############################################
##  Outlier Analysis - UI
############################################
## 1. 翻译标签
outlier_methods <- c("iqr", "zscore")
names(outlier_methods) <- c(i18n$t("IQR Method (1.5×IQR)"),
                            i18n$t("Z-score Method (±3σ)"))
outlier_plots <- c("boxplot", "histogram", "scatter")
names(outlier_plots) <- c(i18n$t("Boxplot (Mark Outliers)"),
                          i18n$t("Histogram (With Thresholds)"),
                          i18n$t("Scatter Plot (Variable Pairs)"))

## 2. 函数形参
outlier_args <- as.list(formals(outlier))
outlier_args <- outlier_args[names(outlier_args) %in% c("dataset", "vars", "method", "iqr_multiplier", "z_threshold", "data_filter")]

## 3. 输入收集
outlier_inputs <- reactive({
  req(input$dataset)
  inputs <- list(
    dataset = input$dataset,
    vars = input$outlier_vars,  
    method = input$outlier_method,
    iqr_multiplier = input$outlier_iqr_multiplier,
    z_threshold = input$outlier_z_threshold,
    data_filter = if (input$show_filter) input$data_filter else "",
    envir = r_data
  )
  # 校验参数完整性
  for (arg in names(outlier_args)) {
    if (is.null(inputs[[arg]]) || length(inputs[[arg]]) == 0) {
      inputs[[arg]] <- outlier_args[[arg]]
    }
  }
  inputs
})

## 4. 变量选择UI
output$ui_outlier_vars <- renderUI({
  req(input$dataset)
  current_data <- get_data(input$dataset, envir = r_data)
  is_num <- sapply(current_data, function(col) is.numeric(col) || is.ts(col))
  num_vars <- names(is_num)[is_num]
  
  if (length(num_vars) == 0) {
    return(div(class = "alert alert-warning", i18n$t("No numeric variables in dataset. Please select another dataset.")))
  }
  
  # 提取变量类型并显示
  var_types <- sapply(current_data[, num_vars, drop = FALSE], function(col) class(col)[1])
  choices <- setNames(nm = paste0(num_vars, " {", var_types, "}"), object = num_vars)
  
  selectizeInput(
    inputId = "outlier_vars",
    label = i18n$t("Select numeric variable:"),
    choices = choices,
    selected = state_multiple("outlier_vars", num_vars),
    multiple = TRUE,
    options = list(placeholder = i18n$t("Select one or more variables"), plugins = list("remove_button", "drag_drop"))
  )
})

## 5. 方法参数调整UI
output$ui_outlier_params <- renderUI({
  req(input$outlier_method)
  tagList(
    # IQR方法：调整倍数（默认1.5）
    conditionalPanel(
      condition = "input.outlier_method == 'iqr'",
      numericInput(
        inputId = "outlier_iqr_multiplier",
        label = i18n$t("IQR Multiplier:"),
        value = state_init("outlier_iqr_multiplier", 1.5),
        min = 0.5, max = 5, step = 0.5
      )
    ),
    # Z-score方法：调整阈值（默认3）
    conditionalPanel(
      condition = "input.outlier_method == 'zscore'",
      numericInput(
        inputId = "outlier_z_threshold",
        label = i18n$t("Z-score Threshold:"),
        value = state_init("outlier_z_threshold", 3),
        min = 1.5, max = 5, step = 0.5
      )
    )
  )
})

## 6. 主UI
output$ui_outlier <- renderUI({
  req(input$dataset)
  tagList(
    wellPanel(
      # Summary标签页：变量选择+方法选择+参数调整
      conditionalPanel(
        condition = "input.tabs_outlier == 'Summary'",
        uiOutput("ui_outlier_vars"),
        radioButtons(
          inputId = "outlier_method",
          label = i18n$t("Select outlier detection method:"),
          choices = outlier_methods,
          selected = state_single("outlier_method", outlier_methods, "iqr"),
          inline = FALSE
        ),
        uiOutput("ui_outlier_params")  # 动态参数面板
      ),
      # Plot标签页：图表选择
      conditionalPanel(
        condition = "input.tabs_outlier == 'Plot'",
        selectizeInput(
          inputId = "outlier_plots",
          label = i18n$t("Select plots:"),
          choices = outlier_plots,
          selected = state_multiple("outlier_plots", outlier_plots, "boxplot"),
          multiple = TRUE,
          options = list(placeholder = i18n$t("Select plot types"), plugins = list("remove_button", "drag_drop"))
        )
      )
    ),
    # 帮助与报告
    help_and_report(
      modal_title = i18n$t("Outlier Analysis"),
      fun_name = "outlier",
      help_file = inclMD(file.path(getOption("radiant.path.basics"), "app/tools/help/outlier.md"))
    )
  )
})

## 7. 图表尺寸
outlier_plot_dims <- reactive({
  req(.outlier())
  plot_count <- length(input$outlier_plots)
  var_count <- length(.outlier()$vars)  # 选择的变量数
  
  # 每个子图基础高度（像素）
  base_height_px <- 300
  total_height_px <- base_height_px * plot_count * ceiling(var_count / 2)  # 2列布局
  
  # 限制最大/最小高度
  total_height_px <- min(total_height_px, 2500)
  total_height_px <- max(total_height_px, 500)
  
  list(
    width = 800,  # 宽屏适配多变量
    height = total_height_px
  )
})
outlier_plot_width <- function() outlier_plot_dims()$width
outlier_plot_height <- function() outlier_plot_dims()$height

## 8. 输出面板
output$outlier<- renderUI({
  # 注册输出组件
  register_print_output("summary_outlier", ".summary_outlier")
  register_plot_output("plot_outlier", ".plot_outlier",
                       height_fun = "outlier_plot_height")
  
  # 标签页布局
  outlier_panels <- tabsetPanel(
    id = "tabs_outlier",
    tabPanel(
      title = i18n$t("Summary"),
      value = "Summary",
      verbatimTextOutput("summary_outlier", placeholder = TRUE)
    ),
    tabPanel(
      title = i18n$t("Plot"),
      value = "Plot",
      download_link("dlp_outlier"),  # 下载按钮
      plotOutput("plot_outlier", height = "100%"),
      style = "margin-top: 10px;"
    )
  )
  
  stat_tab_panel(
    menu = i18n$t("Basics > Data Quality"),
    tool = i18n$t("Outlier Analysis"),
    tool_ui = "ui_outlier",
    output_panels = outlier_panels
  )
})

## 9. 可用性检验
outlier_available <- reactive({
  req(input$dataset)
  current_data <- get_data(input$dataset, envir = r_data)
  
  # 校验是否选择变量
  if (not_available(input$outlier_vars)) {
    return(i18n$t("Please select at least one numeric variable."))
  }
  
  # 校验变量是否存在且为数值型
  invalid_vars <- input$outlier_vars[!input$outlier_vars %in% colnames(current_data)]
  if (length(invalid_vars) > 0) {
    return(i18n$t(paste("Invalid variables: ", paste(invalid_vars, collapse = ", "), ". Please reselect.", sep = "")))
  }
  
  # 校验变量是否为数值型
  non_num_vars <- input$outlier_vars[!sapply(current_data[, input$outlier_vars, drop = FALSE], is.numeric)]
  if (length(non_num_vars) > 0) {
    return(i18n$t(paste("Non-numeric variables: ", paste(non_num_vars, collapse = ", "), ". Please select numeric variables.", sep = "")))
  }
  
  "available"
})

## 10. 计算核心
.outlier <- reactive({
  req(outlier_available() == "available")
  do.call(outlier, outlier_inputs())
})

## 11. Summary输出
.summary_outlier <- reactive({
  req(outlier_available() == "available")
  summary(.outlier())
})

## 12. Plot输出
.plot_outlier <- reactive({
  req(outlier_available() == "available")
  validate(need(input$outlier_plots, i18n$t("Please select at least one plot type first.")))
  withProgress(message = i18n$t("Generating outlier plots..."), value = 0.5, {
    p <- plot(.outlier(), plots = input$outlier_plots, shiny = TRUE)
    setProgress(value = 1)
  })
  p
})

## 13. 下载与截图
download_handler(
  id = "dlp_outlier",
  fun = function(file) {
    plot_obj <- .plot_outlier()
    width_in <- outlier_plot_width() / 96
    height_in <- outlier_plot_height() / 96
    
    ggsave(
      filename = file,
      plot = plot_obj,
      width = width_in,
      height = height_in,
      device = "png",
      dpi = 300,
      limitsize = FALSE,
      bg = "white"
    )
  },
  fn = function() paste0(input$dataset, "_outlier_analysis"),
  type = "png",
  caption = i18n$t("Save outlier plots")
)

## 14. 报告生成
outlier_report <- function() {
  req(outlier_available() == "available")
  figs <- length(input$outlier_plots) > 0
  update_report(
    inp_main = clean_args(outlier_inputs(), outlier_args),
    fun_name = "outlier",
    inp_out = if (figs) list("", list(plots = input$outlier_plots)) else list(""),
    outputs = if (figs) c("summary", "plot") else "summary",
    figs = figs,
    fig.width = outlier_plot_width(),
    fig.height = outlier_plot_height()
  )
}

## 15. 截图功能
observeEvent(input$outlier_report, {
  r_info[["latest_screenshot"]] <- NULL
  outlier_report()
})
observeEvent(input$outlier_screenshot, {
  r_info[["latest_screenshot"]] <- NULL
  radiant_screenshot_modal("modal_outlier_screenshot")
})
observeEvent(input$modal_outlier_screenshot, {
  outlier_report()
  removeModal()
})