修改

Co-authored-by: Copilot <copilot@github.com>

tools/core/soil_prop_stats/B1_TRZD12土壤属性分级分布.py

@@ -43,15 +43,15 @@ def get_prop_level(prop_level):
     if pd.isna(prop_level) or str(prop_level) == "0":
         return "-"
     # 请根据您的实际分级标准调整这里的阈值
-    if str(prop_level) == "8" or prop_level == '砂土及壤质砂土':
+    if str(prop_level) == "1" or prop_level == '砂土及壤质砂土':
         return "砂质"
-    elif str(prop_level) == "11" or prop_level == '砂质壤土':
+    elif str(prop_level) == "2" or prop_level == '砂质壤土':
         return "砂壤质"
-    elif str(prop_level) in ["6","3"] or prop_level in ['粉砂质壤土', '壤土']:
+    elif str(prop_level) in ["3","4"] or prop_level in ['粉砂质壤土', '壤土']:
         return "壤质"
-    elif str(prop_level) in ["1","4","9"] or prop_level in ['粉砂质年壤土', '黏壤土', '砂质黏壤土']:
+    elif str(prop_level) in ["5","6","7"] or prop_level in ['粉砂质年壤土', '黏壤土', '砂质黏壤土']:
         return "黏壤质"
-    elif str(prop_level) in ["2","5","7","10","12"] or prop_level in ['粉砂质黏土', '黏土', '壤质黏土', '砂质黏土', '重黏土']:
+    elif str(prop_level) in ["8","9","10","11","12"] or prop_level in ['粉砂质黏土', '黏土', '壤质黏土', '砂质黏土', '重黏土']:
         return "黏质"
     else: 
         return "-"  

tools/core/soil_prop_stats/B1土壤属性分级分布.py

@@ -259,7 +259,7 @@ def process_data_for_table1(gdb_path, soil_prop_feature_name, df_origin_area, ta
     return df_final, stat_sample
 
 # --- 3. Excel 制表 总表---
-def write_to_excel_table1(df:pd.DataFrame, output_path, prop_config, soil_prop_tif, stat_sample):
+def write_to_excel_table1(df:pd.DataFrame, output_path, prop_config, stats_map, stat_sample):
     """
     【最终修正版】: 将处理好的数据写入格式化的 Excel 文件。
     """
@@ -271,30 +271,35 @@ def write_to_excel_table1(df:pd.DataFrame, output_path, prop_config, soil_prop_t
         wb.save(output_path)
         return
 
-    # 全区制图统计
-    """
-    try:
-        raster = arcpy.Raster(soil_prop_tif)
+    # # 全区制图统计
+    # try:
+    #     raster = arcpy.Raster(soil_prop_tif)
         
-        # 转换为numpy数组进行计算
-        array = arcpy.RasterToNumPyArray(raster,nodata_to_value=9999)
+    #     # 按行政区蒙膜区域统计
+    #     out_raster = arcpy.sa.ExtractByMask(
+    #         in_raster=raster,
+    #         in_mask_data=tif_mask,
+    #         extraction_area="INSIDE"
+    #     )
+    #     # 转换为numpy数组进行计算
+    #     array = arcpy.RasterToNumPyArray(out_raster,nodata_to_value=9999)
         
-        # 过滤掉NoData值
-        # 过滤NoData值和9999值
-        array = array[~np.isnan(array)]  # 过滤NoData
-        array = array[array != 9999]     # 过滤9999
-        array = array.astype(np.float64)
+    #     # 过滤掉NoData值
+    #     # 过滤NoData值和9999值
+    #     array = array[~np.isnan(array)]  # 过滤NoData
+    #     array = array[array != 9999]     # 过滤9999
+    #     array = array.astype(np.float64)
+        
+    #     stats_map = {
+    #         'min': round(np.min(array),2),
+    #         'max': round(np.max(array),2),
+    #         'mean': round(np.mean(array),2),
+    #         'median': round(np.median(array),2),
+    #         'std': round(np.std(array),2)
+    #     }
+    # except Exception as e:
+    #     print(f"错误: {e}")
     
-        stats = {
-            'min': round(np.min(array),2),
-            'max': round(np.max(array),2),
-            'mean': round(np.mean(array),2),
-            'median': round(np.median(array),2),
-            'std': round(np.std(array),2)
-        }
-    except Exception as e:
-        print(f"错误: {e}")
-    """
     # 全区样点统计
     stats = stat_sample
 
@@ -419,17 +424,26 @@ def write_to_excel_table1(df:pd.DataFrame, output_path, prop_config, soil_prop_t
     ws.cell(row=current_row, column=6).value = '100'
 
     # 3. 合计单元格填充
-    ws.merge_cells(f'B{current_row + 1}:F{current_row + 1}')
+    ws.merge_cells(f'A{current_row + 1}:B{current_row + 1}')
     ws.cell(row=current_row + 1, column=1).value = '全区均值'
-    ws.cell(row=current_row + 1, column=2).value = f'{stats["mean"]:.{prop_name}}'
+    ws.merge_cells(f'C{current_row + 1}:D{current_row + 1}')
+    ws.cell(row=current_row + 1, column=3).value = f'{stats["mean"]:.{prop_name}}'
+    ws.merge_cells(f'E{current_row + 1}:F{current_row + 1}')
+    ws.cell(row=current_row + 1, column=5).value = f'{stats_map["mean"]:.{prop_name}}'
 
-    ws.merge_cells(f'B{current_row + 2}:F{current_row + 2}')
+    ws.merge_cells(f'A{current_row + 2}:B{current_row + 2}')
     ws.cell(row=current_row + 2, column=1).value = '全区中位值'
-    ws.cell(row=current_row + 2, column=2).value = f'{stats["median"]:.{prop_name}}'
+    ws.merge_cells(f'C{current_row + 2}:D{current_row + 2}')
+    ws.cell(row=current_row + 2, column=3).value = f'{stats["median"]:.{prop_name}}'
+    ws.merge_cells(f'E{current_row + 2}:F{current_row + 2}')
+    ws.cell(row=current_row + 2, column=5).value = f'{stats_map["median"]:.{prop_name}}'
 
-    ws.merge_cells(f'B{current_row + 3}:F{current_row + 3}')
+    ws.merge_cells(f'A{current_row + 3}:B{current_row + 3}')
     ws.cell(row=current_row + 3, column=1).value = '全区范围'
-    ws.cell(row=current_row + 3, column=2).value = f'{stats["min"]:.{prop_name}} ～ {stats["max"]:.{prop_name}}'
+    ws.merge_cells(f'C{current_row + 3}:D{current_row + 3}')
+    ws.cell(row=current_row + 3, column=3).value = f'{stats["min"]:.{prop_name}} ～ {stats["max"]:.{prop_name}}'
+    ws.merge_cells(f'E{current_row + 3}:F{current_row + 3}')
+    ws.cell(row=current_row + 3, column=5).value = f'{stats_map["min"]:.{prop_name}} ～ {stats_map["max"]:.{prop_name}}'
 
     # --- a. 定义样式 ---
     header_font = Font(name='宋体', size=11, bold=True)
@@ -447,7 +461,7 @@ def write_to_excel_table1(df:pd.DataFrame, output_path, prop_config, soil_prop_t
     print("Excel 报告生成成功！")
 
 
-def main(gdb_path, soil_prop_name, reclassed_features_path, dltb_features, soil_prop_tif, output_path, target_area_dict,xzqmc, prop_config):
+def main(gdb_path, soil_prop_name, reclassed_features_path, dltb_features, stats_map, output_path, target_area_dict,xzqmc, prop_config):
     try:
         # --- 1. 用户配置 ---
         # 输出配置
@@ -493,10 +507,24 @@ def main(gdb_path, soil_prop_name, reclassed_features_path, dltb_features, soil_
         )
         clipped_table_df = arcgis_utils.read_arcgis_table(temp_out_tables_area)
 
+        # 定义需要过滤地类的属性列表
+        filtered_props = ['ECA', 'EMG', 'ACU', 'AZN', 'AFE', 'AMN', 'AMO', 'AB', 'AS1', 'TSE']
+        dltb_features = os.path.join(gdb_path, "地类图斑")
+
         # 生成表1 土壤属性分级分布 的统计Excel报告
         final_dataframe,stat = process_data_for_table1(gdb_path, soil_prop_name, clipped_table_df, target_area_dict,xzqmc,is_by_xzq, prop_config)
         
-        write_to_excel_table1(final_dataframe, output_excel_path, prop_config, soil_prop_tif, stat)
+        # 过滤地类图斑
+        temp_mask = r"in_memory/temp_dltb_features_filtered"
+        temp_files.append(temp_mask)
+        if soil_prop_name == "GZCHD":
+            arcpy.conversion.ExportFeatures(dltb_features, temp_mask, "DLBM LIKE '01%'")
+        if soil_prop_name in filtered_props:
+            arcpy.conversion.ExportFeatures(dltb_features, temp_mask, "DLBM LIKE '01%' OR DLBM LIKE '02%'")
+        else:
+            temp_mask = dltb_features
+        
+        write_to_excel_table1(final_dataframe, output_excel_path, prop_config, stats_map, stat)
 
         # return df_with_factors
     except Exception as e:

tools/core/soil_prop_stats/B2土地利用类型土壤属性.py

@@ -72,7 +72,7 @@ def process_data_for_table2(gdb_path, soil_prop_feature_name, df_dltb, target_ar
     return df_final
 
 # 写入EXCEL 表2
-def write_to_excel_table2(df, output_path, prop_config:dict, soil_prop_name: str = ''):
+def write_to_excel_table2(df, output_path,stats_map, prop_config:dict, soil_prop_name: str = ''):
     """
     将处理好的数据写入格式化的 Excel 文件。
     """
@@ -260,7 +260,7 @@ def write_to_excel_table2(df, output_path, prop_config:dict, soil_prop_name: str
     ws.cell(row=current_row, column=4).value = total_range
     ws.cell(row=current_row, column=5).value = f"{total_counts:.0f}" if total_counts > 0 else "-"
     if pd.notna(total_zhitu_mean):
-        ws.cell(row=current_row, column=6).value = f"{total_zhitu_mean:.{prop_name}}"
+        ws.cell(row=current_row, column=6).value = f"{stats_map['mean']:.{prop_name}}"
     else:
         ws.cell(row=current_row, column=6).value = "-"
     ws.cell(row=current_row, column=7).value = f"{total_areas:.0f}" if total_areas > 0 else "-"
@@ -285,7 +285,7 @@ def write_to_excel_table2(df, output_path, prop_config:dict, soil_prop_name: str
     print("Excel 报告生成成功！")
 
 
-def main(gdb_path, soil_prop_name, dltb_features, soil_prop_tif, output_path,target_areas_df, prop_config):
+def main(gdb_path, soil_prop_name, dltb_features, soil_prop_tif, output_path,target_areas_df, prop_config,stats_map):
     try:
         # --- 1. 用户配置 ---
         # 输出配置
@@ -311,7 +311,7 @@ def main(gdb_path, soil_prop_name, dltb_features, soil_prop_tif, output_path,tar
         final_dataframe = process_data_for_table2(gdb_path, soil_prop_name, dltb_df, target_areas_df)
 
         # final_dataframe = process_data_for_table5_2(gdb_path, out_table_area, sample_table_name, df_with_factors)
-        write_to_excel_table2(final_dataframe, output_excel_path, prop_config, soil_prop_name)
+        write_to_excel_table2(final_dataframe, output_excel_path,stats_map, prop_config, soil_prop_name)
 
         # return df_with_factors
     except Exception as e:

tools/core/soil_prop_stats/B3不同土壤类型土壤属性.py

@@ -55,7 +55,6 @@ def process_data_for_table3(soil_prop_name, df_trlx_sample, df_trlx_zhitu, df_tr
     filtered_props = ['ECA', 'EMG', 'ACU', 'AZN', 'AFE', 'AMN', 'AMO', 'AB', 'AS1', 'TSE']
 
     # 拿到目标df总面积，计算比例进行平差
-    print(target_areas_df)
     if soil_prop_name == "GZCHD":
         target_areas = target_areas_df[target_areas_df['EJDL'] == '耕地']['面积'].values[0]
     elif soil_prop_name in filtered_props:
@@ -91,7 +90,7 @@ def process_data_for_table3(soil_prop_name, df_trlx_sample, df_trlx_zhitu, df_tr
     return df_final, df_sample_mymz
 
 # 写入EXCEL 表2
-def write_to_excel_table3(df, output_path, prop_config:dict, stats):
+def write_to_excel_table3(df, output_path, prop_config:dict, stats,stats_map):
     """
     将处理好的数据写入格式化的 Excel 文件。
     """
@@ -246,9 +245,9 @@ def write_to_excel_table3(df, output_path, prop_config:dict, stats):
     # total_mean = total_weighted_sum.sum() / total_counts
     # total_median = df_to_write['median'].mean()
     total_range = f"{df_to_write['min'].min():.{prop_name}}～{df_to_write['max'].max():.{prop_name}}"
-    total_zhitu_weighted_sum = df_to_write['制图均值'] * df_to_write['面积_亩']
+    # total_zhitu_weighted_sum = df_to_write['制图均值'] * df_to_write['面积_亩']
     total_areas = df_to_write['面积_亩'].sum()
-    total_zhitu_mean = total_zhitu_weighted_sum.sum() / total_areas
+    # total_zhitu_mean = total_zhitu_weighted_sum.sum() / total_areas
 
     ws.merge_cells(start_row=current_row, start_column=1, end_row=current_row, end_column=2)
     ws.cell(row=current_row, column=1).value = '全区'
@@ -256,7 +255,7 @@ def write_to_excel_table3(df, output_path, prop_config:dict, stats):
     ws.cell(row=current_row, column=4).value = f"{stats['median']:.{prop_name}}"
     ws.cell(row=current_row, column=5).value = total_range
     ws.cell(row=current_row, column=6).value = f"{stats['count']:.0f}"
-    ws.cell(row=current_row, column=7).value = f"{total_zhitu_mean:.{prop_name}}"
+    ws.cell(row=current_row, column=7).value = f"{stats_map['mean']:.{prop_name}}"
     ws.cell(row=current_row, column=8).value = f"{total_areas:.0f}"
 
     # --- a. 定义样式 ---
@@ -397,7 +396,7 @@ def write_to_excel_table4(df:pd.DataFrame, output_path, prop_config, stats):
     print(f"数据已成功写入到 {output_path}")
 
 
-def main(gdb_path, soil_prop_name, trlx_features, soil_prop_tif, output_path,target_areas_df, prop_config, dltb_features):
+def main(gdb_path, soil_prop_name, trlx_features, soil_prop_tif, output_path,target_areas_df, prop_config, dltb_features,stats_map):
     try:
         # --- 1. 用户配置 ---
         # 输出配置
@@ -411,16 +410,6 @@ def main(gdb_path, soil_prop_name, trlx_features, soil_prop_tif, output_path,tar
         arcpy.env.overwriteOutput = True
         
         print("开始处理数据...")
-        if soil_prop_name == "GZCHD":
-            temp_gdtb_trlx_out = r"in_memory/temp_gdtb_trlx_out" 
-            temp_gdtb_trlx = r"in_memory/temp_gdtb_trlx"
-            temp_files.append(temp_gdtb_trlx)
-
-        temp_out_features = r"in_memory/temp_out_type_features"
-        out_table_mean = r"in_memory/out_table_type_mean"
-        temp_files.append(temp_out_features)
-        temp_files.append(out_table_mean)
-
         # 2. 用样点进行空间连接到土壤类型图斑
         fields_to_keep = {
             soil_prop_features: [soil_prop_name],
@@ -442,25 +431,37 @@ def main(gdb_path, soil_prop_name, trlx_features, soil_prop_tif, output_path,tar
         # 定义需要过滤地类的属性列表
         filtered_props = ['ECA', 'EMG', 'ACU', 'AZN', 'AFE', 'AMN', 'AMO', 'AB', 'AS1', 'TSE']
 
-        # 空间连接
-        arcpy.analysis.SpatialJoin(soil_prop_features, trlx_features, temp_out_features, "JOIN_ONE_TO_ONE", "KEEP_ALL",field_mappings, "INTERSECT")
-
         if soil_prop_name == "GZCHD":
-            arcpy.analysis.Intersect([trlx_features, dltb_features], temp_gdtb_trlx, 'NO_FID')
-            arcpy.conversion.ExportFeatures(temp_gdtb_trlx,temp_gdtb_trlx_out,"DLBM LIKE '01%'")
+            temp_gdtb_trlx_out = r"in_memory/temp_gdtb_trlx_out" 
+            temp_files.append(temp_gdtb_trlx_out)
 
-            # 3. 以表格显示分区统计 计算均值
-            arcpy.sa.ZonalStatisticsAsTable(temp_gdtb_trlx_out, "YL_TS", soil_prop_tif, out_table_mean, "DATA", "MEAN")
-            trlx_area_df = pd.DataFrame(arcpy.da.FeatureClassToNumPyArray(temp_gdtb_trlx_out, ["YL", "TS", "Shape@Area"]))
-        # 如果当前属性在列表中，则只统计耕地和园地
-        elif soil_prop_name in filtered_props:
+        # 2. 创建临时图斑用于过滤、土壤类型与地类图斑相交
+        # 用于样点
+        temp_out_features = r"in_memory/temp_out_type_features"
+        out_table_mean = r"in_memory/out_table_type_mean"
+        temp_files.append(temp_out_features)
+        temp_files.append(out_table_mean)
+        # 用于制图
         temp_gdtb_trlx_filtered = r"in_memory/temp_gdtb_trlx_filtered"
         temp_gdtb_trlx_out_filtered = r"in_memory/temp_gdtb_trlx_out_filtered"
         temp_files.append(temp_gdtb_trlx_filtered)
         temp_files.append(temp_gdtb_trlx_out_filtered)
 
+        # 空间连接
+        arcpy.analysis.SpatialJoin(soil_prop_features, trlx_features, temp_out_features, "JOIN_ONE_TO_ONE", "KEEP_ALL",field_mappings, "INTERSECT")
+
         # 交集土壤类型与土地利用图斑
         arcpy.analysis.Intersect([trlx_features, dltb_features], temp_gdtb_trlx_filtered, 'NO_FID')
+        
+        # 3. 交集土壤类型与土地利用图斑
+        if soil_prop_name == "GZCHD":
+            arcpy.conversion.ExportFeatures(temp_gdtb_trlx_filtered,temp_gdtb_trlx_out,"DLBM LIKE '01%'")
+
+            # 以表格显示分区统计 计算均值
+            arcpy.sa.ZonalStatisticsAsTable(temp_gdtb_trlx_out, "YL_TS", soil_prop_tif, out_table_mean, "DATA", "MEAN")
+            trlx_area_df = pd.DataFrame(arcpy.da.FeatureClassToNumPyArray(temp_gdtb_trlx_out, ["YL", "TS", "Shape@Area"]))
+        # 如果当前属性在列表中，则只统计耕地和园地
+        elif soil_prop_name in filtered_props:
             # 导出耕地和园地（DLBM LIKE '01%' OR DLBM LIKE '02%'）
             arcpy.conversion.ExportFeatures(temp_gdtb_trlx_filtered, temp_gdtb_trlx_out_filtered, "DLBM LIKE '01%' OR DLBM LIKE '02%'")
             
@@ -472,7 +473,7 @@ def main(gdb_path, soil_prop_name, trlx_features, soil_prop_tif, output_path,tar
             print(f"过滤制图数据：仅统计耕地和园地（DLBM LIKE '01%' OR '02%'）")
         else:
             # 3. 以表格显示分区统计 计算均值
-            arcpy.sa.ZonalStatisticsAsTable(trlx_features, "YL_TS", soil_prop_tif, out_table_mean, "DATA", "MEAN")
+            arcpy.sa.ZonalStatisticsAsTable(temp_gdtb_trlx_filtered, "YL_TS", soil_prop_tif, out_table_mean, "DATA", "MEAN")
             # 获取土壤类型图斑面积
             trlx_area_df = pd.DataFrame(arcpy.da.FeatureClassToNumPyArray(trlx_features, ["YL", "TS", "Shape@Area"]))
 
@@ -493,7 +494,7 @@ def main(gdb_path, soil_prop_name, trlx_features, soil_prop_tif, output_path,tar
         # print(final_dataframe)
 
         # 生成表3
-        write_to_excel_table3(final_dataframe, output_excel_path, prop_config, stat_sample)
+        write_to_excel_table3(final_dataframe, output_excel_path, prop_config, stat_sample,stats_map)
         # 母岩母质表
         write_to_excel_table4(df_mymz, output_excel4_path, prop_config,stat_sample)
 

tools/core/stats_area_to_excel.py

@@ -10,6 +10,7 @@
 import json
 import os
 from pathlib import Path
+import re
 import sys
 import traceback
 import uuid
@@ -92,6 +93,27 @@ def log_arcpy_message(message):
         sys.stderr.write(f"ArcPyError:{message.message}\n")
         sys.stderr.flush()
 
+# 判断单元格类型
+def get_merge_type(merged_range):
+    """
+    判断合并类型
+    返回: 'row'（行合并）, 'column'（列合并）, 'both'（行列合并）或 None（不是合并单元格）
+    """
+    if not merged_range:
+        return None
+    
+    min_row, max_row = merged_range.min_row, merged_range.max_row
+    min_col, max_col = merged_range.min_col, merged_range.max_col
+    
+    if max_row > min_row and max_col > min_col:
+        return 'both'  # 同时跨行和跨列
+    elif max_row > min_row:
+        return 'row'   # 行合并（垂直合并）
+    elif max_col > min_col:
+        return 'column'  # 列合并（水平合并）
+    else:
+        return None  # 实际上不是合并单元格
+    
 def get_specail_map(original_dict):
     grade_map = {}
     dict_len = len(original_dict)
@@ -200,7 +222,8 @@ class SoilQualityReporter:
         # 复制数据并添加分组列
         df_stats = stats.copy()
         # df_stats["GRID_GROUP"] = df_stats["GRIDCODE"].apply(self._map_grade)
-        if "YJDLBM" not in df_stats.columns:
+        if "YJDLBM" not in df_stats.columns and "YNDLBM" in df_stats.columns:
+            # 云南地区使用YNDLBM（二级地类）
             df = df_stats.pivot_table(
                 index="YNDLBM",
                 columns="GRIDCODE",
@@ -210,6 +233,7 @@ class SoilQualityReporter:
                 observed=False
             )
         else:
+            # 其他地区使用YJDLBM（一级地类）
             df = df_stats.pivot_table(
                 index="YJDLBM",
                 columns="GRIDCODE",
@@ -227,11 +251,46 @@ class SoilQualityReporter:
         # 按等级排序并添加总计
         df = df[self.all_grades]
         df["总计"] = df.sum(axis=1)
-        df.loc["总计"] = df.sum(axis=0)
         
         # 重命名索引和列
         df = df.rename(index=self.landuse_map)
         df.columns = [self.grade_map.get(str(col), str(col)) for col in df.columns]
+        # 添加总计行
+        df.loc["总计"] = df.sum(axis=0)
+        
+        # 对于云南地区，添加耕地、园地合计
+        if self.is_yunnan:
+            # 定义排序顺序
+            desired_order = ['水田', '水浇地', '旱地', '耕地合计', '果园', '茶园', '橡胶园', '其他园地', '园地合计', '林地', '草地', '其他', '总计']
+            
+            # 检查是否存在水田、水浇地、旱地中的任意一个
+            cultivated_types = ['水田', '水浇地', '旱地']
+            existing_cultivated_types = [ctype for ctype in cultivated_types if ctype in df.index]
+            if existing_cultivated_types:
+                # 计算耕地合计
+                cultivated_total = df.loc[existing_cultivated_types].sum()
+                # 添加耕地合计行
+                df.loc["耕地合计"] = cultivated_total
+            
+            # 检查是否存在果园、茶园、橡胶园、其他园地中的任意一个
+            other_garden_types = ['果园', '茶园', '橡胶园', '其他园地']
+            existing_garden_types = [ctype for ctype in other_garden_types if ctype in df.index]
+            if existing_garden_types:
+                # 计算园地合计
+                other_garden_total = df.loc[existing_garden_types].sum()
+                # 添加园地合计行
+                df.loc["园地合计"] = other_garden_total
+            
+            # 过滤出实际存在的地类
+            actual_order = [item for item in desired_order if item in df.index]
+            
+            # 添加未在排序列表中的其他地类
+            for item in df.index:
+                if item not in actual_order and item != "总计":
+                    actual_order.append(item)
+            
+            # 重新排序
+            df = df.reindex(actual_order)
             
         return df
     
@@ -351,20 +410,68 @@ class SoilQualityReporter:
         
         # 写入数据
         for r_idx, (index, row) in enumerate(df.iterrows(), start=start_row+4):
+            if self.is_yunnan and index in ['水田','水浇地','旱地', '果园', '茶园', '橡胶园', '其他园地', '耕地合计', '园地合计']:
+                if index in ['耕地合计', '园地合计']:
+                    sheet.cell(row=r_idx, column=2, value="合计")
+                else:
+                    sheet.cell(row=r_idx, column=2, value=index)
+            else:
                 sheet.merge_cells(f"A{r_idx}:B{r_idx}")
                 sheet.cell(row=r_idx, column=1, value=index)
+
             for c_idx, value in enumerate(row, start=2):
                 sheet.cell(row=r_idx, column=c_idx+1, value=value)
 
+        # 对于云南地区，合并耕地行的A列
+        if self.is_yunnan:
+            # 查找水田、水浇地、旱地的行索引
+            cultivated_rows = []
+            other_garden_rows = []
+            for r_idx, (index, _) in enumerate(df.iterrows(), start=start_row+4):
+                if index in ['水田','水浇地','旱地']:
+                    cultivated_rows.append(r_idx)
+                if index in ['果园', '茶园', '橡胶园', '其他园地']:
+                    other_garden_rows.append(r_idx)
+            
+            # 合并耕地行的A列
+            if cultivated_rows:
+                start = cultivated_rows[0]
+                end = cultivated_rows[-1]
+                sheet.merge_cells(f"A{start}:A{end+1}")
+                sheet[f"A{start}"] = "耕地"
+            # 合并其他园地行的A列
+            if other_garden_rows:
+                start = other_garden_rows[0]
+                end = other_garden_rows[-1]
+                sheet.merge_cells(f"A{start}:A{end+1}")
+                sheet[f"A{start}"] = "园地"
+            
+
     def _apply_common_format(self, sheet, landuse_start_row):
         """应用通用格式"""
+        print("正在自动调整列宽...")
+        dims = {}
+        for row in sheet.rows:
+            for cell in row:
+                if cell.value:
+                    merged_range = next((range for range in sheet.merged_cells.ranges if cell.coordinate in range), None)
+                    if get_merge_type(merged_range) == 'column' or get_merge_type(merged_range) == 'both':
+                        continue
+                    if r'\n' in str(cell.value):
+                        continue
+                    # 获取字体大小
+                    font_size = cell.font.size
+                    cell_len = 0.7 * len(re.findall('([\u4e00-\u9fa5])', str(cell.value))) + len(str(cell.value)) * font_size
+                    # 计算列宽
+                    cell_len = cell_len / 8
+                    dims[cell.column] = max(dims.get(cell.column, 0), cell_len)
         # 设置列宽
-        for col in range(1, sheet.max_column + 1):
-            col_letter = chr(64 + col)
-            sheet.column_dimensions[col_letter].width = 14
+        for col, value in dims.items():
+            len_val = max(value+4,14)
+            sheet.column_dimensions[get_column_letter(int(col))].width = len_val
 
-        for row in range(5, sheet.max_row+1):
-            if row not in [landuse_start_row,landuse_start_row+1, landuse_start_row+2,landuse_start_row+3]:
+        for row in range(4, sheet.max_row+1):
+            if row not in [landuse_start_row,landuse_start_row+1, landuse_start_row+2]:
                 sheet.row_dimensions[row].height = 23
         
         # 定义边框样式
@@ -385,6 +492,10 @@ class SoilQualityReporter:
                 if cell.column == 1 and cell.row > 3 and cell.row not in (landuse_start_row, landuse_start_row+2): # 列A
                     cell.font = Font(bold=False, size=14)
 
+                # B列bold = False
+                if cell.column == 2 and cell.row > 3 and cell.row not in (landuse_start_row, landuse_start_row+2):
+                    cell.font = Font(bold=False, size=14)
+                
                 if cell.column == sheet.max_column and cell.row == landuse_start_row+1:
                     cell.font = Font(bold=False, size=14)
 
@@ -407,7 +518,6 @@ class SoilQualityReporter:
                 if cell.row >1 and cell.row not in (landuse_start_row, landuse_start_row+1):
                     cell.border = thin_border
 
-
 def read_arcgis_table(table_path):
     """将ArcGIS表格转换为Pandas DataFrame"""
     array = arcpy.da.TableToNumPyArray(table_path, "*")
@@ -431,7 +541,7 @@ def get_area_by_group(dltb_class_feature, excel_target_path, xzqmc, is_by_xzq=Fa
         # 读取目标面积Excel文件
         if xzqmc in yunnan_region:
             target_df = pd.read_excel(excel_target_path, sheet_name="Sheet2")
-            landuse_types = {'0101':'水田', '0102':'水浇地', '0103':'旱地', '02':'园地', '03':'林地', '04':'草地', '12':'其他'}
+            landuse_types = {'0101':'水田', '0102':'水浇地', '0103':'旱地', '0201':'果园', '0202':'茶园', '0203':'橡胶园', '0204':'其他园地', '03':'林地', '04':'草地', '12':'其他'}
         elif xzqmc in guangxi_region:
             target_df = pd.read_excel(excel_target_path, sheet_name="Sheet1")
             landuse_types = {'01':'耕地', '02':'园地', '03':'林地', '04':'草地', '12':'其他'}
@@ -607,8 +717,10 @@ def main():
                 .reset_index()
             )
         stats["adjusted_area"] = stats["temp_area"]
+
         # stats.to_csv("area_stats.csv", index=True)
        
+
         # 重命名列（按实际土壤分级字段调整）
         if soil_property == "PH" or soil_property == "TRRZ":
             grade_map = get_specail_map(output_settings["标准等级"])
@@ -624,7 +736,7 @@ def main():
         if xzqmc in guangxi_region:
             landuse_map = {"01": "耕地", "02": "园地", "03": "林地", "04": "草地", "12": "其他"}
         else:
-            landuse_map = {"0101": "水田", "0102": "水浇地", "0103": "旱地", "02": "园地", "03": "林地", "04": "草地", "12": "其他"}
+            landuse_map = {'0101':'水田', '0102':'水浇地', '0103':'旱地', '0201':'果园', '0202':'茶园', '0203':'橡胶园', '0204':'其他园地', '03':'林地', '04':'草地', '12':'其他'}
 
         # 平差处理
         excel_target_path = Path("tools/config_json/公布的变更调查平差面积.xlsx")  # 您的目标面积Excel文件路径

tools/core/stats_soil_prop_to_excel.py

@@ -16,6 +16,8 @@ import time
 import arcpy
 import argparse
 
+import numpy as np
+
 from tools.core.utils import 平差工具, common_utils
 
 sys.path.append(str(Path(__file__).parent))
@@ -91,32 +93,77 @@ def process_soil_property(args):
             print(f"缺少{soil_prop_name}的栅格或重分类要素，请检查输入文件路径是否正确！")
             return False
 
+        # 输出配置
+        temp_files = []
+
+        filtered_props = ['ECA', 'EMG', 'ACU', 'AZN', 'AFE', 'AMN', 'AMO', 'AB', 'AS1', 'TSE']
+        dltb_features = os.path.join(data_source_path, "地类图斑")
+
+        # 过滤地类图斑
+        temp_mask = r"in_memory/temp_dltb_features_filtered"
+        temp_files.append(temp_mask)
+        if soil_prop_name == "GZCHD":
+            arcpy.conversion.ExportFeatures(dltb_features, temp_mask, "DLBM LIKE '01%'")
+        if soil_prop_name in filtered_props:
+            arcpy.conversion.ExportFeatures(dltb_features, temp_mask, "DLBM LIKE '01%' OR DLBM LIKE '02%'")
+        else:
+            temp_mask = dltb_features
+        # 全区制图统计
+        try:
+            raster = arcpy.Raster(soil_prop_tif)
+            
+            # 按腌膜区域统计
+            out_raster = arcpy.sa.ExtractByMask(
+                in_raster=raster,
+                in_mask_data=temp_mask,
+                extraction_area="INSIDE"
+            )
+            # 转换为numpy数组进行计算
+            array = arcpy.RasterToNumPyArray(out_raster,nodata_to_value=9999)
+            
+            # 过滤掉NoData值
+            # 过滤NoData值和9999值
+            array = array[~np.isnan(array)]  # 过滤NoData
+            array = array[array != 9999]     # 过滤9999
+            array = array.astype(np.float64)
+            
+            stats_map = {
+                'min': round(np.min(array),2),
+                'max': round(np.max(array),2),
+                'mean': round(np.mean(array),2),
+                'median': round(np.median(array),2),
+                'std': round(np.std(array),2)
+            }
+        except Exception as e:
+            print(f"错误: {e}")
+
+        # =================================================================
         print(f"生成{soil_prop_name}的表1...")
         if soil_prop_name == "TRZD12":
             B1_TRZD12土壤属性分级分布.main(data_source_path, soil_prop_name, reclassed_features, dltb_features, output_path, target_area_all,xzqmc, prop_config)
         elif soil_prop_name == "TRZD":
             B1_TRZD土壤属性分级分布.main(data_source_path, soil_prop_name, reclassed_features, dltb_features, output_path, target_area_all,xzqmc, prop_config)
         else:
-            B1土壤属性分级分布.main(data_source_path, soil_prop_name, reclassed_features, dltb_features, soil_prop_tif, output_path, target_area_all,xzqmc, prop_config)
+            B1土壤属性分级分布.main(data_source_path, soil_prop_name, reclassed_features, dltb_features, stats_map, output_path, target_area_all,xzqmc, prop_config)
         time.sleep(2)
 
-        # print(f"生成{soil_prop_name}的表2...")
-        # if soil_prop_name == "TRZD12":
-        #     B2_TRZD12土地利用类型土壤属性.main(data_source_path, soil_prop_name, dltb_features, reclassed_features, output_path, target_area_df2, prop_config)
-        # elif soil_prop_name == "TRZD":
-        #     B2_TRZD土地利用类型土壤属性.main(data_source_path, soil_prop_name, dltb_features, reclassed_features, output_path, target_area_df2, prop_config)
-        # else:
-        #     B2土地利用类型土壤属性.main(data_source_path, soil_prop_name, dltb_features, soil_prop_tif, output_path, target_area_df2, prop_config)
-        # time.sleep(2)
+        print(f"生成{soil_prop_name}的表2...")
+        if soil_prop_name == "TRZD12":
+            B2_TRZD12土地利用类型土壤属性.main(data_source_path, soil_prop_name, dltb_features, reclassed_features, output_path, target_area_df2, prop_config)
+        elif soil_prop_name == "TRZD":
+            B2_TRZD土地利用类型土壤属性.main(data_source_path, soil_prop_name, dltb_features, reclassed_features, output_path, target_area_df2, prop_config)
+        else:
+            B2土地利用类型土壤属性.main(data_source_path, soil_prop_name, dltb_features, soil_prop_tif, output_path, target_area_df2, prop_config,stats_map)
+        time.sleep(2)
 
-        # print(f"生成{soil_prop_name}的表3...")
-        # if soil_prop_name == "TRZD12":
-        #     B3_TRZD12不同土壤类型土壤属性.main(data_source_path,soil_prop_name,trlx_features,reclassed_features,output_path,target_area_df1,prop_config)
-        # elif soil_prop_name == "TRZD":
-        #     B3_TRZD不同土壤类型土壤属性.main(data_source_path, soil_prop_name, trlx_features, reclassed_features, output_path, target_area_df1, prop_config)
-        # else:
-        #     B3不同土壤类型土壤属性.main(data_source_path, soil_prop_name, trlx_features, soil_prop_tif, output_path, target_area_df1, prop_config, dltb_features)
-        # time.sleep(2)
+        print(f"生成{soil_prop_name}的表3...")
+        if soil_prop_name == "TRZD12":
+            B3_TRZD12不同土壤类型土壤属性.main(data_source_path,soil_prop_name,trlx_features,reclassed_features,output_path,target_area_df1,prop_config)
+        elif soil_prop_name == "TRZD":
+            B3_TRZD不同土壤类型土壤属性.main(data_source_path, soil_prop_name, trlx_features, reclassed_features, output_path, target_area_df1, prop_config)
+        else:
+            B3不同土壤类型土壤属性.main(data_source_path, soil_prop_name, trlx_features, soil_prop_tif, output_path, target_area_df1, prop_config, dltb_features,stats_map)
+        time.sleep(2)
 
         print(f"生成{soil_prop_name}的历史对比...")
         # if arcpy.Exists(history_reclassed_features) and arcpy.Exists(history_raster) and arcpy.Exists(history_samples_path):
@@ -129,6 +176,11 @@ def process_soil_property(args):
     except Exception as e:
         print(f"处理{soil_prop_name}时出错: {str(e)}")
         return False
+    finally:
+        # 清理临时文件
+        temp_files_processor.clean_up_temp_files(temp_files)
+        import gc
+        gc.collect()
 
 def main():
 

tools/ui/config/settings.json

@@ -33,7 +33,7 @@
         "config_file_path": "D:/ProgramData/ArcGis_Py/tools/config_json/广西_华南_地块_config.json",
         "xzq_features": "E:\\@三普属性图出图\\广西银海区\\银海区土壤属性制表\\土壤属性制表数据.gdb\\行政区划",
         "dltb_features": "E:\\@三普属性图出图\\广西银海区\\银海区土壤属性制表\\土壤属性制表数据.gdb\\地类图斑",
-        "xzqmc": "西畴县",
+        "xzqmc": "",
         "is_by_xzq": false
     },
     "acid_stat_settings": {
@@ -49,12 +49,16 @@
         "xzqmc": ""
     },
     "soil_prop_stat_settings": {
-        "xzqmc": "",
+        "xzqmc": "银海区",
         "config_file": "D:/ProgramData/ArcGis_Py/tools/config_json/广西_华南_地块_config.json",
-        "data_source_path": "E:/@三普属性图出图/广西武鸣区/武鸣区报告图表/制表数据.gdb",
-        "sanpu_prop_tif_folder": "E:/@三普属性图出图/广西武鸣区/@基础数据/三普栅格/投影后",
-        "reclassed_feature_folder": "E:/@三普属性图出图/广西武鸣区/过程数据/三普重分类/面积统计用栅格面",
-        "output_folder": "E:/@三普属性图出图/广西武鸣区/武鸣区报告图表",
-        "sample_list": []
+        "data_source_path": "E:/@三普属性图出图/广西银海区/银海区土壤属性制表/土壤属性制表数据.gdb",
+        "sanpu_prop_tif_folder": "E:/@三普属性图出图/广西北海市/@基础数据/三普栅格/投影后",
+        "reclassed_feature_folder": "E:/@三普属性图出图/广西北海市/过程数据/面积统计用栅格面",
+        "output_folder": "E:/@三普属性图出图/测试",
+        "sample_list": [
+            "CEC",
+            "AS1",
+            "AP"
+        ]
     }
 }

tools/ui/tabs/area_stat_tab.py

@@ -26,6 +26,14 @@ def yunnan_dlbm(dlbm):
         return "0102"
     elif dlbm.startswith("0103"):
         return "0103"
+    elif dlbm.startswith("0201"):
+        return "0201"
+    elif dlbm.startswith("0202"):
+        return "0202"
+    elif dlbm.startswith("0203"):
+        return "0203"
+    elif dlbm.startswith("0204"):
+        return "0204"
     else:
         return dlbm[:2]
 """