# 【请在此处填入你的本地数据路径】
china_map   <- st_read("D:/1work/2city/light/raw_data/行政区划/shi2022.shp", quiet = TRUE) 
lake_data   <- read.csv("D:/1work/2city/light/raw_data/湖泊总.csv") 
tif_dir     <- "D:/1work/2city/light/raw_data/tif/"

# 🌟【核心切换开关】：想画哪个城市，直接改这里，后续全自动运行！
target_city <- "JN"

# 统一转换为经纬度投影
china_map_4326 <- st_transform(china_map, crs = 4326)

# 动态过滤并显式锁定图例显示顺序：city_H -> city_M -> city_L
df_current <- lake_data %>% 
  filter(City_Name == target_city) %>% 
  mutate(City_Group = factor(City_Group, levels = c("city_H", "city_M", "city_L")))

# 转换为 sf 空间矢量对象
df_current_sf <- st_as_sf(df_current, coords = c("Centroid_X", "Centroid_Y"), crs = 4326)

# 自动寻找并合并当前城市的完整“区划轮廓模具”
current_boundary <- china_map_4326[st_intersects(china_map_4326, df_current_sf, sparse = FALSE) %>% rowSums() > 0, ] %>% 
  st_union()

# 自动诊断当前城市的 shp 边界是否存在【经纬度颠倒】问题
bbox_check <- st_bbox(current_boundary)
if (mean(c(bbox_check[["xmin"]], bbox_check[["xmax"]])) < mean(c(bbox_check[["ymin"]], bbox_check[["ymax"]]))) {
  cat("⚠️ 警告：检测到当前城市 administrative shp 存在经纬度反转问题，正在全自动拨乱反正...\n")
  current_boundary <- st_flip_coordinates(current_boundary)
}

# 确保转换为标准的 terra 矢量对象
city_vector_4326 <- vect(current_boundary)

tif_files   <- list.files(tif_dir, pattern = "\\.tif$", full.names = TRUE)
raster_list <- lapply(tif_files, rast)

selected_raster_4326 <- NULL

for (i in seq_along(raster_list)) {
  r <- raster_list[[i]]
  r_4326 <- project(r, "EPSG:4326", method = "near")
  
  # 步骤 A：几何外框相交检测
  is_intersected <- is.related(ext(r_4326), city_vector_4326, "intersects")
  
  if (is_intersected) {
    # 步骤 B：试切并检查空值
    test_crop <- crop(r_4326, city_vector_4326)
    valid_pixels <- sum(!is.na(values(test_crop)))
    
    if (valid_pixels > 0) {
      selected_raster_4326 <- r_4326
      cat(paste0("📊 智能匹配成功！", target_city, " 在第 ", i, " 个 TIF 中找到了 ", valid_pixels, " 个有效城镇化像素。\n"))
      break
    } else {
      cat(paste0("ℹ️ 提示：第 ", i, " 个 TIF 虽然几何相交，但在 ", target_city, " 范围内全是空白(NA)，自动跳过...\n"))
    }
  }
}

if (is.null(selected_raster_4326)) {
  stop("❌ 错误：在所有 TIF 文件夹中，都没有找到能在该城市范围内提供【有效像素值】的影像！")
}

# 1. 动态计算【局部放大】的经纬度边界框
bbox_points <- st_bbox(df_current_sf)
x_point_padding <- (bbox_points[["xmax"]] - bbox_points[["xmin"]]) * 0.1
y_point_padding <- (bbox_points[["ymax"]] - bbox_points[["ymin"]]) * 0.1

local_xlim <- c(bbox_points[["xmin"]] - x_point_padding, bbox_points[["xmax"]] + x_point_padding)
local_ylim <- c(bbox_points[["ymin"]] - y_point_padding, bbox_points[["ymax"]] + y_point_padding)
local_extent_4326 <- ext(local_xlim[1], local_xlim[2], local_ylim[1], local_ylim[2])

# 2. 动态计算【总图锁定】的经纬度边界
bbox_city <- st_bbox(current_boundary)
x_city_padding <- (bbox_city[["xmax"]] - bbox_city[["xmin"]]) * 0.05
y_city_padding <- (bbox_city[["ymax"]] - bbox_city[["ymin"]]) * 0.05

city_xlim <- c(bbox_city[["xmin"]] - x_city_padding, bbox_city[["xmax"]] + x_city_padding)
city_ylim <- c(bbox_city[["ymin"]] - y_city_padding, bbox_city[["ymax"]] + y_city_padding)

# --- 执行安全裁剪与后处理 ---
smod_macro_cropped <- crop(selected_raster_4326, city_vector_4326)
smod_local_cropped <- crop(selected_raster_4326, local_extent_4326)

smod_macro_smooth <- resample(smod_macro_cropped, smod_macro_cropped, method = "bilinear")
names(smod_macro_smooth) <- "SMOD_Value"
smod_macro_masked <- mask(smod_macro_smooth, city_vector_4326)

names(smod_local_cropped) <- "SMOD_Class"
smod_local_4326 <- suppressWarnings(as.factor(smod_local_cropped))

gray_colors <- c(
  "11" = "#F0F0F0", "12" = "#E0E0E0", "13" = "#D0D0D0", 
  "21" = "#B8B8B8", "22" = "#A0A0A0", "23" = "#888888", 
  "30" = "#686868", "NA" = "transparent"
)