630 lines
28 KiB
Python
630 lines
28 KiB
Python
# -*- coding: utf-8 -*-
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import os
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import arcpy
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import pandas as pd
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import numpy as np
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from openpyxl import Workbook
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from openpyxl.styles import Font
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from openpyxl.utils import get_column_letter
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from tools.config.arcgis_field_cal_code import codeblock_cal_shfj
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from tools.core.utils.excel_utils import ExcelStyleUtils
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from tools.config.custom_sort import yl_order, ts_order
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# --- 2. 辅助函数 ---
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# 获取要素类各酸化等级面积
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def get_acid_area_by_group(target_area_df):
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try:
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# 转为numpy数组供pandas统计使用
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df = target_area_df.copy()
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area_by_group = df.groupby("SHFJ")["AREA_MU"].sum()
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for key in area_by_group.keys():
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area_by_group[key] = area_by_group[key]
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return area_by_group.to_dict()
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except Exception as e:
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print(f"计算面积时出错: {str(e)}")
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return None
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def apply_adjustment_by_each_level(df, target_area_dict):
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"""
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对DataFrame中的面积数据按每一个酸化等级独立进行平差。
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参数:
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df (pd.DataFrame): 包含面积统计的DataFrame。
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target_area_dict (dict): 每个酸化等级的目标总面积字典。
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例如: {'轻度酸化': 10000.0, '中度酸化': 8000.0, ...}
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"""
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print("\n开始按每个酸化等级独立进行平差...")
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df_adjusted = df.copy()
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for level, target_area in target_area_dict.items():
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col_name = f'制图面积_亩_{level}'
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adjusted_col_name = f'平差后面积_亩_{level}'
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if col_name not in df.columns:
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print(f"警告: 未找到列 '{col_name}'，跳过该等级平差。")
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if adjusted_col_name not in df_adjusted.columns:
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df_adjusted[adjusted_col_name] = 0 # 创建一个空列
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continue
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# a. 计算该等级的实际总面积
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actual_area = df_adjusted[col_name].sum()
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if actual_area > 0:
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# b. 计算误差
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error = target_area - actual_area
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print(f"等级 '{level}': 目标面积={target_area:.2f}, 实际面积={actual_area:.2f}, 误差={error:.2f}")
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# c. 按比例分配误差
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adjustment = error * (df_adjusted[col_name] / actual_area)
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df_adjusted[adjusted_col_name] = df_adjusted[col_name] + adjustment
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df_adjusted[adjusted_col_name] = df_adjusted[adjusted_col_name].clip(lower=0)
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else:
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df_adjusted[adjusted_col_name] = df_adjusted[col_name]
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print("按每个酸化等级独立平差完成。")
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return df_adjusted
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# 获取酸化程度
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def get_acidification_degree(delta_ph):
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"""根据ΔpH值判断酸化程度"""
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if pd.isna(delta_ph) or delta_ph == 0:
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return "-"
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# 请根据您的实际分级标准调整这里的阈值
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if delta_ph > 1.0:
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return "重度酸化"
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elif 0.5 < delta_ph <= 1.0:
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return "中度酸化"
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elif 0.3 < delta_ph <= 0.5:
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return "轻度酸化"
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elif -0.3 <= delta_ph <= 0.3:
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return "未酸化"
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else: # dPH < -0.3
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return "碱化"
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# --- 3. 数据处理与分析 均值表---
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def process_data_for_table5_5(gdb_path, mean_table_name, sample_table_name):
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"""
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【最终版 v2】: 增加对制图样点数的处理，以支持加权平均计算。
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"""
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print("【最终版 v2】开始处理数据...")
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def clean_df(df, columns):
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for col in columns:
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df[col] = df[col].astype(str).str.strip()
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df.replace(['<Null>', 'None', '', '<空>'], np.nan, inplace=True)
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df.dropna(subset=columns, inplace=True)
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return df
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# --- a. 处理样点数据，计算“样点均值” ---
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print("--> 步骤1: 计算样点均值...")
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sample_table_path = os.path.join(gdb_path, sample_table_name)
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sample_fields = ['YL', 'TS', 'dPH']
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df_samples = pd.DataFrame(arcpy.da.TableToNumPyArray(sample_table_path, sample_fields, 'dPH>0.3', skip_nulls=False))
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df_samples = clean_df(df_samples, ['YL', 'TS'])
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# 按 YL, TS 分组，计算 dPH 的均值
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df_sample_means = df_samples.groupby(['YL', 'TS'])['dPH'].mean().reset_index()
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df_sample_means.rename(columns={'dPH': '样点均值'}, inplace=True)
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print("样点均值计算完成。")
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# --- b. 处理制图数据，获取“制图均值”和“制图样点数” ---
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print("--> 步骤2: 获取制图均值和样点数...")
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mean_table_path = os.path.join(gdb_path, mean_table_name)
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mean_fields = ['YL', 'TS', 'MEAN', 'COUNT']
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df_map_data = pd.DataFrame(arcpy.da.TableToNumPyArray(mean_table_path, mean_fields, skip_nulls=False))
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df_map_data = clean_df(df_map_data, ['YL', 'TS'])
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df_map_data.rename(columns={'MEAN': '制图均值', 'COUNT': '制图样点数'}, inplace=True)
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print("制图数据获取完成。")
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# --- c. 合并数据 ---
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print("--> 步骤3: 合并数据...")
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df_skeleton = pd.concat([
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df_sample_means[['YL', 'TS']],
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df_map_data[['YL', 'TS']]
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]).drop_duplicates().reset_index(drop=True)
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df_final = pd.merge(df_skeleton, df_sample_means, on=['YL', 'TS'], how='left')
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# **【核心修改】: 合并整个 df_map_data，而不仅仅是均值列**
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df_final = pd.merge(df_final, df_map_data, on=['YL', 'TS'], how='left')
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# --- d. 计算酸化程度 ---
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print("--> 步骤4: 计算酸化程度...")
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# **【核心修改】: 在计算酸化程度之前，先过滤掉不展示的行**
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# 我们只对 dPH 在酸化范围内 ( > 0.3) 的数据感兴趣
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# 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
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df_final['酸化程度_样本'] = df_final['样点均值'].apply(get_acidification_degree)
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df_final['酸化程度_制图'] = df_final['制图均值'].apply(get_acidification_degree)
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# (可选) 按“亚类”和“土属”排序
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in_yl_order = yl_order + [x for x in df_final['YL'].unique() if x not in yl_order]
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in_ts_order = ts_order + [x for x in df_final['TS'].unique() if x not in ts_order]
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df_final["YL"] = pd.Categorical(df_final['YL'], categories=in_yl_order, ordered=True)
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df_final["TS"] = pd.Categorical(df_final['TS'], categories=in_ts_order, ordered=True)
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df_final.sort_values(['YL', 'TS'], inplace=True)
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print("数据处理流程完成！")
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return df_final
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# --- 4. Excel 制表 均值表---
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def write_to_excel_table5_5(df, output_path):
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"""
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将处理好的数据写入格式化的 Excel 文件。
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"""
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if df.empty:
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print("警告: 没有数据可以写入 Excel。")
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return
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print(f"开始生成 Excel 报告到 '{output_path}'...")
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wb = Workbook()
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ws = wb.create_sheet("Mysheet", 0)
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ws.title = "不同类型土壤pH变化统计"
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# --- b. 绘制表头 ---
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ws.merge_cells('A1:A2'); ws['A1'] = '亚类'
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ws.merge_cells('B1:B2'); ws['B1'] = '土属'
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ws.merge_cells('C1:F1'); ws['C1'] = 'ΔpH'
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ws['C2'] = '样点均值'
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ws['D2'] = '酸化程度'
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ws['E2'] = '制图均值'
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ws['F2'] = '酸化程度'
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# --- c. 填充数据 ---
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current_row = 3
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# **【核心修改】: 先对整个DataFrame进行过滤，只保留需要展示的行**
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# 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
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acid_levels_to_show = ["轻度酸化", "中度酸化", "重度酸化"]
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df_to_write = df[
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df['酸化程度_样本'].isin(acid_levels_to_show) |
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df['酸化程度_制图'].isin(acid_levels_to_show)
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].copy() # 使用 .copy() 避免 SettingWithCopyWarning
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for yl, group_yl_df in df_to_write.groupby('YL', observed=True, sort=False):
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print(f"正在写入亚类: {yl}...")
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yl_start_row = current_row
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# 遍历该亚类下的所有“土属”
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for _, row_data in group_yl_df.iterrows():
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ws.cell(row=current_row, column=2).value = row_data['TS']
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# 填充样点数据
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sample_mean = row_data.get('样点均值')
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if pd.notna(sample_mean):
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ws.cell(row=current_row, column=3).value = f"{sample_mean:.2f}" if sample_mean > 0.3 else "-"
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ws.cell(row=current_row, column=4).value = row_data.get('酸化程度_样本', '-') if sample_mean > 0.3 else "-"
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else:
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ws.cell(row=current_row, column=3).value = "-"
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ws.cell(row=current_row, column=4).value = "-"
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# 填充制图数据
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map_mean = row_data.get('制图均值')
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if pd.notna(map_mean):
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ws.cell(row=current_row, column=5).value = f"{map_mean:.2f}" if map_mean > 0.3 else "-"
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ws.cell(row=current_row, column=6).value = row_data.get('酸化程度_制图', '-') if map_mean > 0.3 else "-"
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else:
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ws.cell(row=current_row, column=5).value = "-"
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ws.cell(row=current_row, column=6).value = "-"
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current_row += 1
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# 计算并写入“合计”行
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ws.cell(row=current_row, column=2).value = '合计'
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# 计算合计行的均值 (均值的均值)
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total_sample_mean = group_yl_df['样点均值'].mean()
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if pd.notna(total_sample_mean):
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ws.cell(row=current_row, column=3).value = f"{total_sample_mean:.2f}"
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ws.cell(row=current_row, column=4).value = get_acidification_degree(total_sample_mean)
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else:
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ws.cell(row=current_row, column=3).value = "-"
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ws.cell(row=current_row, column=4).value = "-"
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# b. **【核心修正】: 计算合计行的“制图均值”(加权平均)**
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# 准备加权平均的分子和分母
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weighted_sum = 0
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total_count = 0
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# 遍历当前亚类分组中的每一行
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for _, row in group_yl_df.iterrows():
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mean_val = row.get('制图均值')
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count_val = row.get('制图样点数')
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# 只有当均值和样点数都存在且有效时，才参与计算
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if pd.notna(mean_val) and pd.notna(count_val) and count_val > 0:
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weighted_sum += mean_val * count_val # Σ (mean * count)
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total_count += count_val # Σ (count)
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# 计算加权平均值
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weighted_avg = (weighted_sum / total_count) if total_count > 0 else 0
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if weighted_avg > 0:
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ws.cell(row=current_row, column=5).value = f"{weighted_avg:.2f}"
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ws.cell(row=current_row, column=6).value = get_acidification_degree(weighted_avg)
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else:
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ws.cell(row=current_row, column=5).value = "-"
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ws.cell(row=current_row, column=6).value = "-"
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# 合并“亚类”单元格
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if yl_start_row <= current_row:
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ws.merge_cells(start_row=yl_start_row, start_column=1, end_row=current_row, end_column=1)
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ws.cell(row=yl_start_row, column=1).value = yl
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current_row += 1
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# --- a. 定义样式 ---
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header_font = Font(name='等线', size=11, bold=True)
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# --- d. 应用样式和调整列宽 (最终健壮版) ---
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max_col_letter = get_column_letter(ws.max_column)
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if current_row > 1: # 确保有数据才应用样式
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ExcelStyleUtils.set_style(ws, f'A1:{max_col_letter}{current_row-1}')
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ExcelStyleUtils.set_style(ws, f'A1:{max_col_letter}2', header_font)
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print("正在自动调整列宽...")
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# 自动调整列宽
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ExcelStyleUtils.auto_adjust_column_width(ws)
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# --- e. 保存文件 ---
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wb.save(output_path)
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print("Excel 报告生成成功！")
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# --- 2. 数据处理与分析 (面积统计表) ---
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def process_data_final(gdb_path, area_table_name, sample_table_name):
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"""
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【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。
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"""
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print("【最终修正版 v2】开始处理数据...")
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def clean_df(df, columns):
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# ... (此函数不变)
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for col in columns:
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df[col] = df[col].astype(str).str.strip()
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df.replace(['<Null>', 'None', '', '<空>'], np.nan, inplace=True)
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df.dropna(subset=columns, inplace=True)
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return df
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# --- a. 从两个表中提取并建立唯一的 (YL, TS) 层级结构 "骨架" ---
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print("--> 步骤1: 建立统一的层级结构...")
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sample_table_path = os.path.join(gdb_path, sample_table_name)
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area_table_path = os.path.join(gdb_path, area_table_name)
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df_samples_raw = pd.DataFrame(arcpy.da.TableToNumPyArray(sample_table_path, ['YL', 'TS'], skip_nulls=False))
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df_area_raw = pd.DataFrame(arcpy.da.TableToNumPyArray(area_table_path, ['YL', 'TS'], skip_nulls=False))
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# 清理并合并两个表中的 (YL, TS) 组合
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df_samples_raw = clean_df(df_samples_raw, ['YL', 'TS'])
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df_area_raw = clean_df(df_area_raw, ['YL', 'TS'])
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# 使用 concat 连接两个DataFrame，然后用 drop_duplicates 去除重复的组合
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df_skeleton = pd.concat([df_samples_raw, df_area_raw]).drop_duplicates().reset_index(drop=True)
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if df_skeleton.empty:
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print("警告: 无法从源数据中建立任何有效的 (YL, TS) 层级结构。")
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return pd.DataFrame(), {}
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print(f"已建立包含 {len(df_skeleton)} 个唯一土壤类型的层级结构。")
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# --- b. 独立统计样点数据 ---
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print("--> 步骤2: 独立统计样点数据...")
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df_samples = pd.DataFrame(arcpy.da.TableToNumPyArray(sample_table_path, ['TS', 'YL', 'dPH'], skip_nulls=False))
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df_samples = clean_df(df_samples, ['YL', 'TS'])
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if not df_samples.empty:
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bins = [-np.inf, -0.3, 0.3, 0.5, 1.0, np.inf]
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labels = ["碱化", "未酸化", "轻度酸化", "中度酸化", "重度酸化"]
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df_samples['SHFJ'] = pd.cut(df_samples['dPH'], bins=bins, labels=labels, right=True)
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sample_counts = df_samples.groupby(['YL', 'TS', 'SHFJ'], observed=False).size().reset_index(name='样点数')
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ts_total_samples = sample_counts.groupby(['YL', 'TS'])['样点数'].transform('sum')
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sample_counts['样点占比'] = (sample_counts['样点数'] / ts_total_samples) * 100
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df_sample_stats = sample_counts.pivot_table(
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index=['YL', 'TS'], columns='SHFJ', values=['样点数', '样点占比'], fill_value=0, observed=False
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).reset_index()
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df_sample_stats.columns = [f'{col[0]}_{col[1]}'.strip('_') if col[1] else col[0] for col in df_sample_stats.columns]
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# 将样点统计结果合并到骨架上
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df_final = pd.merge(df_skeleton, df_sample_stats, on=['YL', 'TS'], how='left')
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else:
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df_final = df_skeleton.copy()
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# --- c. 独立统计面积数据 ---
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print("--> 步骤3: 独立统计面积数据...")
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df_area = pd.DataFrame(arcpy.da.TableToNumPyArray(area_table_path, ['TS', 'YL', 'SHFJ', 'AREA'], skip_nulls=False))
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df_area = clean_df(df_area, ['YL', 'TS'])
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if not df_area.empty:
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df_area['制图面积_亩'] = df_area['AREA'] * 0.0015
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ts_total_area = df_area.groupby(['YL', 'TS'])['制图面积_亩'].transform('sum')
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df_area['面积占比'] = (df_area['制图面积_亩'] / ts_total_area) * 100
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df_area_stats = df_area.pivot_table(
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index=['YL', 'TS'], columns='SHFJ', values=['制图面积_亩', '面积占比'], fill_value=0
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).reset_index()
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df_area_stats.columns = [f'{col[0]}_{col[1]}'.strip('_') if col[1] else col[0] for col in df_area_stats.columns]
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# 将面积统计结果合并到 df_final 上
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# 注意，这里我们合并到已经包含样点数据的 df_final 上
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df_final = pd.merge(df_final, df_area_stats, on=['YL', 'TS'], how='left')
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# --- d. 最后清理和构建映射 ---
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df_final.fillna(0, inplace=True)
|
||
|
||
print("--> 步骤4: 自动构建层级结构...")
|
||
in_yl_order = yl_order + [x for x in df_final['YL'].unique() if x not in yl_order]
|
||
in_ts_order = ts_order + [x for x in df_final['TS'].unique() if x not in ts_order]
|
||
df_final["YL"] = pd.Categorical(df_final['YL'], categories=in_yl_order, ordered=True)
|
||
df_final["TS"] = pd.Categorical(df_final['TS'], categories=in_ts_order, ordered=True)
|
||
df_final.sort_values(['YL', 'TS'], inplace=True)
|
||
dynamic_soil_mapping = df_final.groupby('YL', observed=True)['TS'].unique().apply(list).to_dict()
|
||
# for yl in dynamic_soil_mapping:
|
||
# dynamic_soil_mapping[yl].sort()
|
||
|
||
print("数据处理流程完成！")
|
||
return df_final, dynamic_soil_mapping
|
||
|
||
|
||
# --- 3. Excel 制表 面积统计表 ---
|
||
def write_to_excel(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:A2'); ws['A1'] = '亚类'
|
||
ws.merge_cells('B1:B2'); ws['B1'] = '土属'
|
||
|
||
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('YL', 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 是一个只包含当前亚类数据的子DataFrame
|
||
# 我们可以安全地在这个子DataFrame上进行迭代和计算
|
||
|
||
print(f"正在写入亚类: {yl}...")
|
||
yl_start_row = current_row
|
||
|
||
# 筛选出当前亚类的所有数据
|
||
group_yl_df = df[df['YL'] == yl]
|
||
|
||
# 1. 遍历该亚类下的所有“土属”并写入数据
|
||
for ts in ts_list:
|
||
ws.cell(row=current_row, column=2).value = ts
|
||
|
||
# 在子集中查找当前土属的数据行
|
||
row_data = group_yl_df[group_yl_df['TS'] == 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'平差后面积_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/亩
|
||
ws.cell(row=current_row, column=col_start + 2).number_format = "0.00"
|
||
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. 计算并写入这个亚类的“合计”行
|
||
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'平差后面积_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“亚类”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
|
||
max_col = 2 + len(acid_levels) * 4
|
||
if current_row > 1: # 确保有数据才应用样式
|
||
ExcelStyleUtils.set_style(ws, f'A1:{get_column_letter(max_col)}{current_row-1}')
|
||
ExcelStyleUtils.set_style(ws, f'A1:{get_column_letter(max_col)}2', header_font)
|
||
|
||
# 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, trlx_polygon, sh_ph_polygon, ph_raster, output_path, target_areas_df):
|
||
try:
|
||
# --- 1. 用户配置 ---
|
||
sample_table_name = "历史样点PH信息_Table" # 图2: 样点信息表名
|
||
|
||
# 输出配置
|
||
output_excel_path = os.path.join(output_path, "土壤类型酸化统计表.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
|
||
|
||
in_zone_feature = trlx_polygon # 土壤类型图
|
||
# in_class_feature = sh_ph_polygon # 已重分类好的酸化PH图层
|
||
in_class_feature = "最小面积统计单元"
|
||
in_value_raster = ph_raster # 酸化PH栅格
|
||
dltb_ph_statstable = "土地利用类型_酸化面积表" # 土壤类型_酸化面积表（gdb table）
|
||
out_table_area = r"土壤类型_酸化面积表" # 输出的交集表名
|
||
out_table_mean = r"土壤类型_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:
|
||
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}")
|
||
|
||
# 1.用arcpy.analysis.TabulateIntersection进行交集制表
|
||
arcpy.analysis.TabulateIntersection(
|
||
in_zone_feature,
|
||
["TS", "YL"],
|
||
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YL_TS字段
|
||
if not arcpy.ListFields(in_zone_feature, "YL_TS"):
|
||
# 如果不存在，则添加该字段
|
||
arcpy.management.AddField(in_zone_feature, "YL_TS", "TEXT")
|
||
# 计算YL_TS字段的值
|
||
arcpy.management.CalculateField(in_zone_feature,"YL_TS","!YL! + '_' + !TS!","PYTHON3")
|
||
|
||
# 2.用arcpy.sa.ZonalStatisticsAsTable进行区域统计
|
||
mean_table = arcpy.sa.ZonalStatisticsAsTable(
|
||
in_zone_feature, "YL_TS", 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,
|
||
[["YL", "TEXT"],["TS", "TEXT"]],
|
||
)
|
||
arcpy.management.CalculateField(mean_table, "YL", "!YL_TS!.split('_')[0]", "PYTHON3")
|
||
arcpy.management.CalculateField(mean_table, "TS", "!YL_TS!.split('_')[1]", "PYTHON3")
|
||
|
||
|
||
# 生成表5.4的面积统计Excel报告
|
||
final_dataframe, soil_structure = process_data_final(gdb_path, out_table_area, sample_table_name)
|
||
|
||
# 统计地类图斑酸化总面积亩
|
||
each_acid_area = get_acid_area_by_group(target_areas_df)
|
||
print(f"容县土壤类型图斑总 acid 总面积（亩）：{each_acid_area}")
|
||
# 执行平差计算
|
||
if each_acid_area:
|
||
adjusted_dataframe = apply_adjustment_by_each_level(final_dataframe, each_acid_area)
|
||
print("使用平差值进行修正！")
|
||
write_to_excel(adjusted_dataframe, soil_structure, output_excel_path)
|
||
else:
|
||
print("未使用平差值进行修正！")
|
||
write_to_excel(final_dataframe, soil_structure, output_excel_path)
|
||
|
||
# 生成表5.4的均值统计Excel报告
|
||
final_mean_dataframe = process_data_for_table5_5(gdb_path, out_table_mean, sample_table_name)
|
||
write_to_excel_table5_5(final_mean_dataframe, output_excel_path.replace(".xlsx", "_mean.xlsx"))
|
||
# adjusted_dataframe.to_csv(output_excel_path.replace(".xlsx", "_adjusted.csv"), index=False)
|
||
|
||
|
||
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()
|