#' @tool_code ST_ANOVA_ONE
#' @name 单因素方差分析
#' @version 1.0.0
#' @description 三组及以上独立样本的均值差异比较（含事后多重比较）
#' @author SSA-Pro Team

library(glue)
library(ggplot2)
library(base64enc)

run_analysis <- function(input) {
  # ===== 初始化 =====
  logs <- c()
  log_add <- function(msg) { logs <<- c(logs, paste0("[", Sys.time(), "] ", msg)) }

  on.exit({}, add = TRUE)

  # ===== 数据加载 =====
  log_add("开始加载输入数据")
  df <- tryCatch(
    load_input_data(input),
    error = function(e) {
      log_add(paste("数据加载失败:", e$message))
      return(NULL)
    }
  )

  if (is.null(df)) {
    return(make_error(ERROR_CODES$E100_INTERNAL_ERROR, details = "数据加载失败"))
  }
  log_add(glue("数据加载成功: {nrow(df)} 行, {ncol(df)} 列"))

  p <- input$params
  guardrails_cfg <- input$guardrails

  group_var <- p$group_var
  value_var <- p$value_var

  # ===== 参数校验 =====
  if (!(group_var %in% names(df))) {
    return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = group_var))
  }
  if (!(value_var %in% names(df))) {
    return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = value_var))
  }

  # ===== 数据清洗 =====
  original_rows <- nrow(df)
  df <- df[!is.na(df[[group_var]]) & trimws(as.character(df[[group_var]])) != "", ]
  df <- df[!is.na(df[[value_var]]), ]

  removed_rows <- original_rows - nrow(df)
  if (removed_rows > 0) {
    log_add(glue("数据清洗: 移除 {removed_rows} 行缺失值 (剩余 {nrow(df)} 行)"))
  }

  # 确保数值型
  if (!is.numeric(df[[value_var]])) {
    df[[value_var]] <- as.numeric(as.character(df[[value_var]]))
    df <- df[!is.na(df[[value_var]]), ]
  }

  # 分组信息
  df[[group_var]] <- as.factor(df[[group_var]])
  groups <- levels(df[[group_var]])
  n_groups <- length(groups)

  if (n_groups < 3) {
    return(make_error(ERROR_CODES$E003_INSUFFICIENT_GROUPS,
                      col = group_var, expected = "3+", actual = n_groups))
  }

  log_add(glue("分组变量 '{group_var}' 有 {n_groups} 个水平: {paste(groups, collapse=', ')}"))

  # ===== 护栏检查 =====
  guardrail_results <- list()
  warnings_list <- c()
  use_kruskal <- FALSE

  # 每组样本量检查
  group_sizes <- table(df[[group_var]])
  min_group_n <- min(group_sizes)
  sample_check <- check_sample_size(min_group_n, min_required = 3, action = ACTION_BLOCK)
  guardrail_results <- c(guardrail_results, list(sample_check))
  log_add(glue("最小组样本量: {min_group_n}, {sample_check$reason}"))

  # 正态性检验（每组）
  if (isTRUE(guardrails_cfg$check_normality)) {
    log_add("执行正态性检验")
    normality_failed <- FALSE
    for (g in groups) {
      vals <- df[df[[group_var]] == g, value_var]
      if (length(vals) >= 3 && length(vals) <= 5000) {
        norm_check <- check_normality(vals, alpha = 0.05, action = ACTION_SWITCH, action_target = "Kruskal-Wallis")
        guardrail_results <- c(guardrail_results, list(norm_check))
        log_add(glue("组[{g}] 正态性: p = {round(norm_check$p_value, 4)}, {norm_check$reason}"))
        if (!norm_check$passed) normality_failed <- TRUE
      }
    }
    if (normality_failed) use_kruskal <- TRUE
  }

  # 方差齐性检验 (Levene)
  if (!use_kruskal) {
    tryCatch({
      homo_check <- check_homogeneity(df, group_var, value_var, alpha = 0.05, action = ACTION_WARN)
      guardrail_results <- c(guardrail_results, list(homo_check))
      log_add(glue("方差齐性 (Levene): p = {round(homo_check$p_value, 4)}, {homo_check$reason}"))
      if (!homo_check$passed) {
        warnings_list <- c(warnings_list, "方差不齐性，使用 Welch 校正的 ANOVA")
      }
    }, error = function(e) {
      log_add(paste("方差齐性检验失败:", e$message))
    })
  }

  guardrail_status <- run_guardrail_chain(guardrail_results)

  if (guardrail_status$status == "blocked") {
    return(list(status = "blocked", message = guardrail_status$reason, trace_log = logs))
  }

  if (guardrail_status$status == "switch") {
    use_kruskal <- TRUE
    warnings_list <- c(warnings_list, guardrail_status$reason)
    log_add(glue("正态性不满足，切换为 Kruskal-Wallis 检验"))
  }

  if (length(guardrail_status$warnings) > 0) {
    warnings_list <- c(warnings_list, guardrail_status$warnings)
  }

  # ===== 各组描述统计 =====
  group_stats <- lapply(groups, function(g) {
    vals <- df[df[[group_var]] == g, value_var]
    list(
      group = g,
      n = length(vals),
      mean = round(mean(vals), 3),
      sd = round(sd(vals), 3),
      median = round(median(vals), 3),
      q1 = round(quantile(vals, 0.25), 3),
      q3 = round(quantile(vals, 0.75), 3)
    )
  })

  # ===== 核心计算 =====
  if (use_kruskal) {
    log_add("执行 Kruskal-Wallis 检验")
    formula_obj <- as.formula(paste(value_var, "~", group_var))
    result <- kruskal.test(formula_obj, data = df)
    method_used <- "Kruskal-Wallis rank sum test"
    stat_name <- "H"

    # 效应量: η² (eta-squared approximation for Kruskal-Wallis)
    eta_sq <- (result$statistic - n_groups + 1) / (nrow(df) - n_groups)
    eta_sq <- max(0, as.numeric(eta_sq))

    output_results <- list(
      method = method_used,
      statistic = jsonlite::unbox(as.numeric(result$statistic)),
      statistic_name = stat_name,
      df = jsonlite::unbox(as.numeric(result$parameter)),
      p_value = jsonlite::unbox(as.numeric(result$p.value)),
      p_value_fmt = format_p_value(result$p.value),
      effect_size = list(
        eta_squared = jsonlite::unbox(round(eta_sq, 4)),
        interpretation = interpret_eta_sq(eta_sq)
      ),
      group_stats = group_stats
    )

    # 事后多重比较: Dunn test (pairwise Wilcoxon)
    posthoc_result <- tryCatch({
      pw <- pairwise.wilcox.test(df[[value_var]], df[[group_var]], p.adjust.method = "bonferroni")
      pw
    }, error = function(e) {
      log_add(paste("Dunn 事后检验失败:", e$message))
      NULL
    })
  } else {
    log_add("执行单因素 ANOVA")
    formula_obj <- as.formula(paste(value_var, "~", group_var))

    # 检查方差齐性决定使用经典 ANOVA 还是 Welch ANOVA
    use_welch <- any(grepl("方差不齐性", warnings_list))

    if (use_welch) {
      result <- oneway.test(formula_obj, data = df, var.equal = FALSE)
      method_used <- "One-way ANOVA (Welch correction)"
    } else {
      aov_result <- aov(formula_obj, data = df)
      result_summary <- summary(aov_result)
      result <- list(
        statistic = result_summary[[1]]$`F value`[1],
        parameter = c(result_summary[[1]]$Df[1], result_summary[[1]]$Df[2]),
        p.value = result_summary[[1]]$`Pr(>F)`[1]
      )
      method_used <- "One-way ANOVA"
    }

    stat_name <- "F"

    # 效应量: η² (eta-squared)
    ss_between <- sum(tapply(df[[value_var]], df[[group_var]], function(x) length(x) * (mean(x) - mean(df[[value_var]]))^2))
    ss_total <- sum((df[[value_var]] - mean(df[[value_var]]))^2)
    eta_sq <- ss_between / ss_total

    f_val <- if (is.list(result)) result$statistic else as.numeric(result$statistic)
    df_val <- if (is.list(result) && !is.null(result$parameter)) {
      if (length(result$parameter) == 2) result$parameter else as.numeric(result$parameter)
    } else {
      as.numeric(result$parameter)
    }
    p_val <- if (is.list(result)) result$p.value else as.numeric(result$p.value)

    output_results <- list(
      method = method_used,
      statistic = jsonlite::unbox(as.numeric(f_val)),
      statistic_name = stat_name,
      df = if (length(df_val) == 2) as.numeric(df_val) else jsonlite::unbox(as.numeric(df_val)),
      p_value = jsonlite::unbox(as.numeric(p_val)),
      p_value_fmt = format_p_value(p_val),
      effect_size = list(
        eta_squared = jsonlite::unbox(round(eta_sq, 4)),
        interpretation = interpret_eta_sq(eta_sq)
      ),
      group_stats = group_stats
    )

    # 事后多重比较: Tukey HSD (if classic ANOVA) or pairwise t-test
    posthoc_result <- tryCatch({
      if (use_welch) {
        pairwise.t.test(df[[value_var]], df[[group_var]], p.adjust.method = "bonferroni", pool.sd = FALSE)
      } else {
        TukeyHSD(aov(formula_obj, data = df))
      }
    }, error = function(e) {
      log_add(paste("事后多重比较失败:", e$message))
      NULL
    })
  }

  log_add(glue("{stat_name} = {round(as.numeric(output_results$statistic), 3)}, P = {round(as.numeric(output_results$p_value), 4)}"))

  # 整理事后比较结果
  posthoc_pairs <- NULL
  if (!is.null(posthoc_result)) {
    if (inherits(posthoc_result, "TukeyHSD")) {
      tukey_df <- as.data.frame(posthoc_result[[1]])
      posthoc_pairs <- lapply(seq_len(nrow(tukey_df)), function(i) {
        list(
          comparison = rownames(tukey_df)[i],
          diff = round(tukey_df$diff[i], 3),
          ci_lower = round(tukey_df$lwr[i], 3),
          ci_upper = round(tukey_df$upr[i], 3),
          p_adj = round(tukey_df$`p adj`[i], 4),
          p_adj_fmt = format_p_value(tukey_df$`p adj`[i]),
          significant = tukey_df$`p adj`[i] < 0.05
        )
      })
    } else if (inherits(posthoc_result, "pairwise.htest")) {
      p_matrix <- posthoc_result$p.value
      for (i in seq_len(nrow(p_matrix))) {
        for (j in seq_len(ncol(p_matrix))) {
          if (!is.na(p_matrix[i, j])) {
            if (is.null(posthoc_pairs)) posthoc_pairs <- list()
            posthoc_pairs[[length(posthoc_pairs) + 1]] <- list(
              comparison = paste(rownames(p_matrix)[i], "vs", colnames(p_matrix)[j]),
              p_adj = round(p_matrix[i, j], 4),
              p_adj_fmt = format_p_value(p_matrix[i, j]),
              significant = p_matrix[i, j] < 0.05
            )
          }
        }
      }
    }
  }

  output_results$posthoc <- posthoc_pairs

  # ===== 生成图表 =====
  log_add("生成箱线图")
  plot_base64 <- tryCatch({
    generate_anova_boxplot(df, group_var, value_var)
  }, error = function(e) {
    log_add(paste("图表生成失败:", e$message))
    NULL
  })

  # ===== 生成可复现代码 =====
  original_filename <- if (!is.null(input$original_filename) && nchar(input$original_filename) > 0) {
    input$original_filename
  } else {
    "data.csv"
  }

  reproducible_code <- glue('
# SSA-Pro 自动生成代码
# 工具: 单因素方差分析
# 时间: {Sys.time()}
# ================================

library(ggplot2)

# 数据准备
df <- read.csv("{original_filename}")
group_var <- "{group_var}"
value_var <- "{value_var}"

# 单因素 ANOVA
result <- aov(as.formula(paste(value_var, "~", group_var)), data = df)
summary(result)

# 事后多重比较 (Tukey HSD)
TukeyHSD(result)

# 可视化
ggplot(df, aes(x = .data[[group_var]], y = .data[[value_var]], fill = .data[[group_var]])) +
  geom_boxplot(alpha = 0.7) +
  theme_minimal() +
  labs(title = paste("Distribution of", value_var, "by", group_var))
')

  # ===== 构建 report_blocks =====
  blocks <- list()

  # Block 1: 各组描述统计
  desc_headers <- c("组别", "N", "均值", "标准差", "中位数")
  desc_rows <- lapply(group_stats, function(gs) {
    c(gs$group, as.character(gs$n), as.character(gs$mean), as.character(gs$sd), as.character(gs$median))
  })
  blocks[[length(blocks) + 1]] <- make_table_block(desc_headers, desc_rows, title = "各组描述统计")

  # Block 2: 检验结果
  kv_items <- list(
    "方法" = method_used,
    "统计量" = paste0(stat_name, " = ", round(as.numeric(output_results$statistic), 3)),
    "P 值" = output_results$p_value_fmt,
    "η²" = as.character(output_results$effect_size$eta_squared),
    "效应量解释" = output_results$effect_size$interpretation
  )
  blocks[[length(blocks) + 1]] <- make_kv_block(kv_items, title = "检验结果")

  # Block 3: 事后多重比较
  if (!is.null(posthoc_pairs) && length(posthoc_pairs) > 0) {
    ph_headers <- c("比较", "P 值 (校正)", "显著性")
    ph_rows <- lapply(posthoc_pairs, function(pair) {
      sig <- if (pair$significant) "*" else ""
      c(pair$comparison, pair$p_adj_fmt, sig)
    })
    blocks[[length(blocks) + 1]] <- make_table_block(ph_headers, ph_rows,
      title = "事后多重比较",
      footnote = if (use_kruskal) "Bonferroni 校正的 Wilcoxon 检验" else "Tukey HSD / Bonferroni 校正")
  }

  # Block 4: 箱线图
  if (!is.null(plot_base64)) {
    blocks[[length(blocks) + 1]] <- make_image_block(plot_base64,
      title = paste(value_var, "by", group_var),
      alt = paste("箱线图：", value_var, "按", group_var, "分组"))
  }

  # Block 5: 结论摘要
  p_val_num <- as.numeric(output_results$p_value)
  sig_text <- if (p_val_num < 0.05) "各组间存在统计学显著差异" else "各组间差异无统计学意义"
  conclusion <- glue("{method_used}: {stat_name} = {round(as.numeric(output_results$statistic), 3)}, P {output_results$p_value_fmt}。{sig_text}（η² = {output_results$effect_size$eta_squared}，{output_results$effect_size$interpretation}效应）。")

  if (!is.null(posthoc_pairs) && p_val_num < 0.05) {
    sig_pairs <- Filter(function(x) x$significant, posthoc_pairs)
    if (length(sig_pairs) > 0) {
      pair_names <- sapply(sig_pairs, function(x) x$comparison)
      conclusion <- paste0(conclusion, glue("\n\n事后比较显示以下组间差异显著：{paste(pair_names, collapse = '、')}。"))
    }
  }
  blocks[[length(blocks) + 1]] <- make_markdown_block(conclusion, title = "结论摘要")

  # ===== 返回结果 =====
  log_add("分析完成")

  return(list(
    status = "success",
    message = "分析完成",
    warnings = if (length(warnings_list) > 0) warnings_list else NULL,
    results = output_results,
    report_blocks = blocks,
    plots = if (!is.null(plot_base64)) list(plot_base64) else list(),
    trace_log = logs,
    reproducible_code = as.character(reproducible_code)
  ))
}

# η² 效应量解释
interpret_eta_sq <- function(eta_sq) {
  if (eta_sq < 0.01) return("微小")
  if (eta_sq < 0.06) return("小")
  if (eta_sq < 0.14) return("中等")
  return("大")
}

# NULL 合并运算符
`%||%` <- function(x, y) if (is.null(x)) y else x

# 辅助函数：ANOVA 箱线图
generate_anova_boxplot <- function(df, group_var, value_var) {
  p <- ggplot(df, aes(x = .data[[group_var]], y = .data[[value_var]], fill = .data[[group_var]])) +
    geom_boxplot(alpha = 0.7, outlier.shape = 21) +
    stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "red") +
    theme_minimal() +
    labs(
      title = paste("Distribution of", value_var, "by", group_var),
      x = group_var,
      y = value_var
    ) +
    scale_fill_brewer(palette = "Set2") +
    theme(legend.position = "none")

  tmp_file <- tempfile(fileext = ".png")
  ggsave(tmp_file, p, width = max(7, length(unique(df[[group_var]])) * 1.5), height = 5, dpi = 100)
  base64_str <- base64encode(tmp_file)
  unlink(tmp_file)

  return(paste0("data:image/png;base64,", base64_str))
}