初始化

tools/core/utils/平差工具.py

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+# 获取每个一级地类面积，主要是12类
+import arcpy
+import numpy as np
+import pandas as pd
+from .math_utils import correct_rounding_error
+
+
+# 获取目标面积
+def get_area_by_group(dltb_class_feature, excel_target_path, xzqmc, is_by_xzq=False):
+    try:
+        # 读取目标面积Excel文件
+        target_df = pd.read_excel(excel_target_path)
+        
+        # 确保列名匹配
+        target_df.columns = target_df.columns.str.strip()
+        
+        if is_by_xzq:
+            # 地类编码映射字典
+            land_type_mapping = {
+                '耕地': '01',
+                '园地': '02', 
+                '林地': '03',
+                '草地': '04',
+                '其他地类': '12'
+            }
+
+            # 方法1：重命名列后转换为字典
+            df_encoded = target_df.rename(columns=land_type_mapping)
+            result_dict = df_encoded.set_index('行政单位').to_dict('index')
+
+            return result_dict
+        
+        # 检查要素类是否存在
+        if not arcpy.Exists(dltb_class_feature):
+            print(f"警告：输入要素类不存在: {dltb_class_feature}")
+        else:
+            # 转为numpy数组供pandas统计使用
+            df = pd.DataFrame(arcpy.da.TableToNumPyArray(dltb_class_feature, ["YJDLBM", "TBDLMJ"], skip_nulls=False, null_value=np.nan))
+            qtdl_df = df[df['YJDLBM'] == '12']
+            if qtdl_df['TBDLMJ'].isnull().any() or qtdl_df['TBDLMJ'].eq(0).any():
+                print("警告：其他地类TBDLMJ字段 存在空值或无效的记录，将不平差其他地类")
+                target_areas = {}
+            else:
+                area_by_group = df.groupby("YJDLBM")["TBDLMJ"].sum()
+
+                for key in area_by_group.keys():
+                    area_by_group[key] = area_by_group[key] * 0.0015
+
+                target_areas = area_by_group.to_dict()
+        
+            # 获取铁山港区的目标面积
+            gangnan_target = target_df[target_df['行政单位'] == xzqmc]
+            
+            if gangnan_target.empty:
+                print(f"警告：未找到{xzqmc}的目标面积数据,将使用TBDLMJ数据进行平差")
+                return target_areas
+            
+            # 提取各土地利用类型的目标面积
+            landuse_types = {'01':'耕地', '02':'园地', '03':'林地', '04':'草地', '12':'其他地类'}
+            
+            for dlbm, dlmc in landuse_types.items():
+                if dlmc in gangnan_target.columns:
+                    if gangnan_target[dlmc].values[0] and not np.isnan(gangnan_target[dlmc].values[0]):
+                        target_areas[dlbm] = gangnan_target[dlmc].values[0]
+
+            return target_areas
+ 
+    except Exception as e:
+        print(f"计算面积时出错: {str(e)}")
+        return {}
+
+# 按地类平差（全区统一平差）
+def adjust_area_statistics(stats_df, target_areas):
+    """
+    根据Excel中的目标面积对统计数据进行平差处理
+    
+    Parameters:
+    stats_df: 原始统计数据DataFrame
+    excel_target_path: 包含目标面积的Excel文件路径
+    
+    Returns:
+    adjusted_df: 平差后的DataFrame
+    """
+    try:
+        if target_areas is None:
+            print("警告：目标面积数据为空，不进行平差")
+            return stats_df
+        
+        # 准备平差数据
+        adjusted_df = stats_df.copy()
+        if "YJDLBM" not in adjusted_df.columns:
+            dlbm = "YNDLBM"
+        else:
+            dlbm = "YJDLBM"
+        
+        adjusted_df['adjusted_area'] = adjusted_df['temp_area']
+        adjusted_df['adjustment_factor'] = 1.0
+
+        # 计算每个地类的原始总面积
+        original_totals = stats_df.groupby(dlbm)['temp_area'].sum().to_dict()
+
+        # 对每个地类进行平差
+        for yjdl, target_area in target_areas.items():
+            if (yjdl in original_totals and original_totals[yjdl] > 0) or target_area > 0:
+                adjustment_factor = target_area / original_totals[yjdl]
+                
+                # 应用平差系数
+                mask = adjusted_df[dlbm] == yjdl
+                adjusted_df.loc[mask, 'adjusted_area'] = adjusted_df.loc[mask, 'temp_area'] * adjustment_factor
+                adjusted_df.loc[mask, 'adjustment_factor'] = adjustment_factor
+                
+                # 应用误差矫正，确保总和等于目标值
+                adjusted_areas = adjusted_df.loc[mask, 'adjusted_area'].tolist()
+                original_areas = stats_df.loc[mask, 'temp_area'].tolist()
+                corrected_areas = correct_rounding_error(target_area, adjusted_areas, original_areas)
+                adjusted_df.loc[mask, 'adjusted_area'] = corrected_areas
+                
+                print(f"地类 {yjdl}: 平差系数 = {adjustment_factor:.6f}, 目标面积 = {target_area}, 矫正后总面积 = {sum(corrected_areas)}")
+
+        return adjusted_df
+        
+    except Exception as e:
+        print(f"平差处理失败: {e}")
+        return stats_df
+
+# 按行政区+地类进行平差
+def adjust_by_district_landuse(stats_df:pd.DataFrame, target_areas_dict:dict):
+    """
+    按行政区+地类进行平差
+    
+    Parameters:
+    stats_df: 原始统计数据DataFrame
+    target_areas_dict: 目标面积字典，格式：{'行政区': {'地类': 目标面积}}
+    
+    Returns:
+    adjusted_df: 平差后的DataFrame
+    """
+    # 复制原始数据
+    adjusted_df = stats_df.copy()
+    adjusted_df['adjusted_area'] = adjusted_df['temp_area']
+    adjusted_df['adjustment_factor'] = 1.0
+    
+    # 获取所有存在的行政区和地类
+    existing_districts = adjusted_df['XZQMC'].unique()
+    
+    # 检查目标字典中的行政区是否存在
+    missing_districts = []
+    tt = [td for td in target_areas_dict.keys()]
+    for ed in existing_districts:
+        if ed not in tt:
+            missing_districts.append(ed)
+    
+    # 如果有行政区不存在，返回原始数据并提示
+    if missing_districts:
+        print(f"警告：平差数据中不存在行政区: {missing_districts}，未进行平差")
+        return stats_df
+    
+    # 计算每个行政区每个地类的原始总面积
+    original_totals = stats_df.groupby(['XZQMC', 'YJDLBM'])['temp_area'].sum()
+    
+    # 对每个行政区的每个地类进行平差
+    for xzqmc, landuse_targets in target_areas_dict.items():
+        for yjdl, target_area in landuse_targets.items():
+            # 检查该行政区是否有此地类数据
+            if (xzqmc, yjdl) in original_totals.index and original_totals.at[(xzqmc, yjdl)] > 0:
+                adjustment_factor = target_area / original_totals[(xzqmc, yjdl)]
+                
+                # 应用平差系数
+                mask = (adjusted_df['XZQMC'] == xzqmc) & (adjusted_df['YJDLBM'] == yjdl)
+                adjusted_df.loc[mask, 'adjusted_area'] = adjusted_df.loc[mask, 'temp_area'] * adjustment_factor
+                adjusted_df.loc[mask, 'adjustment_factor'] = adjustment_factor
+                
+                # 应用误差矫正，确保总和等于目标值
+                adjusted_areas = adjusted_df.loc[mask, 'adjusted_area'].tolist()
+                original_areas = stats_df.loc[mask, 'temp_area'].tolist()
+                corrected_areas = correct_rounding_error(target_area, adjusted_areas, original_areas)
+                adjusted_df.loc[mask, 'adjusted_area'] = corrected_areas
+                
+                print(f"{xzqmc} - 地类 {yjdl}: 平差系数 = {adjustment_factor:.6f}, 目标面积 = {target_area}, 矫正后总面积 = {sum(corrected_areas)}")
+    
+    return adjusted_df
+
+
+def get_target_areas(excel_path:str, sheet_name:str, xzqmc:str) -> pd.DataFrame:
+    df_excel = pd.read_excel(excel_path, sheet_name)
+    target_df = df_excel[df_excel['行政单位'] == xzqmc]
+
+    df_area_for_merge = target_df.set_index('行政单位').iloc[0].reset_index(name='面积').rename(columns={'index': 'EJDL'})
+
+    return df_area_for_merge
+
+def get_target_areas_by_group(excel_target_path):
+    # 读取目标面积Excel文件
+    target_df = pd.read_excel(excel_target_path,"Sheet1")
+    
+    # 确保列名匹配
+    target_df.columns = target_df.columns.str.strip()
+
+    result_dict = target_df.set_index('行政单位').to_dict('index')
+
+    return result_dict