDataClean <- downloadHandler(
      filename = 'tCoursesSelected_clean.csv',
      content = function(file) {
        write.csv(data4trajPlot(), file, row.names = FALSE)
      }
    )
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  # Barplot with distribution of clusters across conditions
  plotHierClDist = function() {
    cat(file = stderr(), 'plotClDist: in\n')
    
    # get cell IDs with cluster assignments depending on dendrogram cut
    loc.dend <- isolate(userFitDendHier())
    if (is.null(loc.dend)) {
      cat(file = stderr(), 'plotClDist: loc.dend is NULL\n')
      return(NULL)
    }
    
    loc.dt.cl = data.table(id = getDataTrackObjLabUni_afterTrim(),
                           cl = cutree(as.dendrogram(loc.dend), k = input$inPlotHierNclust))
    
    
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    # get cellIDs with condition name
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    loc.dt.gr = isolate(getDataCond())
    if (is.null(loc.dt.gr)) {
      cat(file = stderr(), 'plotClDist: loc.dt.gr is NULL\n')
      return(NULL)
    }
    
    loc.dt = merge(loc.dt.cl, loc.dt.gr, by = 'id')
    
    # display only selected clusters
    if(isolate(input$chBPlotHierClSel))
      loc.dt = loc.dt[cl %in% isolate(input$inPlotHierClSel)]
    
    loc.dt.aggr = loc.dt[, .(nCells = .N), by = .(group, cl)]
    
    
    p.out = ggplot(loc.dt.aggr, aes(x = group, y = nCells)) +
      geom_bar(aes(fill = as.factor(cl)), stat = 'identity', position = 'fill') +
      scale_y_continuous(labels = percent) +
      ylab("percentage of cells\n") +  
      xlab("") +  
      scale_fill_discrete(name = "Cluster no.") +
      myGgplotTheme
    
    return(p.out)
    
  }
  
  #  Hierarchical - display heatmap
  getPlotHierHeatMapHeight <- function() {
    return (input$inPlotHierHeatMapHeight)
  }
  
  getPlotHierTrajHeight <- function() {
    return (input$inPlotHierTrajHeight)
  }
  
  output$outPlotHier <- renderPlot({
    locBut = input$butPlotHierHeatMap
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotHier: Go button not pressed\n')
      
      return(NULL)
    }
    
    plotHier()
  }, height = getPlotHierHeatMapHeight)
  
  #  Hierarchical - display timecourses plot
  output$outPlotHierTraj <- renderPlot({
    locBut = input$butPlotHierTraj
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotHierTraj: Go button not pressed\n')
      
      return(NULL)
    }
    
    plotHierTraj()
  })
  
  #  Hierarchical - display bar plot
  output$outPlotHierClDist <- renderPlot({
    locBut = input$butPlotHierClDist
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotClDist: Go button not pressed\n')
      
      return(NULL)
    }
    
    plotHierClDist()
  })
  
  
  
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  #  Hierarchical - Heat Map - download pdf
  callModule(downPlot, "downPlotHier",       paste0('clust_hierch_heatMap_',
                                                    s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                                                    '_',
                                                    s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.pdf'), plotHier)

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  # Hierarchical - Trajectories - download pdf
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  callModule(downPlot, "downPlotHierTraj",       paste0('clust_hierch_tCourses_',
                                                    s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                                                    '_',
                                                    s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.pdf'), plotHierTraj, TRUE)

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  # Hierarchical - Bar Plot - download pdf
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  callModule(downPlot, "downPlotHierClDist",       paste0('clust_hierch_clDist_',
                                                        s.cl.diss[as.numeric(input$selectPlotHierDiss)],
                                                        '_',
                                                        s.cl.linkage[as.numeric(input$selectPlotHierLinkage)], '.pdf'), plotHierClDist, TRUE)
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  ##### Sparse hierarchical clustering using sparcl
  
  # UI for advanced options
  output$uiPlotHierSparNperms = renderUI({
    if (input$inHierSparAdv)
      sliderInput(
        'inPlotHierSparNperms',
        'Number of permutations',
        min = 1,
        max = 20,
        value = 1,
        step = 1,
        ticks = TRUE
      )
  })
  
  # UI for advanced options
  output$uiPlotHierSparNiter = renderUI({
    if (input$inHierSparAdv)
      sliderInput(
        'inPlotHierSparNiter',
        'Number of iterations',
        min = 1,
        max = 50,
        value = 1,
        step = 1,
        ticks = TRUE
      )
  })
  
  output$uiPlotHierSparClSel = renderUI({
    if(input$chBPlotHierSparClSel) {
      selectInput('inPlotHierSparClSel', 'Select clusters to display', 
                  choices = seq(1, input$inPlotHierSparNclust, 1),
                  multiple = TRUE, 
                  selected = 1)
    }
  })
  

  getPlotHierSparHeatMapHeight <- function() {
    return (input$inPlotHierSparHeatMapHeight)
  }
  
  userFitHierSpar <- reactive({
    dm.t = data4clust()
    if (is.null(dm.t)) {
      return()
    }
    
    #cat('rownames: ', rownames(dm.t), '\n')
    
    perm.out <- HierarchicalSparseCluster.permute(
      dm.t,
      wbounds = NULL,
      nperms = ifelse(input$inHierSparAdv, input$inPlotHierSparNperms, 1),
      dissimilarity = s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)]
    )
    
    sparsehc <- HierarchicalSparseCluster(
      dists = perm.out$dists,
      wbound = perm.out$bestw,
      niter = ifelse(input$inHierSparAdv, input$inPlotHierSparNiter, 1),
      method = s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)],
      dissimilarity = s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)]
    )
    return(sparsehc)
  })
  
  
  userFitDendHierSpar <- reactive({
    sparsehc = userFitHierSpar()
    if (is.null(sparsehc)) {
      return()
    }
    
    dend <- as.dendrogram(sparsehc$hc)
    dend <- color_branches(dend, k = input$inPlotHierSparNclust)
    
    return(dend)
  })
  
  # Function instead of reactive as per:
  # http://stackoverflow.com/questions/26764481/downloading-png-from-shiny-r
  # This function is used to plot and to downoad a pdf
  plotHierSpar <- function() {
    
    dm.t = data4clust()
    if (is.null(dm.t)) {
      return()
    }
    
    sparsehc <- userFitHierSpar()
    
    dend <- as.dendrogram(sparsehc$hc)
    dend <- color_branches(dend, k = input$inPlotHierSparNclust)
    
    if (input$inPlotHierSparRevPalette)
      my_palette <-
      rev(colorRampPalette(brewer.pal(9, input$selectPlotHierSparPalette))(n = 99))
    else
      my_palette <-
      colorRampPalette(brewer.pal(9, input$selectPlotHierSparPalette))(n = 99)
    
    
    col_labels <- get_leaves_branches_col(dend)
    col_labels <- col_labels[order(order.dendrogram(dend))]
    
    if (input$selectPlotHierSparDend == 1)
      assign("var.tmp", dend)
    else
      assign("var.tmp", FALSE)
    
    
    loc.colnames = paste0(ifelse(sparsehc$ws == 0, "",
                                 ifelse(
                                   sparsehc$ws <= 0.1,
                                   "* ",
                                   ifelse(sparsehc$ws <= 0.5, "** ", "*** ")
                                 )),  colnames(dm.t))
    
    loc.colcol   = ifelse(sparsehc$ws == 0,
                          "black",
                          ifelse(
                            sparsehc$ws <= 0.1,
                            "blue",
                            ifelse(sparsehc$ws <= 0.5, "green", "red")
                          ))
    
    
    loc.p = heatmap.2(
      dm.t,
      Colv = "NA",
      Rowv = var.tmp,
      srtCol = 90,
      dendrogram = ifelse(input$selectPlotHierSparDend == 1, "row", 'none'),
      trace = "none",
      key = input$selectPlotHierSparKey,
      margins = c(
        input$inPlotHierSparMarginX,
        input$inPlotHierSparMarginY
      ),
      col = my_palette,
      na.col = grey(input$inPlotHierSparNAcolor),
      denscol = "black",
      density.info = "density",
      RowSideColors = col_labels,
      colRow = col_labels,
      colCol = loc.colcol,
      labCol = loc.colnames,
#      sepcolor = grey(input$inPlotHierSparGridColor),
#      colsep = 1:ncol(dm.t),
#      rowsep = 1:nrow(dm.t),
      cexRow = input$inPlotHierSparFontX,
      cexCol = input$inPlotHierSparFontY,
      main = paste("Linkage method: ", s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)])
    )
    
    return(loc.p)
  }
  
  
  plotHierSparTraj <- function(){
    cat(file = stderr(), 'plotHierSparTraj: in\n')

    loc.dt = isolate(data4trajPlot())
    
    cat("plotHierSparTraj: on to plot\n\n")
    if (is.null(loc.dt)) {
      cat(file = stderr(), 'plotHierSparTraj: dt is NULL\n')
      return(NULL)
    }
    
    cat(file = stderr(), 'plotHierSparTraj: dt not NULL\n')
    
    # get cellIDs with cluster assignments based on dendrogram cut
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    loc.dt.cl = getDataCl(userFitDendHierSpar(), isolate(input$inPlotHierSparNclust), getDataTrackObjLabUni_afterTrim())
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    loc.dt = merge(loc.dt, loc.dt.cl, by = 'id')
    
    # plot only selected clusters
    if(isolate(input$chBPlotHierSparClSel))
      loc.dt = loc.dt[cl %in% isolate(input$inPlotHierSparClSel)]
    
    
    # Future: change such that a column with colouring status is chosen by the user
    # colour trajectories, if dataset contains mi.din column
    # with filtering status of trajectory
    if (sum(names(loc.dt) %in% 'mid.in') > 0)
      loc.line.col.arg = 'mid.in'
    else
      loc.line.col.arg = NULL
    
    p.out = myGgplotTraj(
      dt.arg = loc.dt,
      x.arg = 'realtime',
      y.arg = 'y',
      group.arg = "id",
      facet.arg = 'cl',
      facet.ncol.arg = input$inPlotTrajFacetNcol,
      xlab.arg = 'Time (min)',
      line.col.arg = loc.line.col.arg
    )
    
    return(p.out)
  }
  
  
  # Barplot with distribution of clusters across conditions
  plotHierSparClDist = function() {
    cat(file = stderr(), 'plotHierSparClDist: in\n')
    
    # get cell IDs with cluster assignments depending on dendrogram cut
    sparsehc <- isolate(userFitHierSpar())
    if (is.null(sparsehc)) {
      cat(file = stderr(), 'plotHierSparClDist: sparsehc is NULL\n')
      return(NULL)
    }
    
    loc.dt.cl = data.table(id = getDataTrackObjLabUni_afterTrim(),
                           cl = cutree(as.dendrogram(sparsehc$hc), k = input$inPlotHierSparNclust))
    
    
    loc.dt.gr = isolate(getDataCond())
    if (is.null(loc.dt.gr)) {
      cat(file = stderr(), 'plotHierSparClDist: loc.dt.gr is NULL\n')
      return(NULL)
    }
    
    loc.dt = merge(loc.dt.cl, loc.dt.gr, by = 'id')
    
    # plot only selected clusters
    if(isolate(input$chBPlotHierSparClSel))
      loc.dt = loc.dt[cl %in% isolate(input$inPlotHierSparClSel)]
    
    loc.dt.aggr = loc.dt[, .(nCells = .N), by = .(group, cl)]
    
    p.out = ggplot(loc.dt.aggr, aes(x = group, y = nCells)) +
      geom_bar(aes(fill = as.factor(cl)), stat = 'identity', position = 'fill') +
      scale_y_continuous(labels = percent) +
      ylab("percentage of cells\n") +  
      xlab("") +  
      scale_fill_discrete(name = "Cluster no.") +
      myGgplotTheme
    
    return(p.out)
    
  }

  # Sparse Hierarchical - display heatmap
  output$outPlotHierSpar <- renderPlot({
    locBut = input$butPlotHierSparHeatMap
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotHierSpar: Go button not pressed\n')
      
      return(NULL)
    }
    
    plotHierSpar()
  }, height = getPlotHierSparHeatMapHeight)
  
  # Sparse Hierarchical - display timecourses plot
  output$outPlotHierSparTraj <- renderPlot({
    locBut = input$butPlotHierSparTraj
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotHierSparTraj: Go button not pressed\n')
      
      return(NULL)
    }
    
    plotHierSparTraj()
  })
  
  # Sparse Hierarchical - display timecourses plot
  output$outPlotHierSparClDist <- renderPlot({
    locBut = input$butPlotHierSparClDist
    
    if (locBut == 0) {
      cat(file = stderr(), 'outPlotHierSparClDist: Go button not pressed\n')
      
      return(NULL)
    }
    
    plotHierSparClDist()
  })
  
  
  # Sparse Hierarchical - Heat Map - download pdf
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  callModule(downPlot, "downPlotHierSparHM",       paste0('clust_hierchSparse_heatMap_',
                                                    s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
                                                    '_',
                                                    s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.pdf'), plotHierSpar)
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  # Sparse Hierarchical - Trajectories - download pdf
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  callModule(downPlot, "downPlotHierSparTraj",       paste0('clust_hierchSparse_tCourses_',
                                                        s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
                                                        '_',
                                                        s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.pdf'), plotHierSparTraj, TRUE)
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  # Sparse Hierarchical - Bar Plot - download pdf
  callModule(downPlot, "downPlotHierSparClDist",       paste0('clust_hierchSparse_clDist_',
                                                          s.cl.spar.diss[as.numeric(input$selectPlotHierSparDiss)],
                                                          '_',
                                                          s.cl.spar.linkage[as.numeric(input$selectPlotHierSparLinkage)], '.pdf'), plotHierSparClDist, TRUE)
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  # Sparse Hierarchical clustering (sparcl) interactive version
  output$plotHierSparInt <- renderD3heatmap({
    dm.t = data4clust()
    if (is.null(dm.t)) {
      return()
    }
    
    sparsehc <- userFitHierSpar()
    
    dend <- as.dendrogram(sparsehc$hc)
    dend <- color_branches(dend, k = input$inPlotHierSparNclust)
    
    if (input$inPlotHierSparRevPalette)
      my_palette <-
      rev(colorRampPalette(brewer.pal(9, input$selectPlotHierSparPalette))(n = 99))
    else
      my_palette <-
      colorRampPalette(brewer.pal(9, input$selectPlotHierSparPalette))(n = 99)
    
    
    col_labels <- get_leaves_branches_col(dend)
    col_labels <- col_labels[order(order.dendrogram(dend))]
    
    if (input$selectPlotHierSparDend == 1)
      assign("var.tmp", dend)
    else
      assign("var.tmp", FALSE)
    
    
    loc.colnames = paste0(colnames(dm.t), ifelse(sparsehc$ws == 0, "",
                                                 ifelse(
                                                   sparsehc$ws <= 0.1,
                                                   " *",
                                                   ifelse(sparsehc$ws <= 0.5, " **", " ***")
                                                 )))
    
    d3heatmap(
      dm.t,
      Rowv = var.tmp,
      dendrogram = ifelse(input$selectPlotHierSparDend == 1, "row", 'none'),
      trace = "none",
      revC = FALSE,
      na.rm = FALSE,
      margins = c(
        input$inPlotHierSparMarginX * 10,
        input$inPlotHierSparMarginY * 10
      ),
      colors = my_palette,
      na.col = grey(input$inPlotHierSparNAcolor),
      cexRow = input$inPlotHierSparFontY,
      cexCol = input$inPlotHierSparFontX,
      xaxis_height = input$inPlotHierSparMarginX * 10,
      yaxis_width = input$inPlotHierSparMarginY * 10,
      show_grid = TRUE,
      #labRow = rownames(dm.t),
      labCol = loc.colnames
    )
  })
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  callModule(clustBay, 'TabClustBay', data4clust)
  
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})