# 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