# -*- coding: utf-8 -*- import os import arcpy import pandas as pd import numpy as np from collections import OrderedDict 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, codeblock_dltb_ejdl, codeblock_dltb_yjdl 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 -0.3 <= delta_ph <= 0.3: return "未酸化" else: # dPH < -0.3 return "碱化" # --- 3. 数据处理与分析 均值--- def process_data_for_table5_3(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("【最终版 v2】开始处理数据...") def clean_df(df, columns): # ... (此函数不变) 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 = ['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, ['YJDL', 'EJDL']) # 按 YJDL, EJDL 分组，计算 dPH 的均值 df_sample_means = df_samples.groupby(['YJDL', 'EJDL'])['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) # **【核心修改】: 增加读取 COUNT 字段** mean_fields = ['YJDL', 'EJDL', '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, ['YJDL', 'EJDL']) 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[['YJDL', 'EJDL']], df_map_data[['YJDL', 'EJDL']] ]).drop_duplicates().reset_index(drop=True) df_final = pd.merge(df_skeleton, df_sample_means, on=['YJDL', 'EJDL'], 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=['YJDL', 'EJDL'], 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) # (可选) 按“一级地类”和“二级地类”排序 in_ejdl_order = ejdl_order + [x for x in df_final['EJDL'].unique() if x not in ejdl_order] df_final["YJDL"] = pd.Categorical(df_final['YJDL'], categories=yjdl_order, ordered=True) df_final["EJDL"] = pd.Categorical(df_final['EJDL'], categories=in_ejdl_order, ordered=True) df_final.sort_values(['YJDL', 'EJDL'], 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_3(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:B1'); ws['A1'] = '土地利用类型' ws.merge_cells('C1:F1'); ws['C1'] = 'ΔpH' ws['A2'] = '一级' ws['B2'] = '二级' ws['C2'] = '样点均值' ws['D2'] = '酸化程度' ws['E2'] = '制图均值' ws['F2'] = '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** # 只有当“样点酸化程度”或“制图酸化程度”不为“未酸化”、“碱化”或“-”时，才展示该行 acid_levels_to_show = ["轻度酸化", "中度酸化", "重度酸化"] 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 yl, group_yl_df in df_to_write.groupby('YJDL', sort=False, observed=False): print(f"正在写入一级地类: {yl}...") yl_start_row = current_row # 遍历该一级地类下的所有“二级地类” for _, row_data in group_yl_df.iterrows(): ws.cell(row=current_row, column=2).value = row_data['EJDL'] # 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=3).value = f"{sample_mean:.2f}" if sample_mean > 0.3 else "-" ws.cell(row=current_row, column=4).value = row_data.get('酸化程度_样本', '-') if sample_mean > 0.3 else "-" else: ws.cell(row=current_row, column=3).value = "-" ws.cell(row=current_row, column=4).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=5).value = f"{map_mean:.2f}" if map_mean > 0.3 else "-" ws.cell(row=current_row, column=6).value = row_data.get('酸化程度_制图', '-') if map_mean > 0.3 else "-" else: ws.cell(row=current_row, column=5).value = "-" ws.cell(row=current_row, column=6).value = "-" current_row += 1 # 计算并写入“合计”行 if ws.cell(row=current_row-1, column=2).value in ["林地", "草地", "其他"]: ws.merge_cells(start_row=yl_start_row, start_column=1, end_row=yl_start_row, end_column=2) ws.cell(row=yl_start_row, column=1).value = yl continue ws.cell(row=current_row, column=2).value = '合计' # 计算合计行的均值 (均值的均值) total_sample_mean = group_yl_df['样点均值'].mean() if pd.notna(total_sample_mean): ws.cell(row=current_row, column=3).value = f"{total_sample_mean:.2f}" ws.cell(row=current_row, column=4).value = get_acidification_degree(total_sample_mean) else: ws.cell(row=current_row, column=3).value = "-" ws.cell(row=current_row, column=4).value = "-" # b. **【核心修正】: 计算合计行的“制图均值”(加权平均)** # 准备加权平均的分子和分母 weighted_sum = 0 total_count = 0 # 遍历当前一级地类分组中的每一行 for _, row in group_yl_df.iterrows(): mean_val = row.get('制图均值') count_val = row.get('制图样点数') # 只有当均值和样点数都存在且有效时，才参与计算 if pd.notna(mean_val) and pd.notna(count_val) and count_val > 0: weighted_sum += mean_val * count_val # Σ (mean * count) total_count += count_val # Σ (count) # 计算加权平均值 weighted_avg = (weighted_sum / total_count) if total_count > 0 else 0 if weighted_avg > 0: ws.cell(row=current_row, column=5).value = f"{weighted_avg:.2f}" ws.cell(row=current_row, column=6).value = get_acidification_degree(weighted_avg) else: ws.cell(row=current_row, column=5).value = "-" ws.cell(row=current_row, column=6).value = "-" # 合并“一级地类”单元格 if yl_start_row <= current_row: ws.merge_cells(start_row=yl_start_row, start_column=1, end_row=current_row, end_column=1) ws.cell(row=yl_start_row, column=1).value = yl 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...") # 自动调整列宽 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 (使用 Pandas) --- def process_data_for_table5_4(gdb_path, area_table_name, sample_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): # ... (此函数不变) 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) df_samples_raw = pd.DataFrame(arcpy.da.TableToNumPyArray(sample_table_path, ['YJDL', 'EJDL'], skip_nulls=False)) df_area_raw = pd.DataFrame(arcpy.da.TableToNumPyArray(area_table_path, ['YJDL', 'EJDL'], skip_nulls=False)) # 清理并合并两个表中的 (YJDL, EJDL) 组合 df_samples_raw = clean_df(df_samples_raw, ['YJDL', 'EJDL']) df_area_raw = clean_df(df_area_raw, ['YJDL', 'EJDL']) # 使用 concat 连接两个DataFrame，然后用 drop_duplicates 去除重复的组合 df_skeleton = pd.concat([df_samples_raw, df_area_raw]).drop_duplicates().reset_index(drop=True) if df_skeleton.empty: print("警告: 无法从源数据中建立任何有效的 (YJDL, EJDL) 层级结构。") return pd.DataFrame(), {} print(f"已建立包含 {len(df_skeleton)} 个唯一土壤类型的层级结构。") # --- 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, ['EJDL', 'YJDL', 'dPH'], skip_nulls=False)) df_samples = clean_df(df_samples, ['YJDL', 'EJDL']) if not df_samples.empty: # ... (统计逻辑不变) bins = [-np.inf, -0.3, 0.3, 0.5, 1.0, np.inf] labels = ["碱化", "未酸化", "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(['YJDL', 'EJDL', 'SHFJ'], observed=False).size().reset_index(name='样点数') ts_total_samples = sample_counts.groupby(['YJDL', 'EJDL'])['样点数'].transform('sum') sample_counts['样点占比'] = (sample_counts['样点数'] / ts_total_samples) * 100 df_sample_stats = sample_counts.pivot_table( index=['YJDL', 'EJDL'], columns='SHFJ', values=['样点数', '样点占比'], fill_value=0, observed=False ).reset_index() df_sample_stats.columns = [f'{col[0]}_{col[1]}'.strip('_') if col[1] else col[0] for col in df_sample_stats.columns] # 将样点统计结果合并到骨架上 df_final = pd.merge(df_skeleton, df_sample_stats, on=['YJDL', 'EJDL'], how='left') else: df_final = df_skeleton.copy() # --- 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, ['EJDL', 'YJDL', 'SHFJ', 'AREA'], skip_nulls=False)) df_area = clean_df(df_area, ['YJDL', 'EJDL']) if not df_area.empty: # 计算平差系数 landuse_types = {'耕地':'01', '园地':'02', '林地':'03', '草地':'04', '其他':'12'} df_area['AREA_MU'] = df_area['AREA'] * 0.0015 yjdl_area = df_area.groupby(['YJDL'])['AREA_MU'].sum().reset_index() yjdl_area.columns = ['YJDL', 'ORIGINAL_TOTAL_MU'] adjustment_factors = [] for _, row in yjdl_area.iterrows(): yjdl = row['YJDL'] original_total = row['ORIGINAL_TOTAL_MU'] target_total = target_area_dict.get(landuse_types[yjdl], original_total) # 如果没有指定，就用原始面积 adjustment_factor = target_total / original_total adjustment_factors.append({ 'YJDL': yjdl, '原始总面积_亩': original_total, '目标总面积_亩': target_total, '平差系数': adjustment_factor }) factor_df = pd.DataFrame(adjustment_factors) # 4. 对每个二级地类应用平差系数 # 合并原始数据和平差系数 df_with_factors = df_area.merge(factor_df[['YJDL', '平差系数']], on='YJDL') df_with_factors['制图面积_亩'] = df_with_factors['AREA_MU'] * df_with_factors['平差系数'] ts_total_area = df_with_factors.groupby(['YJDL', 'EJDL'])['制图面积_亩'].transform('sum') df_with_factors['面积占比'] = (df_with_factors['制图面积_亩'] / ts_total_area) * 100 df_area_stats = df_with_factors.pivot_table( index=['YJDL', 'EJDL'], 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] # 将面积统计结果合并到 df_final 上 # 注意，这里我们合并到已经包含样点数据的 df_final 上 df_final = pd.merge(df_final, df_area_stats, on=['YJDL', 'EJDL'], how='left') # --- d. 最后清理和构建映射 --- df_final.fillna(0, inplace=True) print("--> 步骤4: 自动构建层级结构...") dynamic_soil_mapping = df_final.groupby('YJDL')['EJDL'].unique().apply(list).to_dict() dynamic_soil_mapping = OrderedDict(sorted(dynamic_soil_mapping.items(),key=lambda item: yjdl_order.index(item[0]))) in_ejdl_order = ejdl_order + [x for x in df_final['EJDL'].unique() if x not in ejdl_order] for yl in dynamic_soil_mapping: # dynamic_soil_mapping[yl].sort() dynamic_soil_mapping[yl] = sorted( dynamic_soil_mapping[yl], key=lambda x: in_ejdl_order.index(x)) print("数据处理流程完成！") return df_final, dynamic_soil_mapping # --- 3. Excel 制表 面积--- def write_to_excel_table5_4(df, soil_mapping, 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。") return print(f"开始生成 Excel 报告到 '{output_path}'...") wb = Workbook() ws = wb.create_sheet("Mysheet", 0) ws.title = "不同类型土壤酸化程度统计" # --- b. 绘制表头 (不变) --- ws.merge_cells('A1:B1'); ws['A1'] = '土地利用类型' ws['A2'] = '一级' ws['B2'] = '二级' acid_levels = ['轻度酸化', '中度酸化', '重度酸化'] all_possible_levels = ['碱化', '未酸化', '轻度酸化', '中度酸化', '重度酸化'] acid_level_headers = ['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)'] col_start = 3 for header in acid_level_headers: ws.merge_cells(start_row=1, start_column=col_start, end_row=1, end_column=col_start + 3) ws.cell(row=1, column=col_start).value = header ws.cell(row=2, column=col_start).value = '样点数/个' ws.cell(row=2, column=col_start + 1).value = '占比/%' ws.cell(row=2, column=col_start + 2).value = '制图面积/亩' ws.cell(row=2, column=col_start + 3).value = '占比/%' col_start += 4 # --- c. 填充数据 (完全重构的逻辑) --- current_row = 3 # 使用 .groupby('YJDL', sort=False) 来保证我们之前设置的排序顺序 for yl, ts_list in soil_mapping.items(): # **【关键】** group_yl 是一个只包含当前一级地类数据的子DataFrame # 我们可以安全地在这个子DataFrame上进行迭代和计算 print(f"正在写入一级地类: {yl}...") yl_start_row = current_row # 筛选出当前一级地类的所有数据 group_yl_df = df[df['YJDL'] == yl] # 1. 遍历该一级地类下的所有“二级地类”并写入数据 for ts in ts_list: ws.cell(row=current_row, column=2).value = ts # 在子集中查找当前二级地类的数据行 row_data = group_yl_df[group_yl_df['EJDL'] == ts] # --- 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: # 如果找到了数据 (row_data 不为空)，我们就获取这一行的数据 # .iloc[0] 获取第一行（也是唯一一行）的数据，作为一个 Series 对象 data_series = row_data.iloc[0] # 遍历每一个酸化等级，填充对应的四列数据 for level in acid_levels: # 1. 构建要从 data_series 中查找的列名 sample_col = f'样点数_{level}' sample_pct_col = f'样点占比_{level}' area_col = f'制图面积_亩_{level}' area_pct_col = f'面积占比_{level}' # 2. 从 data_series 中安全地获取值 # 使用 .get(key, default_value) 的好处是，如果列名不存在，它会返回默认值(0)，而不会报错 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 # - 对于数值，我们判断它是否大于0。如果是，就填入数值；否则，填入 "-" # - 对于样点数，我们将其转为整数 # - 对于占比和面积，我们保留两位小数 # 样点数/个 ws.cell(row=current_row, column=col_start).value = int(sample_val) 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/万亩 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 += 4 else: # 如果没有找到该土属的数据 (row_data 为空) # 这意味着该土属在源数据中不存在任何样点或面积信息 # 我们将整行所有统计单元格都填充为 "-" # acid_levels 列表包含3个等级，每个等级4列，总共12列 for _ in range(len(acid_levels) * 4): ws.cell(row=current_row, column=col_start).value = "-" col_start += 1 # --- 填充单元格的逻辑结束 --- # 完成一行填充后，行号加1，为下一行做准备 current_row += 1 # 2. 计算并写入这个一级地类的“合计”行 if ws.cell(row=current_row-1, column=2).value in ["林地","草地", "其他"]: ws.merge_cells(start_row=yl_start_row, start_column=1, end_row=yl_start_row, end_column=2) ws.cell(row=yl_start_row, column=1).value = yl continue ws.cell(row=current_row, column=2).value = '合计' # 计算总样点数和总面积，仅针对当前 group_yl yl_grand_total_samples = 0 for lvl in all_possible_levels: if f'样点数_{lvl}' in group_yl_df: yl_grand_total_samples += group_yl_df[f'样点数_{lvl}'].sum() yl_grand_total_area = 0 for lvl in all_possible_levels: if f'制图面积_亩_{lvl}' in group_yl_df: yl_grand_total_area += group_yl_df[f'制图面积_亩_{lvl}'].sum() col_start = 3 for level in acid_levels: sample_sum = group_yl_df.get(f'样点数_{level}', 0).sum() col_name = f'制图面积_亩_{level}' area_sum = group_yl_df[col_name].sum() if col_name in group_yl_df else 0 # area_sum = group_yl_df.get(f'制图面积_亩_{level}', 0).sum() sample_perc = (sample_sum / yl_grand_total_samples * 100) if yl_grand_total_samples > 0 else 0 area_perc = (area_sum / yl_grand_total_area * 100) if yl_grand_total_area > 0 else 0 ws.cell(row=current_row, column=col_start).value = int(sample_sum) if sample_sum > 0 else "-" ws.cell(row=current_row, column=col_start + 1).value = f"{sample_perc:.2f}%" if sample_sum > 0 else "-" ws.cell(row=current_row, column=col_start + 2).value = f"{area_sum:.0f}" if area_sum > 0 else "-" ws.cell(row=current_row, column=col_start + 3).value = f"{area_perc:.2f}%" if area_sum > 0 else "-" col_start += 4 # 3. 合并“一级地类”单元格 if yl_start_row <= current_row: ws.merge_cells(start_row=yl_start_row, start_column=1, end_row=current_row, end_column=1) ws.cell(row=yl_start_row, column=1).value = yl 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...") # 调整列宽 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:str, ph_features:str,dltb_class_feature:str, shph_tif:str, output_path:str,target_areas_dict:dict): try: # --- 1. 用户配置 --- # 输出配置 output_excel_path = os.path.join(output_path, "土地利用类型酸化统计表.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 = dltb_class_feature # 地类图斑 in_class_feature = ph_features # 已重分类好的酸化PH图层 in_value_raster = shph_tif # 赋值栅格,酸化PH栅格 out_table_area = r"土地利用类型_酸化面积表" # 输出的面积统计表名 out_table_mean = r"土地利用类型_酸化均值表" # 输出的均值表名 print("开始处理数据...") if not arcpy.Exists(out_table_area): # 判断输入表是否存在SHFJ字段 try: if not arcpy.ListFields(in_zone_feature, "EJDL"): arcpy.management.CalculateField(in_zone_feature, "EJDL", "calculate_ejdl(!DLBM!,!DLMC!)", "PYTHON3", codeblock_dltb_ejdl) arcpy.management.CalculateField(in_zone_feature, "YJDL", "calculate_yjdl(!DLBM!)", "PYTHON3", codeblock_dltb_yjdl) if not arcpy.ListFields(in_class_feature, "SHFJ"): 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}") # 拿到地类图斑的坐标系 desc = arcpy.Describe(in_zone_feature) spatial_ref = desc.spatialReference # 1.用arcpy.analysis.TabulateIntersection进行交集制表,面积使用地类图斑投影坐标系下面积 with arcpy.EnvManager(outputCoordinateSystem=spatial_ref): arcpy.analysis.TabulateIntersection( in_zone_feature, ["YJDL", "EJDL"], in_class_feature, out_table_area, "SHFJ", out_units="SQUARE_METERS", ) if not arcpy.Exists(out_table_mean): # 判断输入表是否存在YJDL_EJDL字段 if not arcpy.ListFields(in_zone_feature, "YJDL_EJDL"): # 如果不存在，则添加该字段 arcpy.management.AddField(in_zone_feature, "YJDL_EJDL", "TEXT") # 计算YJDL_EJDL字段的值 arcpy.management.CalculateField(in_zone_feature,"YJDL_EJDL","!YJDL! + '_' + !EJDL!","PYTHON3") # 2.用arcpy.sa.ZonalStatisticsAsTable进行区域统计 mean_table = arcpy.sa.ZonalStatisticsAsTable( in_zone_feature, "YJDL_EJDL", in_value_raster, out_table_mean, "DATA", "MEAN" ) # 2.1 添加土壤类型字段并计算 arcpy.management.AddFields( out_table_mean, [["YJDL", "TEXT"],["EJDL", "TEXT"]], ) arcpy.management.CalculateField(mean_table, "YJDL", "!YJDL_EJDL!.split('_')[0]", "PYTHON3") arcpy.management.CalculateField(mean_table, "EJDL", "!YJDL_EJDL!.split('_')[1]", "PYTHON3") # 生成表5.4的面积统计Excel报告 final_dataframe, soil_structure = process_data_for_table5_4(gdb_path, out_table_area, sample_table_name,target_areas_dict) write_to_excel_table5_4(final_dataframe, soil_structure, output_excel_path) # 生成表5.3的均值统计Excel报告 final_mean_dataframe = process_data_for_table5_3(gdb_path, out_table_mean, sample_table_name) write_to_excel_table5_3(final_mean_dataframe, output_excel_path.replace(".xlsx", "_mean.xlsx")) 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()