# -*- coding: utf-8 -*- import os import arcpy import pandas as pd import numpy as np from openpyxl import Workbook from openpyxl.styles import Font from openpyxl.utils import get_column_letter from tools.config.arcgis_field_cal_code import codeblock_cal_shfj from tools.core.utils.excel_utils import ExcelStyleUtils yjdl_order = ["耕地", "园地", "林地", "草地", "其他"] ejdl_order = ["水田", "旱地", "水浇地", "果园", "茶园", "橡胶园", "其他园地"] # --- 2. 辅助函数 --- # 等级计算 def get_acidification_degree(delta_ph): """根据ΔpH值判断酸化程度""" if pd.isna(delta_ph) or delta_ph == 0: return "-" # 请根据您的实际分级标准调整这里的阈值 if delta_ph > 1.0: return "重度酸化" elif 0.5 < delta_ph <= 1.0: return "中度酸化" elif 0.3 < delta_ph <= 0.5: return "轻度酸化" elif 0.1 < delta_ph <= 0.3: return "弱酸化" else: # dPH < -0.3 return "其他" # --- 3. 数据处理与分析 均值--- def process_data_for_table5_7(gdb_path, mean_table_name, sample_table_name): """ 【最终版 v2】: 增加对制图样点数的处理，以支持加权平均计算。 """ print("开始处理数据...") def clean_df(df, columns): for col in columns: df[col] = df[col].astype(str).str.strip() df.replace(['', 'None', '', '<空>'], np.nan, inplace=True) df.dropna(subset=columns, inplace=True) return df # --- a. 处理样点数据，计算“样点均值” --- print("--> 步骤1: 计算样点均值...") sample_table_path = os.path.join(gdb_path, sample_table_name) sample_fields = ['XZQMC','YJDL','EJDL', 'dPH'] df_samples = pd.DataFrame(arcpy.da.TableToNumPyArray(sample_table_path, sample_fields, 'dPH>0.3', skip_nulls=False)) df_samples = clean_df(df_samples, ['XZQMC','YJDL', 'EJDL']) # 按 YJDL, EJDL 分组，计算 dPH 的均值 df_sample_means = df_samples.groupby(['XZQMC'])['dPH'].mean().reset_index() df_sample_means.rename(columns={'dPH': '样点均值'}, inplace=True) print("样点均值计算完成。") # --- b. 处理制图数据，获取“制图均值”和“制图样点数” --- print("--> 步骤2: 获取制图均值和样点数...") mean_table_path = os.path.join(gdb_path, mean_table_name) mean_fields = ['XZQMC', 'MEAN', 'COUNT'] df_map_data = pd.DataFrame(arcpy.da.TableToNumPyArray(mean_table_path, mean_fields, skip_nulls=False)) df_map_data = clean_df(df_map_data, ['XZQMC']) df_map_data.rename(columns={'MEAN': '制图均值', 'COUNT': '制图样点数'}, inplace=True) print("制图数据获取完成。") # --- c. 合并数据 --- print("--> 步骤3: 合并数据...") df_skeleton = pd.concat([ df_sample_means[['XZQMC']], df_map_data[['XZQMC']] ]).drop_duplicates().reset_index(drop=True) df_final = pd.merge(df_skeleton, df_sample_means, on=['XZQMC'], how='left') # **【核心修改】: 合并整个 df_map_data，而不仅仅是均值列** df_final = pd.merge(df_final, df_map_data, on=['XZQMC'], how='left') # --- d. 计算酸化程度 --- print("--> 步骤4: 计算酸化程度...") # **【核心修改】: 在计算酸化程度之前，先过滤掉不展示的行** # 我们只对 dPH 在酸化范围内 ( > 0.3) 的数据感兴趣 # 但为了计算合计，我们需要保留所有数据，所以这一步只计算，不删除 df_final['酸化程度_样本'] = df_final['样点均值'].apply(get_acidification_degree) df_final['酸化程度_制图'] = df_final['制图均值'].apply(get_acidification_degree) df_final.sort_values(['XZQMC'], inplace=True) print("数据处理流程完成！") return df_final # --- 4. Excel 制表 均值--- def write_to_excel_table5_7(df, output_path): """ 将处理好的数据写入格式化的 Excel 文件。 """ if df.empty: print("警告: 没有数据可以写入 Excel。") return print(f"开始生成 Excel 报告到 '{output_path}'...") wb = Workbook() ws = wb.create_sheet("Mysheet", 0) ws.title = "不同土地利用类型pH变化统计" # --- b. 绘制表头 --- ws.merge_cells('A1:A2'); ws['A1'] = '乡镇/街道' ws.merge_cells('B1:E1'); ws['B1'] = 'ΔpH' ws['B2'] = '样点均值' ws['C2'] = '酸化程度' ws['D2'] = '制图均值' ws['E2'] = '酸化程度' # --- c. 填充数据 --- current_row = 3 # **【核心修改】: 先对整个DataFrame进行过滤，只保留需要展示的行** acid_levels_to_show = ["弱酸化", "轻度酸化", "中度酸化", "重度酸化", "其他"] df_to_write = df[ df['酸化程度_样本'].isin(acid_levels_to_show) | df['酸化程度_制图'].isin(acid_levels_to_show) ].copy() # 使用 .copy() 避免 SettingWithCopyWarning for _, row_data in df_to_write.iterrows(): print(f"正在写入一级地类...") # 写入数据” ws.cell(row=current_row, column=1).value = row_data['XZQMC'] # 填充样点数据 sample_mean = row_data.get('样点均值') if pd.notna(sample_mean): ws.cell(row=current_row, column=2).value = f"{sample_mean:.2f}" if sample_mean > 0.3 else "-" ws.cell(row=current_row, column=3).value = row_data.get('酸化程度_样本', '-') if sample_mean > 0.3 else "-" else: ws.cell(row=current_row, column=2).value = "-" ws.cell(row=current_row, column=3).value = "-" # 填充制图数据 map_mean = row_data.get('制图均值') if pd.notna(map_mean): ws.cell(row=current_row, column=4).value = f"{map_mean:.2f}" if map_mean > 0.3 else "-" ws.cell(row=current_row, column=5).value = row_data.get('酸化程度_制图', '-') if map_mean > 0.3 else "-" else: ws.cell(row=current_row, column=4).value = "-" ws.cell(row=current_row, column=5).value = "-" current_row += 1 # --- a. 定义样式 --- header_font = Font(name='等线', size=11, bold=True) # --- d. 应用样式和调整列宽 --- max_col_letter = get_column_letter(ws.max_column) if current_row > 1: # 确保有数据才应用样式 ExcelStyleUtils.set_style(ws, f'A1:{max_col_letter}{current_row-1}') ExcelStyleUtils.set_style(ws, f'A1:{max_col_letter}2', header_font) print("正在自动调整列宽...") # 设置列宽 ExcelStyleUtils.auto_adjust_column_width(ws) # --- e. 保存文件 --- wb.save(output_path) print("Excel 报告生成成功！") # --- 2. 数据处理与分析 面积 各乡镇--- def process_data_for_table5_4(gdb_path, area_table_name, target_area_dict): """ 【最终修正版 v2】: 先建立统一的层级结构，再分别合并统计结果。 """ print("【最终修正版 v2】开始处理数据...") def clean_df(df, columns): for col in columns: df[col] = df[col].astype(str).str.strip() df.replace(['', 'None', '', '<空>'], np.nan, inplace=True) df.dropna(subset=columns, inplace=True) return df # --- a. 独立统计面积数据 --- print("--> 步骤1: 独立统计面积数据...") area_table_path = os.path.join(gdb_path, area_table_name) df_area = pd.DataFrame(arcpy.da.TableToNumPyArray(area_table_path, ['XZQMC', 'SHFJ', 'AREA'], skip_nulls=False)) df_area = clean_df(df_area, ['XZQMC']) df_final = pd.DataFrame() if not df_area.empty: # 计算平差系数 target_shfj_areas = target_area_dict.groupby(['SHFJ'])['AREA_MU'].sum().reset_index() original_shfj_areas = df_area.groupby(['SHFJ'])['AREA'].sum().reset_index() original_shfj_areas['AREA_MU'] = original_shfj_areas['AREA'] * 0.0015 adjustment_factors = [] for index, row in original_shfj_areas.iterrows(): shfj = row['SHFJ'] area_mu = row['AREA_MU'] adjustment_factor = target_shfj_areas[target_shfj_areas['SHFJ'] == shfj]['AREA_MU'].values[0] / area_mu adjustment_factors.append({ 'SHFJ': shfj, '平差系数':adjustment_factor }) factor_df = pd.DataFrame(adjustment_factors) df_sh_area = df_area.merge(factor_df[['SHFJ', '平差系数']], on='SHFJ') df_sh_area['制图面积_亩'] = df_sh_area['AREA'] * 0.0015 * df_sh_area['平差系数'] ts_total_area = df_sh_area.groupby(['XZQMC'])['制图面积_亩'].transform('sum') df_sh_area['面积占比'] = (df_sh_area['制图面积_亩'] / ts_total_area) * 100 df_area_stats = df_sh_area.pivot_table( index=['XZQMC'], columns='SHFJ', values=['制图面积_亩', '面积占比'], fill_value=0 ).reset_index() df_area_stats.columns = [f'{col[0]}_{col[1]}'.strip('_') if col[1] else col[0] for col in df_area_stats.columns] df_final = df_area_stats print("--> 步骤2: 计算酸化面积合计...") # 定义属于酸化类别的面积列 acidic_area_cols = [ '制图面积_亩_轻度酸化', '制图面积_亩_中度酸化', '制图面积_亩_重度酸化' ] # 确保这些列存在于DataFrame中，不存在的列用0代替 for col in acidic_area_cols: if col not in df_final.columns: df_final[col] = 0 # 将这三列相加，得到合计值 df_final['酸化面积合计_亩'] = df_final[acidic_area_cols].sum(axis=1) # --- d. 最后清理和构建映射 --- df_final.fillna(0, inplace=True) print("数据处理流程完成！") return df_final # --- 3. Excel 制表 面积--- def write_to_excel_table5_4(df, output_path): """ 【最终修正版】: 将处理好的数据写入格式化的 Excel 文件。 """ if df.empty: print("警告: 没有数据可以写入 Excel，将创建一个空的报告。") wb = Workbook() ws = wb.create_sheet("Mysheet", 0) ws.title = "不同乡镇酸化面积统计" ws['A1'] = "没有有效的统计数据。" wb.save(output_path) return print(f"开始生成 Excel 报告到 '{output_path}'...") wb = Workbook() ws = wb.create_sheet("Mysheet", 0) ws.title = "不同乡镇酸化面积统计" # --- b. 绘制表头 (不变) --- ws.merge_cells('A1:A2'); ws['A1'] = '乡镇/街道' acid_levels = ['弱酸化', '轻度酸化', '中度酸化', '重度酸化', '其他'] # acid_level_headers = ['0.1<ΔpH≤0.3', '0.3<ΔpH≤0.5', '0.5<ΔpH≤1.0', 'ΔpH>1.0', '其他'] # all_possible_levels = ['碱化', '未酸化', '轻度酸化', '中度酸化', '重度酸化'] acid_level_headers = ['弱酸化(0.1<ΔpH≤0.3)','轻度酸化(0.3<ΔpH≤0.5)', '中度酸化(0.5<ΔpH≤1.0)', '重度酸化(ΔpH>1.0)', '其他(未酸化)'] col_start = 2 for header in acid_level_headers: ws.merge_cells(start_row=1, start_column=col_start, end_row=1, end_column=col_start + 1) ws.cell(row=1, column=col_start).value = header ws.cell(row=2, column=col_start).value = '面积/亩' ws.cell(row=2, column=col_start + 1).value = '占比/%' col_start += 2 # 增加合计列的表头** total_col = col_start # 记录合计列的列号 ws.merge_cells(start_row=1, start_column=total_col, end_row=2, end_column=total_col) ws.cell(row=1, column=total_col).value = '酸化面积合计' # --- c. 填充数据 (完全重构的逻辑) --- current_row = 3 # **【核心修改】: 不再需要 group_yl_df，直接遍历整个 df** # 假设 df 已经按 XZQMC 排序（如果需要的话） df_sorted = df.sort_values('XZQMC').reset_index(drop=True) for index, row_data in df_sorted.iterrows(): ws.cell(row=current_row, column=1).value = row_data['XZQMC'] col_start = 2 for level in acid_levels: area_col = f'制图面积_亩_{level}' area_pct_col = f'面积占比_{level}' area_val = row_data.get(area_col, 0) area_pct_val = row_data.get(area_pct_col, 0) ws.cell(row=current_row, column=col_start).value = f"{area_val:.0f}" if area_val > 0 else "-" ws.cell(row=current_row, column=col_start + 1).value = f"{area_pct_val:.2f}%" if area_val > 0 else "-" col_start += 2 # **【核心修改】: 填充酸化面积合计列的值** total_area_val = row_data.get('酸化面积合计_亩', 0) ws.cell(row=current_row, column=total_col).value = f"{total_area_val:.0f}" if total_area_val > 0 else "-" current_row += 1 # **(可选) 增加一个所有乡镇的“总合计”行** # print("--> 计算并写入总合计行...") # ws.cell(row=current_row, column=1).value = '总合计' # col_start = 2 # for level in acid_levels: # area_col = f'制图面积_亩_{level}' # area_sum = df_sorted.get(area_col, 0).sum() # # 总合计行的占比是相对于所有乡镇的总面积 # grand_total_area = df_sorted[[f'制图面积_亩_{lvl}' for lvl in all_possible_levels if f'制图面积_亩_{lvl}' in df_sorted]].sum().sum() # area_perc = (area_sum / grand_total_area * 100) if grand_total_area > 0 else 0 # ws.cell(row=current_row, column=col_start).value = f"{area_sum:.2f}" if area_sum > 0 else "-" # ws.cell(row=current_row, column=col_start + 1).value = f"{area_perc:.2f}" if area_sum > 0 else "-" # col_start += 2 # grand_total_acidic_area = df_sorted['酸化面积合计_亩'].sum() # ws.cell(row=current_row, column=total_col).value = f"{grand_total_acidic_area:.2f}" if grand_total_acidic_area > 0 else "-" # current_row += 1 # --- a. 定义样式 (不变) --- header_font = Font(name='等线', size=11, bold=True) # --- d. 应用样式和调整列宽 (最终健壮版) --- max_col_letter = get_column_letter(ws.max_column) if current_row > 1: # 确保有数据才应用样式 ExcelStyleUtils.set_style(ws, f'A1:{max_col_letter}{current_row-1}') ExcelStyleUtils.set_style(ws, f'A1:{max_col_letter}2', header_font) print("正在自动调整列宽...") # 设置列宽 ExcelStyleUtils.auto_adjust_column_width(ws) # --- e. 保存文件 --- wb.save(output_path) print("Excel 报告生成成功！") # 步骤5.3: 生成表5.3 - 总表数据处理 def process_data_for_table5_2(gdb_path, area_table_name, sample_table_name, target_area_dict:pd.DataFrame): def clean_df(df, columns): for col in columns: df[col] = df[col].astype(str).str.strip() df.replace(['', 'None', '', '<空>'], np.nan, inplace=True) df.dropna(subset=columns, inplace=True) return df # --- a. 从两个表中提取并建立唯一的 (YJDL, EJDL) 层级结构 "骨架" --- print("--> 步骤1: 建立统一的层级结构...") sample_table_path = os.path.join(gdb_path, sample_table_name) area_table_path = os.path.join(gdb_path, area_table_name) # --- b. 独立统计样点数据 --- print("--> 步骤2: 独立统计样点数据...") df_samples = pd.DataFrame(arcpy.da.TableToNumPyArray(sample_table_path, ['XZQMC', 'dPH'], skip_nulls=False)) df_samples = clean_df(df_samples, ['XZQMC']) if not df_samples.empty: bins = [-np.inf, 0.1, 0.3, 0.5, 1.0, np.inf] labels = ["其他", "弱酸化", "轻度酸化", "中度酸化", "重度酸化"] df_samples['SHFJ'] = pd.cut(df_samples['dPH'], bins=bins, labels=labels, right=True) sample_counts = df_samples.groupby(['SHFJ'], observed=False).size().reset_index(name='样点数') sample_counts = sample_counts.merge(df_samples.groupby(['SHFJ'], observed=False)['dPH'].mean(), on='SHFJ') ts_total_samples = sample_counts['样点数'].sum() sample_counts['样点占比'] = (sample_counts['样点数'] / ts_total_samples) * 100 # print(sample_counts) # --- c. 独立统计面积数据 --- print("--> 步骤3: 独立统计面积数据...") df_area = pd.DataFrame(arcpy.da.TableToNumPyArray(area_table_path, ['XZQMC', 'SHFJ', 'AREA'], skip_nulls=False)) df_area = clean_df(df_area, ['XZQMC']) if not df_area.empty: # 计算平差系数 target_shfj_areas = target_area_dict.groupby(['SHFJ'])['AREA_MU'].sum().reset_index() original_shfj_areas = df_area.groupby(['SHFJ'])['AREA'].sum().reset_index() original_shfj_areas['AREA_MU'] = original_shfj_areas['AREA'] * 0.0015 adjustment_factors = [] for index, row in original_shfj_areas.iterrows(): shfj = row['SHFJ'] area_mu = row['AREA_MU'] adjustment_factor = target_shfj_areas[target_shfj_areas['SHFJ'] == shfj]['AREA_MU'].values[0] / area_mu adjustment_factors.append({ 'SHFJ': shfj, '平差系数':adjustment_factor }) factor_df = pd.DataFrame(adjustment_factors) df_sh_area = df_area.merge(factor_df[['SHFJ', '平差系数']], on='SHFJ') df_sh_area['制图面积_亩'] = df_sh_area['AREA'] * 0.0015 * df_sh_area['平差系数'] df_area_counts = df_sh_area.groupby(['SHFJ'], observed=False)[['制图面积_亩']].sum() ts_total_area = df_area_counts['制图面积_亩'].sum() df_area_counts['面积占比'] = (df_area_counts['制图面积_亩'] / ts_total_area) * 100 df_final = pd.merge(sample_counts, df_area_counts, on=['SHFJ'], how='left') # # --- d. 最后清理和构建映射 --- df_final.fillna(0, inplace=True) return df_final # --- 3. Excel 制表 总表--- def write_to_excel_table5_2(df, df_mean, output_path): """ 【最终修正版】: 将处理好的数据写入格式化的 Excel 文件。 """ if df.empty: print("警告: 没有数据可以写入 Excel，将创建一个空的报告。") wb = Workbook() ws = wb.create_sheet("Mysheet", 0) ws['A1'] = "没有有效的统计数据。" wb.save(output_path) return print(f"开始生成 Excel 报告到 '{output_path}'...") wb = Workbook() ws = wb.create_sheet("Mysheet", 0) ws.title = "行政区酸化程度等级分布及占比" # --- b. 绘制表头 (不变) --- ws.merge_cells('A1:B1'); ws['A1'] = '酸化程度' ws.merge_cells('C1:D1'); ws['C1'] = '样点统计' ws.merge_cells('E1:F1'); ws['E1'] = '制图统计' ws.merge_cells('A8:B8'); ws['A8'] = '总计' ws.merge_cells('A9:B9'); ws['A9'] = '全县酸化样点ΔpH 均值' ws.merge_cells('A10:B10'); ws['A10'] = '全县酸化制图ΔpH 均值' ws['A2'] = '分级'; ws['B2'] = '值域' ws['C2'] = '数量/个'; ws['D2'] = '占比' ws['E2'] = '面积/亩'; ws['F2'] = '占比' acid_levels = ['弱酸化', '轻度酸化', '中度酸化', '重度酸化', '其他'] acid_level_headers = ['0.1<ΔpH≤0.3', '0.3<ΔpH≤0.5', '0.5<ΔpH≤1.0', 'ΔpH>1.0', '未酸化'] # --- c. 填充数据 --- current_row = 3 # 1. 遍历该一级地类下的所有“二级地类”并写入数据 for index,level in enumerate(acid_levels): ws.cell(row=current_row, column=1).value = level ws.cell(row=current_row, column=2).value = acid_level_headers[index] # 在子集中查找当前二级地类的数据行 row_data = df[df['SHFJ'] == level] # --- 填充单元格的逻辑开始 --- col_start = 3 # 从第 C 列开始填充 # 检查是否找到了该土属的数据 if not row_data.empty: data_series = row_data.iloc[0] # 1. 构建要从 data_series 中查找的列名 sample_col = f'样点数' sample_pct_col = f'样点占比' area_col = f'制图面积_亩' area_pct_col = f'面积占比' # 2. 从 data_series 中安全地获取值 sample_val = data_series.get(sample_col, 0) sample_pct_val = data_series.get(sample_pct_col, 0) area_val = data_series.get(area_col, 0) area_pct_val = data_series.get(area_pct_col, 0) # 3. 将获取到的值填入单元格 ws.cell(row=current_row, column=col_start).value = f"{sample_val:.0f}" if sample_val > 0 else "-" # 占比/% ws.cell(row=current_row, column=col_start + 1).value = f"{sample_pct_val:.2f}%" if sample_val > 0 else "-" # 制图面积/亩 ws.cell(row=current_row, column=col_start + 2).value = f"{area_val:.0f}" if area_val > 0 else "-" # 占比/% ws.cell(row=current_row, column=col_start + 3).value = f"{area_pct_val:.2f}%" if area_val > 0 else "-" # 移动到下一个酸化等级的起始列 col_start += 2 else: for _ in range(4): ws.cell(row=current_row, column=col_start).value = "-" col_start += 1 current_row += 1 # 合计单元格填充 mask = df["SHFJ"].isin(acid_levels) df_acid = df[mask] weighted_avg = (df_acid["dPH"] * df_acid["样点数"]).sum() / df_acid["样点数"].sum() mean_msk = df_mean["酸化程度_制图"].isin(acid_levels) df_mean_acid = df_mean[mean_msk] weighted_mean = (df_mean_acid["制图均值"] * df_mean_acid["制图样点数"]).sum() / df_mean_acid["制图样点数"].sum() ws.merge_cells('C9:F9') ws.merge_cells('C10:F10') ws['C8'] = df[df['SHFJ'].isin(acid_levels)]['样点数'].sum() ws['D8'] = f"{df[df['SHFJ'].isin(acid_levels)]['样点占比'].sum():.2f}%" ws['E8'] = f"{df[df['SHFJ'].isin(acid_levels)]['制图面积_亩'].sum():.0f}" ws['F8'] = f"{df[df['SHFJ'].isin(acid_levels)]['面积占比'].sum():.2f}%" ws['C9'] = f"{weighted_avg:.2f}" # type: ignore ws['C10'] = f"{weighted_mean:.2f}" # --- a. 定义样式 (不变) --- header_font = Font(name='宋体', size=11) # --- d. 应用样式和调整列宽 (最终健壮版) --- if current_row > 1: # 确保有数据才应用样式 ExcelStyleUtils.set_style(ws, f'A1:F10') ExcelStyleUtils.set_style(ws, f'A1:F2', header_font) print("正在自动调整列宽...") # 设置列宽 ExcelStyleUtils.auto_adjust_column_width(ws) # --- e. 保存文件 --- wb.save(output_path) print("Excel 报告生成成功！") def main(gdb_path, xzq_features, ph_features, dltb_features, sh_ph_tif, output_path,target_areas_dict:dict): try: # --- 1. 用户配置 --- # 输出配置 output_excel_path = os.path.join(output_path,"乡镇街道酸化统计表.xlsx") # 生成的Excel报告文件路径 # 设置工作空间和变量 arcpy.env.workspace = gdb_path arcpy.env.overwriteOutput = True sample_table_name = "历史样点PH信息_Table" # 图2: 样点信息表名 in_zone_feature = xzq_features # 规划分区图层 in_class_feature = ph_features # 已重分类好的酸化PH图层 dltb_class_feature = dltb_features in_value_raster = sh_ph_tif # 赋值栅格 out_feature_class = "最小面积统计单元" out_table_area = r"行政区划_酸化面积表" # 输出的交集表名 out_table_mean = r"行政区划_酸化均值表" # 输出的均值表名 print("开始处理数据...") if not arcpy.Exists(out_feature_class): # 判断输入表是否存在SHFJ字段 try: arcpy.management.CalculateField(in_class_feature, "SHFJ", "calculate_shfj(!gridcode!)", "PYTHON3", codeblock_cal_shfj) except Exception as e: print(f"计算SHFJ字段时发生错误: {e}") arcpy.analysis.Intersect( in_features=[dltb_class_feature, in_class_feature], out_feature_class=out_feature_class, join_attributes="ALL", output_type="INPUT" ) if not arcpy.Exists(out_table_area): # 1.用arcpy.analysis.TabulateIntersection进行交集制表 arcpy.analysis.TabulateIntersection( in_zone_feature, ["XZQMC"], out_feature_class, out_table_area, "SHFJ", out_units="SQUARE_METERS", ) if not arcpy.Exists(out_table_mean): # 2.用arcpy.sa.ZonalStatisticsAsTable进行区域统计 arcpy.sa.ZonalStatisticsAsTable( in_zone_feature, "XZQMC", in_value_raster, out_table_mean, "DATA", "MEAN" ) # 计算按地类平差后的各酸化等级面积 if arcpy.Exists(out_feature_class): df = pd.DataFrame(arcpy.da.TableToNumPyArray(out_feature_class, ["YJDL", "SHFJ", "Shape_Area"])) df_area = df.groupby(["YJDL", "SHFJ"]).agg({"Shape_Area": "sum"}).reset_index() yjdl_area = df_area.groupby(['YJDL'])['Shape_Area'].sum().reset_index() landuse_types = {'耕地':'01', '园地':'02', '林地':'03', '草地':'04', '其他':'12'} adjustment_factors = [] for _, row in yjdl_area.iterrows(): yjdl = row['YJDL'] original_total = row['Shape_Area'] * 0.0015 target_total = target_areas_dict.get(landuse_types[yjdl], original_total) adjustment_factor = target_total / original_total adjustment_factors.append({ 'YJDL': yjdl, '平差系数': adjustment_factor }) factor_df = pd.DataFrame(adjustment_factors) df_with_factors = df_area.merge(factor_df[['YJDL', '平差系数']], on='YJDL') df_with_factors['AREA_MU'] = df_with_factors['Shape_Area'] * df_with_factors['平差系数'] * 0.0015 # print(df_with_factors) # 生成表5.4的面积统计Excel报告 final_area_dataframe = process_data_for_table5_4(gdb_path, out_table_area, df_with_factors) write_to_excel_table5_4(final_area_dataframe, output_excel_path) # 生成表5.3的均值统计Excel报告 final_mean_dataframe = process_data_for_table5_7(gdb_path, out_table_mean, sample_table_name) write_to_excel_table5_7(final_mean_dataframe, output_excel_path.replace(".xlsx", "_mean.xlsx")) # 生成总表5.2的统计Excel报告 final_dataframe = process_data_for_table5_2(gdb_path, out_table_area, sample_table_name, df_with_factors) write_to_excel_table5_2(final_dataframe, final_mean_dataframe, output_excel_path.replace(".xlsx", "_total.xlsx")) return df_with_factors except Exception as e: print(f"\n处理过程中发生严重错误: {e}") import traceback traceback.print_exc() finally: import gc gc.collect() # --- 4. 主程序入口 --- # if __name__ == "__main__": # main()