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+import pandas as pd
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+import numpy as np
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+import time
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+import public as pb
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+import openpyxl
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+import matplotlib.pyplot as plt
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+from scipy.stats import spearmanr
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+# import tkinter as tk
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+# from tkinter import ttk
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+from tkinter import filedialog
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+from tkinter import messagebox
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+from openpyxl import load_workbook
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+from openpyxl.drawing.image import Image as OImage
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+import os
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+import ttkbootstrap as ttk
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+from ttkbootstrap.constants import *
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+from PIL import Image, ImageTk
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+from ttkbootstrap.dialogs import Messagebox
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+import plotly.graph_objects as go
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+import plotly.io as pio
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+from sklearn.linear_model import LinearRegression
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+import report
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+import partReport
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+from docx import Document
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+from openpyxl.worksheet.hyperlink import Hyperlink
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+import docx
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+from docx import Document
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+from docx.shared import Inches
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+from docx.oxml import OxmlElement, ns
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+from docx.shared import Pt, RGBColor
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+from docx.oxml.ns import nsdecls, nsmap
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+from docx.oxml import parse_xml
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+from docx.enum.dml import MSO_THEME_COLOR_INDEX
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+from docx import Document
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+from docx.opc.constants import RELATIONSHIP_TYPE as RT
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+from docx.enum.table import WD_TABLE_ALIGNMENT, WD_CELL_VERTICAL_ALIGNMENT
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+from docx.oxml.ns import qn
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+from docx.enum.text import WD_ALIGN_PARAGRAPH
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+from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
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+import uuid
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+import hashlib
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+from ttkbootstrap.dialogs import Querybox
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+# 显示所有数据
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+pd.set_option('display.width', 10000) # 设置字符显示宽度
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+pd.set_option('display.max_rows', None) # 设置显示最大行
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+pd.set_option('display.max_columns', None) # 设置显示最大列，None为显示所有列
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+# 一些全局变量
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+changeFileUrl = '' # 选择的文件路径
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+saveFileUrl = '' #保存文件路径
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+resData_1_Style = None
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+resData_2 = None
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+resData_3_Style = pd.DataFrame({})
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+resData_4 = pd.DataFrame({})
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+resData_5_Style = None
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+resData_6 = None
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+resData_8_Style = None
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+resData_7 = None
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+resData_10_Style = None
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+resData_9 = None
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+resData_12_Style = None
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+resData_11 = None
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+resData_14_Style = None
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+resData_13 = None
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+htmlContent = []
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+htmlStatisticsContent = []
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+# 报告需要用到的数据
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+table_1_data = pd.DataFrame({})
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+table_3_data = pd.DataFrame({})
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+table_5_data = pd.DataFrame({})
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+table_8_data = pd.DataFrame({}) # 样品编号替换为编号
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+table_10_data = pd.DataFrame({})
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+table_12_data = pd.DataFrame({})
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+table_14_data = pd.DataFrame({})
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+checkType = '' # 保存可用类型
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+# 保存需要用到的异常指标数据
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+table_1_index = pd.DataFrame({})
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+table_3_index = pd.DataFrame({})
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+table_5_index = pd.DataFrame({})
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+table_8_index = pd.DataFrame({}) # 样品编号替换为编号
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+table_10_index = pd.DataFrame({})
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+table_12_index = pd.DataFrame({})
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+table_14_index = pd.DataFrame({})
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+# 合并数据 检查审核结果中有阈值以外字段的 提取出来 todo 提取序号 编号 土地类型
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+# 设置字体
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+# 设置字体 微软雅黑 新罗马 加粗
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+plt.rcParams['font.family'] = ['Times New Roman','Microsoft YaHei']
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+# 设置字体加粗
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+font = {'weight': 'bold'}
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+plt.rc('font', **font) # 应用字体设置
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+# 公共函数
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+def calculate_row_range(row):
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+
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+ return row.max() - row.min() # 对每一行计算最大值与最小值之差
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+
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+# 转数字
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+def filter_number(arr):
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+ """
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+ :param arr:
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+ :return:
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+ """
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+ return pd.to_numeric(arr, errors='coerce')
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+# 公共函数处理重复样品：根据样品编号筛选出所有重复的样品数据，求均值后再和总数据合并
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+def getRepeat(arr):
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+ df1 = arr[arr.duplicated(subset='原样品编号',keep=False)].drop_duplicates('原样品编号')['原样品编号']
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+ dpData = pd.DataFrame({})
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+ # 循环 筛选对应重复数据
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+ for i in df1:
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+ dpArr = arr[arr['原样品编号'] == i]
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+ numeric_cols = dpArr.select_dtypes(include=['int', 'float']).columns
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+ dpArr[numeric_cols] = dpArr[numeric_cols].apply(lambda x: round(x.mean(),2), axis=0)
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+ newData = dpArr.drop_duplicates(subset=['原样品编号'], keep='last')
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+ dpData = dpData._append(newData)
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+ return dpData
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+# 存疑行标红
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+def highlight_condition(s):
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+ if s['审核结果'] != '' and not pd.isna(s['审核结果']):
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+ return ['background-color: #99CC99']*len(s)
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+ else:
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+ return ['']*len(s)
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+
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+# 自适应列宽
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+def autoColumns(url):
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+ wb = load_workbook(url)
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+ ws = wb.active
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+ ws_pd = wb['频度分析']
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+ # 自适应调整列宽
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+ for column_cells in ws.columns:
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+ length = max(len(str(cell.value)) for cell in column_cells if cell.value is not None)
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+ ws.column_dimensions[column_cells[0].column_letter].width = length + 15 # 可以根据需要调整额外的宽度
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+ for column_cells in ws_pd.columns:
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+ length = max(len(str(cell.value)) for cell in column_cells if cell.value is not None)
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+ ws_pd.column_dimensions[column_cells[0].column_letter].width = length + 5 # 可以根据需要调整额外的宽度
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+ # 保存调整后的Excel文件
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+ wb.save(url)
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+
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+
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+# 频度分析函数 公用
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+def frequency_analysis(arr):
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+ qua_2 = arr.quantile(0.02)
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+ print(1.55)
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+ qua_5 = arr.quantile(0.05)
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+ qua_10 = arr.quantile(0.1)
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+ qua_20 = arr.quantile(0.2)
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+ qua_50 = arr.quantile(0.5)
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+ qua_80 = arr.quantile(0.8)
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+ qua_90 = arr.quantile(0.9)
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+ qua_95 = arr.quantile(0.95)
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+ qua_98 = arr.quantile(0.98)
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+ min_value = arr.min()
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+ max_value = arr.max()
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+ print(1.6)
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+ median_value = arr.median() # 中位数
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+ jc_value = arr.max() - arr.min() # 极差
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+ print(1.7)
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+ std_value = arr.std() # 标准差
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+ mean_value = arr.mean() # 平均数
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+ variation_value = std_value / mean_value # 变异系数 = 标准差/均值
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+ data = pd.DataFrame(
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+ [qua_2, qua_5, qua_10, qua_20, qua_50, qua_80, qua_90, qua_95, qua_98, min_value, max_value, median_value,
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+ jc_value, std_value, mean_value, variation_value])
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+ index_value = ['2%', '5%', '10%', '20%', '50%', '80%', '90%', '95%', '98%', '最小值', '最大值', '中位数', '极差',
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+ '标准差', '平均数', '变异系数']
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+ # 汇总数据
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+ data.index = index_value
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+ data_res = round(data, 2)
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+ print('data_res', data_res)
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+ return data_res
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+
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+# 绘图函数
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+def getImg(x,y,url,name,sheetName,xLabel,YLabel,numArr,fileUrl,loc):
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+ coef, p_value = spearmanr(x, y)
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+ fig = go.Figure(data=go.Scatter(
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+ x=x,
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+ y=y,
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+ text=numArr.to_numpy(),
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+ mode='markers',name='散点数据'))
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+ # 设置图表布局
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+ fig.update_layout(title=f"{xLabel}和{YLabel}Spearman相关性系数: {coef:.2f}",
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+ xaxis_title=xLabel,
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+ yaxis_title=YLabel)
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+
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+ model = LinearRegression()
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+ model.fit(x.to_numpy().reshape(-1, 1), y) # 用x的平方作为特征值
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+ y_pred = model.predict(x.to_numpy().reshape(-1, 1))
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+ fig.add_trace(go.Scatter(x=x, y=y_pred, mode='lines', name='拟合直线'))
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+
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+ html_file_path = f"{url}/{name}频度统计图.html"
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+ pio.write_html(fig, file=html_file_path, auto_open=False)
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+ # 在表格中插入html
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+ workbook = load_workbook(filename=fileUrl)
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+ # 选择一个工作表
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+ ws = workbook[sheetName]
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+ # 将 HTML 内容作为富文本写入单元格
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+ ws[loc] = '=HYPERLINK("file:///{0}","点击查看统计图")'.format(html_file_path)
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+ workbook.save(fileUrl)
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+
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+
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+# 频度统计直方图
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+def getStatisticsImg(data,xLabel,name,fileUrl,url,loc):
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+ fig = go.Figure(data=[go.Histogram(x=data)])
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+ # 设置标题和其他格式
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+ fig.update_layout(
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+ title_text= f"{name}统计图",
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+ xaxis_title=xLabel,
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+ yaxis_title='频次',
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+ bargap=0.2, # 相邻位置坐标的钢筋之间的间隙
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+ bargroupgap=0.1 #
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+ )
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+ html_file_path = f"{url}/{name}频度统计图.html"
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+ pio.write_html(fig, file=html_file_path, auto_open=False)
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+ # 在表格中插入html
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+ workbook = load_workbook(filename=fileUrl)
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+ # 选择一个工作表
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+ ws = workbook['频度分析']
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+ # 将 HTML 内容作为富文本写入单元格
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+ ws[loc] = '=HYPERLINK("file:///{0}","点击查看统计图")'.format(html_file_path)
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+ workbook.save(fileUrl)
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+
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+# ---------------数据读取计算-----------------
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+def is_trial_file():
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+ try:
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+ with open('./html/config.txt', 'r') as file:
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+ start_date_str = file.read()
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+ return True
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+ except FileNotFoundError:
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+ # 如果文件不存在，这是用户第一次打开，开始试用
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+ with open('./html/config.txt', 'w') as file:
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+ file.write('376d8bf8f8855ad8de997fa5dac1bd24956aef0cbfa0cf8ac04053a7043e3d90248051f6f03f02b20430949504a5556fb112131fc81205768229ffa023831b04')
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+ return False
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+
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+def is_code_file():
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+ try:
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+ with open('./html/code.txt', 'r') as file:
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+ start_date_str = file.read()
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+ return True
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+ except FileNotFoundError:
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+ # 如果文件不存在，这是用户第一次打开，开始试用
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+
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+ return False
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+
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+
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+def getOption():
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+ # 检查标记文件和注册码文件
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+ type = 'HUNDRED_DATA' # 试用100条
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+ configFile = './html/config.docx'
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+ codeFile = './html/code.docx'
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+ # resginNum = getNum()
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+ # 注册码正确 可用
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+ if not is_trial_file() and not is_code_file():
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+ type = 'HUNDRED_DATA'
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+ elif is_trial_file() and not is_code_file():
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+ type = 'OVER_LINE'
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+ elif is_code_file():
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+ type = 'ALL'
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+ return type
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+
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+
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+def getNum():
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+ device_id = uuid.getnode() # 获取设备的唯一ID，通常是MAC地址
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+ str = f'{device_id}-window-pc-user'
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+ sha256 = hashlib.sha256()
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+ sha256.update(str.encode('utf-8')) # 将字符串编码为UTF-8格式
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+ newStr = sha256.hexdigest()
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+ front_8 = newStr[:8]
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+ middle_40_36 = newStr[36:40]
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+ end_4 = newStr[-4:]
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+ return f"{front_8}{middle_40_36}{end_4}"
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+# 总体审核函数
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+def checkData(fileUrl):
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+ try:
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+ # 这里先弹窗提示输入注册码，获取注册码请点击下方获取申请码按钮了解详情。
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+ # 无注册码点击确定按钮弹框消失，但此时只能使用一百条数据
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+ # 默认读取第一个sheet
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+
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+ type = getOption()
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+ global checkType
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+ checkType = type
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+ data = pd.read_excel(fileUrl,converters={'原样品编号': str})
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+ print('fileUrl', data['总砷'])
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+ if type == 'OVER_LINE':
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+ show_error('试用已结束，使用更多请点击下方获取申请码按钮联系管理员！')
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+ elif type == 'HUNDRED_DATA' or type == 'ALL':
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+ if type == 'HUNDRED_DATA':
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+ data = data.head(100)
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+ global htmlContent
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+ htmlContent = []
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+ if not data.empty:
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+ # 开始审核
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+ # 计算均值--已有
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+ # 极差 极差的具体计算公式为：R=xmax−xmin
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+ # 删除质控样品 编号里含zk的样品可以删除
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+ simpleData = data.dropna(subset=['原样品编号'])
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+
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+ simpleData = simpleData[~simpleData['原样品编号'].str.contains('ZK')]
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+ simpleData = simpleData.replace(r'[^.\w]+', '', regex=True)
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+ # print('simpleData',simpleData)
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+ simpleData = simpleData.replace('未检测', np.nan)
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+ simpleData.iloc[:, 3:] = simpleData.iloc[:, 3:].apply(pd.to_numeric,errors='ignore')
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+ print('simpleData--砷', simpleData['总镉'].min(), simpleData['总镉'].max())
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+ strList = ['原样品编号','样品编号','地理位置','土壤类型','母质','土地利用类型','土壤质地']
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+ for i in simpleData.columns:
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+ if i not in strList:
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+ simpleData[i] = pd.to_numeric(simpleData[i], errors='ignore')
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+ # 处理重复样品
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+ res = getRepeat(simpleData)
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+ simpleData = simpleData._append(res).drop_duplicates(subset=['原样品编号'], keep='last')
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+ print('simpleData--砷',simpleData['总砷'].min() )
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+ jCData = simpleData[['土壤容重1（g/cm³）', '土壤容重2（g/cm³）', '土壤容重3（g/cm³）', '土壤容重4（g/cm³）']]
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+ # 计算土壤容重均值
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+ rZMean = round(simpleData[['土壤容重1（g/cm³）', '土壤容重2（g/cm³）', '土壤容重3（g/cm³）', '土壤容重4（g/cm³）']].mean(
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+ axis=1),2)
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+ # 极差值
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+ jCResData = jCData.apply(calculate_row_range, axis=1) # 在每一行上应用函数，axis=1表示按行操作
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+ # 相对极差 相对极差（%）= 极差（绝对极差）/ 数值平均值 × 100%。
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+ relativeJCData = jCResData / simpleData['土壤容重平均值（g/cm³）'] * 100
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+ # 加和
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+ plusData = simpleData['2~0.2mm颗粒含量'] + simpleData['0.2~0.02mm颗粒含量'] + simpleData[
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+ '0.02~0.002mm颗粒含量'] + simpleData['0.002mm以下颗粒含量']
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+ # ---------------表1----------数据汇总
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|
+ resData = pd.DataFrame({
|
|
|
+ '编号': simpleData['原样品编号'],
|
|
|
+ '地理位置': simpleData['地理位置'],
|
|
|
+ '土壤类型': simpleData['土壤类型'],
|
|
|
+ '土地利用类型': simpleData['土地利用类型'],
|
|
|
+ '母质': simpleData['母质'],
|
|
|
+ '土壤质地': simpleData['土壤质地'],
|
|
|
+ '土壤容重1(g/cm3)': simpleData['土壤容重1（g/cm³）'],
|
|
|
+ '土壤容重2(g/cm3)': simpleData['土壤容重2（g/cm³）'],
|
|
|
+ '土壤容重3(g/cm3)': simpleData['土壤容重3（g/cm³）'],
|
|
|
+ '土壤容重4(g/cm3)': simpleData['土壤容重4（g/cm³）'],
|
|
|
+ '土壤容重平均值(g/cm3)': simpleData['土壤容重平均值（g/cm³）'],
|
|
|
+ '土壤容重平均值(g/cm3)(计算)': rZMean,
|
|
|
+ '极差': jCResData,
|
|
|
+ '相对极差(%)': relativeJCData,
|
|
|
+ '洗失量(吸管法需填)%': simpleData['洗失量（吸管法需填）'],
|
|
|
+ '2-0.2mm颗粒含量%': simpleData['2~0.2mm颗粒含量'],
|
|
|
+ '0.2-0.02mm颗粒含量%': simpleData['0.2~0.02mm颗粒含量'],
|
|
|
+ '0.02-0.002mm颗粒含量%': simpleData['0.02~0.002mm颗粒含量'],
|
|
|
+ '0.002mm以下颗粒含量%': simpleData['0.002mm以下颗粒含量'],
|
|
|
+ '加和%': plusData,
|
|
|
+ 'pH': simpleData['pH']
|
|
|
+ })
|
|
|
+ # 调用审核函数 得到审核结果
|
|
|
+ table_1_res = pb.soil_bulk_density(resData)
|
|
|
+ resData = resData.reset_index(drop=True)
|
|
|
+ resData['审核结果'] = table_1_res['审核结果']
|
|
|
+ global table_1_data
|
|
|
+ table_1_data_res = resData[resData['土壤质地'] != resData['土壤类型(判断)']]
|
|
|
+ table_1_data = resData
|
|
|
+ # 提取异常指标数据
|
|
|
+ global table_1_index
|
|
|
+ table_1_index = pd.DataFrame({
|
|
|
+ '样品编号': simpleData['样品编号'],
|
|
|
+ '土地利用类型': resData['土地利用类型'],
|
|
|
+ '指标': table_1_res['异常指标'],
|
|
|
+ '原因': table_1_res['审核结果']
|
|
|
+ })
|
|
|
+ # 这里数据直接使用即可（土壤质地填报）todo
|
|
|
+ del resData['土壤类型(判断)']
|
|
|
+ resData.insert(loc=6, column='土壤类型(判断)', value=table_1_res['土壤类型(判断)'])
|
|
|
+ global resData_1_Style
|
|
|
+ resData_1_Style = resData.style.apply(highlight_condition,axis=1)
|
|
|
+ # 表2--------------------------表2 土壤容重与机械组成总体数据频度分析----------------------------------------
|
|
|
+ # 计算6个指标的百分位 及其他值 2% 5% 10% 20% 50% 80% 90% 95% 98% 最小值 最大值 中位数 极差 标准差 平均数 变异系数
|
|
|
+ # 土壤容重均值 rZMean
|
|
|
+ data_2 = pd.DataFrame({
|
|
|
+ '土壤容重(g/cm3)': resData['土壤容重平均值(g/cm3)(计算)'],
|
|
|
+ '洗失量(吸管法需填)%': simpleData['洗失量（吸管法需填）'],
|
|
|
+ '2-0.2mm颗粒含量%': simpleData['2~0.2mm颗粒含量'],
|
|
|
+ '0.2-0.02mm颗粒含量%': simpleData['0.2~0.02mm颗粒含量'],
|
|
|
+ '0.002-0.002mm颗粒含量%': simpleData['0.02~0.002mm颗粒含量'],
|
|
|
+ '0.002mm以下颗粒含量%': simpleData['0.002mm以下颗粒含量']
|
|
|
+ })
|
|
|
+ global resData_2
|
|
|
+ print(1.5)
|
|
|
+ resData_2 = frequency_analysis(data_2)
|
|
|
+ print(2)
|
|
|
+ # 表3--------------------------表3水稳性大团聚体数据汇总----------------------------------------
|
|
|
+ # 数据计算 这里数据暂时还没有 数据获取到以后再进行测试
|
|
|
+ resData_3 = pd.DataFrame({
|
|
|
+ '编号': simpleData['原样品编号'],
|
|
|
+ '总和(%)': simpleData['水稳性大团聚体总和（%）'],
|
|
|
+ '＞5mm%': simpleData['水稳>5mm（%）'],
|
|
|
+ '3-5mm%': simpleData['水稳3mm~5mm（%）'],
|
|
|
+ '2-3mm%': simpleData['水稳2mm~3mm（%）'],
|
|
|
+ '1-2mm%': simpleData['水稳1mm~2mm（%）'],
|
|
|
+ '0.5-1mm%': simpleData['水稳0.5mm~1mm（%）'],
|
|
|
+ '0.25-0.5mm%': simpleData['水稳0.25mm~0.5mm（%）'],
|
|
|
+ 'pH值': simpleData['pH'],
|
|
|
+ '有机质g/kg': simpleData['有机质'],
|
|
|
+ '土地利用类型': simpleData['土地利用类型'],
|
|
|
+ '母质': simpleData['母质']
|
|
|
+ })
|
|
|
+ # 数据审核
|
|
|
+ resData_3 = resData_3.reset_index(drop=True)
|
|
|
+ res_3_v = pb.water_stable(resData_3)
|
|
|
+ resData_3['审核结果'] = res_3_v['审核结果']
|
|
|
+ global resData_3_Style
|
|
|
+ global table_3_data
|
|
|
+ table_3_data = resData_3
|
|
|
+ # 提取异常数据
|
|
|
+ global table_3_index
|
|
|
+ table_3_index = pd.DataFrame({
|
|
|
+ '样品编号': simpleData['样品编号'],
|
|
|
+ '指标': res_3_v['异常指标'],
|
|
|
+ '原因': res_3_v['审核结果']
|
|
|
+ })
|
|
|
+ resData_3_Style = resData_3.style.apply(highlight_condition, axis=1)
|
|
|
+ # 表4--------------------------表4 水稳性大团聚体频度分析-----------------------
|
|
|
+ print(3)
|
|
|
+ resData_4_need = resData_3[['总和(%)','＞5mm%','3-5mm%','2-3mm%','1-2mm%','0.5-1mm%','0.25-0.5mm%']]
|
|
|
+ global resData_4
|
|
|
+ resData_4 = frequency_analysis(resData_4_need)
|
|
|
+ print(3.1)
|
|
|
+ # 表5--------------------------表5pH、阳离子交换量、交换性盐基基础数据收集----------------------------------------
|
|
|
+ forPlus = simpleData['交换性钙'] + simpleData['交换性镁'] + simpleData['交换性钾'] + simpleData['交换性钠']
|
|
|
+ resData_5 = pd.DataFrame({
|
|
|
+ '编号': simpleData['原样品编号'],
|
|
|
+ 'pH': simpleData['pH'],
|
|
|
+ '含水量': simpleData['风干试样含水量（分析基）'],
|
|
|
+ '土壤类型': simpleData['土壤类型'],
|
|
|
+ '阳离子交换量Cmol(+)/kg': simpleData['阳离子交换量'],
|
|
|
+ '交换性盐总量Cmol(+)/kg': simpleData['交换性盐基总量'],
|
|
|
+ '交换性钙Cmol(1/2Ca2+)/kg': simpleData['交换性钙'],
|
|
|
+ '交换性镁cmol(1/2Mg2+)/kg': simpleData['交换性镁'],
|
|
|
+ '交换性钾Cmol(+)/kg': simpleData['交换性钾'],
|
|
|
+ '交换性钠cmol(+)/kg': simpleData['交换性钠'],
|
|
|
+ '四大离子之和': forPlus,
|
|
|
+ '阳交量与交盐量差': simpleData['阳离子交换量'] - simpleData['交换性盐基总量'],
|
|
|
+ '盐基饱和度%': simpleData['交换性盐基总量'] / simpleData['阳离子交换量'] # 交换性盐基/阳离子交换量
|
|
|
+ })
|
|
|
+ print(3.2)
|
|
|
+ resData_5 = resData_5.reset_index(drop=True)
|
|
|
+ res_5_v = pb.cation_value(resData_5)
|
|
|
+ print(3.3)
|
|
|
+ print('res_5_v', res_5_v)
|
|
|
+ resData_5['审核结果'] = res_5_v['审核结果']
|
|
|
+ global resData_5_Style
|
|
|
+ global table_5_data
|
|
|
+ table_5_data = resData_5
|
|
|
+ # 提取异常数据
|
|
|
+ print(3.5)
|
|
|
+ global table_5_index
|
|
|
+ table_5_index = pd.DataFrame({
|
|
|
+ '样品编号': simpleData['样品编号'],
|
|
|
+ '指标': res_5_v['异常指标'],
|
|
|
+ '原因': res_5_v['审核结果']
|
|
|
+ })
|
|
|
+ resData_5_Style = resData_5.style.apply(highlight_condition, axis=1)
|
|
|
+ print(4)
|
|
|
+ # 表6--------------------------表6----------------------------------------
|
|
|
+ global resData_6
|
|
|
+ resData_6 = frequency_analysis(resData_5[['pH']])
|
|
|
+
|
|
|
+ # 表8--------------------------表8----------------------------------------
|
|
|
+ # 离子可能存在未检测情况 对离子指标进行转数字操作 防止后续计算出错
|
|
|
+ naArr = filter_number(simpleData['水溶性Na⁺含量'])
|
|
|
+ kArr = filter_number(simpleData['水溶性K⁺含量'])
|
|
|
+ caArr = filter_number(simpleData['水溶性Ca²⁺含量'])
|
|
|
+ mgArr = filter_number(simpleData['水溶性Mg²⁺含量'])
|
|
|
+ clArr = filter_number(simpleData['水溶性Cl⁻含量'])
|
|
|
+ coArr = filter_number(simpleData['水溶性CO₃²⁻含量'])
|
|
|
+ hcoArr = filter_number(simpleData['水溶性HCO₃⁻含量'])
|
|
|
+ soArr = filter_number(simpleData['水溶性SO₄²⁻含量'])
|
|
|
+
|
|
|
+ # 数据转换
|
|
|
+ changeNa = naArr * 23 / 100
|
|
|
+ changK = kArr * 39 / 100
|
|
|
+ changeCa = caArr * 40 / 100
|
|
|
+ changeMg = mgArr * 24 / 100
|
|
|
+ changeCl = clArr * 35 / 100
|
|
|
+ changeCo = coArr * 60 / 100
|
|
|
+ changeCOH = hcoArr * 61 / 100
|
|
|
+ changeSo = soArr * 96 / 100
|
|
|
+ eightPlusArr = changeNa + changK + changeCa + changeMg + changeCl + changeCo + changeCOH + changeSo
|
|
|
+ totalCations = changeNa + changK + changeCa + changeMg
|
|
|
+ totalAnions = changeCl + changeCo + changeCOH + changeSo
|
|
|
+ allArr = filter_number(simpleData['全盐量']) # 单位g/kg
|
|
|
+ resData_8 = pd.DataFrame({
|
|
|
+ '样品编号': simpleData['原样品编号'],
|
|
|
+ 'pH': simpleData['pH'],
|
|
|
+ '水溶性全盐量g/kg': allArr,
|
|
|
+ '电导率ms/cm': simpleData['电导率'],
|
|
|
+ '水溶性钠离子含量Cmol(Na+)/kg': naArr,
|
|
|
+ '水溶性钾离子含量Cmol(K+)/kg': kArr,
|
|
|
+ '水溶性钙离子含量cmol(1/2Ca2+)/kg': caArr,
|
|
|
+ '水溶性镁离子Cmol(1/2Mg2+)/kg': mgArr,
|
|
|
+ '水溶性氯离子含量cmol(Cl-)/kg': clArr,
|
|
|
+ '水溶性碳酸根离子含量cmol(1/2CO32+)/kg': coArr,
|
|
|
+ '水溶性碳酸氢离子含量cmol(1/2HCO3-)/kg': hcoArr,
|
|
|
+ '水溶性硫酸根离子含量cmol(1/2SO42-)/kg': soArr,
|
|
|
+ '八大离子加和g/kg': eightPlusArr,
|
|
|
+ '(水溶性全盐量-八大离子加和)/水溶性全盐量×100': (allArr - eightPlusArr) / allArr * 100,
|
|
|
+ '离子总量g/kg': filter_number(simpleData['离子总量']),
|
|
|
+ '阳离子总量-阴离子总量': totalCations - totalAnions,
|
|
|
+ '土地利用类型': simpleData['土地利用类型']
|
|
|
+ })
|
|
|
+ # 调用判断函数
|
|
|
+ res_value_8 = pb.eight_ion_coun(resData_8, simpleData)
|
|
|
+ resData_8 = resData_8.reset_index(drop=True)
|
|
|
+ resData_8['审核结果'] = res_value_8['审核结果']
|
|
|
+ global resData_8_Style
|
|
|
+ global table_8_data
|
|
|
+ table_8_data = resData_8
|
|
|
+ # 提取异常数据
|
|
|
+ global table_8_index
|
|
|
+ table_8_index = pd.DataFrame({
|
|
|
+ '样品编号': simpleData['样品编号'],
|
|
|
+ '指标': res_value_8['异常指标'],
|
|
|
+ '原因': res_value_8['审核结果']
|
|
|
+ })
|
|
|
+ resData_8_Style = resData_8.style.apply(highlight_condition, axis=1)
|
|
|
+ print(5)
|
|
|
+ # 表7--------------------------表7 数据频度分析----------------------------------------
|
|
|
+ global resData_7
|
|
|
+ resData_7 = frequency_analysis(resData_8[['水溶性全盐量g/kg', '电导率ms/cm']])
|
|
|
+
|
|
|
+ # 表10--------------------------表10 有机质、全氮、全磷、全钾数据------------------------
|
|
|
+ resData_10 = pd.DataFrame({
|
|
|
+ '编号': simpleData['原样品编号'],
|
|
|
+ '有机质g/kg': simpleData['有机质'],
|
|
|
+ '全氮g/kg': simpleData['全氮'],
|
|
|
+ '全磷g/kg': simpleData['全磷'],
|
|
|
+ '有效磷g/kg': simpleData['有效磷'],
|
|
|
+ '全钾g/kg': simpleData['全钾'],
|
|
|
+ '缓效钾mg/kg': simpleData['缓效钾'],
|
|
|
+ '速效钾mg/kg': simpleData['速效钾'],
|
|
|
+ 'pH': simpleData['pH'],
|
|
|
+ '母质': simpleData['母质'],
|
|
|
+ '土地利用类型': simpleData['土地利用类型'],
|
|
|
+ '交换性钾': simpleData['交换性钾'],
|
|
|
+ '阳离子交换量': simpleData['阳离子交换量']
|
|
|
+ })
|
|
|
+ # 调用判断函数
|
|
|
+ res_value_10 = pb.nutrient_data(resData_10)
|
|
|
+ resData_10 = resData_10.reset_index(drop=True)
|
|
|
+ resData_10['审核结果'] = res_value_10['审核结果']
|
|
|
+ print(6)
|
|
|
+ # 写入表格
|
|
|
+ global resData_10_Style
|
|
|
+ global table_10_data
|
|
|
+ table_10_data = resData_10
|
|
|
+ # 提取异常数据
|
|
|
+ global table_10_index
|
|
|
+ table_10_index = pd.DataFrame({
|
|
|
+ '样品编号': simpleData['样品编号'],
|
|
|
+ '指标': res_value_10['异常指标'],
|
|
|
+ '原因': res_value_10['审核结果']
|
|
|
+ })
|
|
|
+ resData_10_Style = resData_10.style.apply(highlight_condition, axis=1)
|
|
|
+ # 表9--------------------------表9----------------------------------------
|
|
|
+ # 计算频度数据
|
|
|
+ global resData_9
|
|
|
+ resData_9 = frequency_analysis(resData_10[
|
|
|
+ ['有机质g/kg', '全氮g/kg', '全磷g/kg', '有效磷g/kg', '全钾g/kg',
|
|
|
+ '缓效钾mg/kg', '速效钾mg/kg']])
|
|
|
+ # 表12--------------------------表12 土壤指标含量----------------------------------------
|
|
|
+ resData_12 = pd.DataFrame({
|
|
|
+ '编号': simpleData['原样品编号'],
|
|
|
+ 'pH': simpleData['pH'],
|
|
|
+ '母质': simpleData['母质'],
|
|
|
+ '有机质': simpleData['有机质'],
|
|
|
+ '全氮': simpleData['全氮'],
|
|
|
+ '全磷': simpleData['全磷'],
|
|
|
+ '全钾': simpleData['全钾'],
|
|
|
+ '有效磷': simpleData['有效磷'],
|
|
|
+ '速效钾': simpleData['速效钾'],
|
|
|
+ '缓效钾': simpleData['缓效钾'],
|
|
|
+ '有效硫mg/kg': simpleData['有效硫'],
|
|
|
+ '有效硅mg/kg': simpleData['有效硅'],
|
|
|
+ '有效铁mg/kg': simpleData['有效铁'],
|
|
|
+ '有效锰mg/kg': simpleData['有效锰'],
|
|
|
+ '有效铜mg/kg': simpleData['有效铜'],
|
|
|
+ '有效锌mg/kg': simpleData['有效锌'],
|
|
|
+ '有效硼mg/kg': simpleData['有效硼'],
|
|
|
+ '有效钼mg/kg': simpleData['有效钼']
|
|
|
+ })
|
|
|
+
|
|
|
+ # 调用判断函数
|
|
|
+ res_value_12 = pb.soil_metal(resData_12)
|
|
|
+ resData_12 = resData_12.reset_index(drop=True)
|
|
|
+ resData_12['审核结果'] = res_value_12['审核结果']
|
|
|
+ global resData_12_Style
|
|
|
+ global table_12_data
|
|
|
+ table_12_data = resData_12
|
|
|
+ # 提取异常数据
|
|
|
+ global table_12_index
|
|
|
+ table_12_index = pd.DataFrame({
|
|
|
+ '样品编号': simpleData['样品编号'],
|
|
|
+ '指标': res_value_12['异常指标'],
|
|
|
+ '原因': res_value_12['审核结果']
|
|
|
+ })
|
|
|
+ resData_12_Style = resData_12.style.apply(highlight_condition, axis=1)
|
|
|
+ print(7)
|
|
|
+ # 写入表格
|
|
|
+ # 表11--------------------------表11 土壤指标频度分析----------------------------------------
|
|
|
+ global resData_11
|
|
|
+ resData_11 = frequency_analysis(resData_12[['有效硅mg/kg', '有效铁mg/kg', '有效锰mg/kg', '有效铜mg/kg',
|
|
|
+ '有效锌mg/kg', '有效硼mg/kg', '有效钼mg/kg']])
|
|
|
+
|
|
|
+ # 表14--------------------------表14 土壤重金属指标----------------------------------------
|
|
|
+ resData_14 = pd.DataFrame({
|
|
|
+ '编号': simpleData['原样品编号'],
|
|
|
+ '母质': simpleData['母质'],
|
|
|
+ '土地利用类型': simpleData['土地利用类型'],
|
|
|
+ 'pH': simpleData['pH'],
|
|
|
+ '镉mg/kg': simpleData['总镉'],
|
|
|
+ '汞mg/kg': simpleData['总汞'],
|
|
|
+ '砷mg/kg': simpleData['总砷'],
|
|
|
+ '铅mg/kg': simpleData['总铅'],
|
|
|
+ '铬mg/kg': simpleData['总铬'],
|
|
|
+ '镍mg/kg': simpleData['总镍']
|
|
|
+ })
|
|
|
+ # 调用判断函数
|
|
|
+ res_value_14 = pb.last_metal(resData_14)
|
|
|
+ resData_14 = resData_14.reset_index(drop=True)
|
|
|
+ resData_14['审核结果'] = res_value_14['审核结果']
|
|
|
+ global resData_14_Style
|
|
|
+ global table_14_data
|
|
|
+ table_14_data = resData_14
|
|
|
+ # 提取异常数据
|
|
|
+ global table_14_index
|
|
|
+ table_14_index = pd.DataFrame({
|
|
|
+ '样品编号': simpleData['样品编号'],
|
|
|
+ '指标': res_value_14['异常指标'],
|
|
|
+ '原因': res_value_14['审核结果']
|
|
|
+ })
|
|
|
+ resData_14_Style = resData_14.style.apply(highlight_condition, axis=1)
|
|
|
+ # 写入表格
|
|
|
+ # 表13--------------------------表13 土壤重金属频度分析----------------------------------------
|
|
|
+ global resData_13
|
|
|
+ print('resData_13---1', resData_13)
|
|
|
+ resData_13 = frequency_analysis(
|
|
|
+ resData_14[['镉mg/kg', '汞mg/kg', '砷mg/kg', '铅mg/kg', '铬mg/kg', '镍mg/kg']])
|
|
|
+ print('resData_13--', resData_13)
|
|
|
+ show_info('文件审核完成，请点击保存按钮保存文件！')
|
|
|
+ else:
|
|
|
+ #提示文件为空 重新选择
|
|
|
+ print("Excel 文件为空。")
|
|
|
+ except Exception as err:
|
|
|
+ print('审核过程中出错！', err)
|
|
|
+ show_error('审核过程中出错！')
|
|
|
+
|
|
|
+
|
|
|
+def makeNormalWord(url):
|
|
|
+ # 根据提取数据 合并数据 生成报告
|
|
|
+ length = len(table_1_index)
|
|
|
+ emptyArr = [np.nan for i in range(length)]
|
|
|
+ indexArr = pd.RangeIndex(start=1, stop=length+1)
|
|
|
+ newData = pd.DataFrame({
|
|
|
+ '序号': indexArr,
|
|
|
+ '样品编号': table_1_index['样品编号'],
|
|
|
+ '土地利用类型': table_1_index['土地利用类型'],
|
|
|
+ '指标': table_1_index['指标'] + table_3_index['指标'] + table_5_index['指标'] + table_8_index['指标'] + table_10_index['指标'] + table_12_index['指标'] + table_14_index['指标'],
|
|
|
+ '原因': table_1_index['原因'] + table_3_index['原因'] + table_5_index['原因'] + table_8_index['原因'] + table_10_index['原因'] + table_12_index['原因'] + table_14_index['原因'],
|
|
|
+ '结合外业调查及相关信息评价': emptyArr,
|
|
|
+ '数据判定': emptyArr
|
|
|
+ })
|
|
|
+ newData = newData.replace(np.nan, '')
|
|
|
+ name = os.path.basename(changeFileUrl)
|
|
|
+ n = name.split('.')
|
|
|
+ areaName = n[0].replace('数据', '')
|
|
|
+ # 新建文档
|
|
|
+ doc = Document()
|
|
|
+ # 添加标题
|
|
|
+ doc.add_heading(f"{areaName}数据审核过程存疑数据一览表", level=0).bold = True
|
|
|
+ table_1 = doc.add_table(rows=length + 1, cols=7, style='Light Shading Accent 1')
|
|
|
+ table_1.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ # 遍历表格 插入数据
|
|
|
+ # 遍历表格的所有单元格，并填充内容
|
|
|
+ for i, row in enumerate(table_1.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(newData.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r = paragraph.add_run(str(newData.iloc[i - 1, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ # 保存文件
|
|
|
+ doc.save(f'{url}/{areaName}存疑数据一览表.docx')
|
|
|
+
|
|
|
+################一下是GUI部分-----------------------------------------------------------------------------
|
|
|
+
|
|
|
+# 选择文件
|
|
|
+def open_file():
|
|
|
+ # 这里限制只能取 excel文件
|
|
|
+ filetypes = (
|
|
|
+ ('excel files', '*.xlsx'),
|
|
|
+ ('All files', '*.xlsx*')
|
|
|
+ )
|
|
|
+
|
|
|
+ filename = filedialog.askopenfilename(
|
|
|
+ title='选择文件',
|
|
|
+ initialdir='/',
|
|
|
+ filetypes=filetypes)
|
|
|
+
|
|
|
+ # 这里增加判断 若文件为空提示错误
|
|
|
+ simpleData = pd.read_excel(filename)
|
|
|
+ if not simpleData.empty:
|
|
|
+ global changeFileUrl
|
|
|
+ changeFileUrl = filename
|
|
|
+ titleList = ['序号', '原样品编号', '样品编号','地理位置','土壤类型','母岩','母质','土地利用类型','洗失量（吸管法需填）',
|
|
|
+ '2~0.2mm颗粒含量','0.2~0.02mm颗粒含量','0.02~0.002mm颗粒含量','0.002mm以下颗粒含量','土壤质地',
|
|
|
+ '风干试样含水量（分析基）','pH','阳离子交换量','交换性盐基总量','交换性钙','交换性镁','交换性钠',
|
|
|
+ '交换性钾','全盐量','电导率','水溶性Na⁺含量','水溶性K⁺含量','水溶性Ca²⁺含量','水溶性Mg²⁺含量',
|
|
|
+ '水溶性Cl⁻含量','水溶性CO₃²⁻含量','水溶性HCO₃⁻含量','水溶性SO₄²⁻含量', '离子总量','有机质','全氮',
|
|
|
+ '全磷','全钾','全硒','有效磷','速效钾','缓效钾', '有效硫','有效硅','有效铁', '有效锰','有效铜','有效锌',
|
|
|
+ '有效硼','有效钼','碳酸钙','总汞','总砷','总铅','总镉','总铬','总镍','土壤容重1（g/cm³）','土壤容重2（g/cm³）',
|
|
|
+ '土壤容重3（g/cm³）','土壤容重4（g/cm³）','土壤容重平均值（g/cm³）','水稳>5mm（%）','水稳3mm~5mm（%）',
|
|
|
+ '水稳2mm~3mm（%）','水稳1mm~2mm（%）','水稳0.5mm~1mm（%）','水稳0.25mm~0.5mm（%）','水稳性大团聚体总和（%）']
|
|
|
+ # 也可以增加文件内容判断 格式不正确 提示错误 这里验证表头
|
|
|
+ errList = []
|
|
|
+ for item in simpleData.columns:
|
|
|
+ if item not in titleList:
|
|
|
+ errList.append(item)
|
|
|
+ if len(errList) > 0:
|
|
|
+ show_info(f'{errList}以上指标格式错误，请按照以下格式重新填写表头：{titleList}以保证审核流程正确执行！')
|
|
|
+ else:
|
|
|
+ # 验证通过 提示框展示文件名称
|
|
|
+ show_info('文件选择完成，点击审核按钮开始审核！')
|
|
|
+ else:
|
|
|
+ show_error('文件为空，请检查文件！')
|
|
|
+
|
|
|
+
|
|
|
+# 设置字体的函数
|
|
|
+def set_font(cell):
|
|
|
+ cell.paragraphs[0].runs[0].font.name = "Times New Roman" # 设置英文字体
|
|
|
+ cell.paragraphs[0].runs[0].font.size = Pt(9) # 字体大小
|
|
|
+ cell.paragraphs[0].runs[0]._element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312') # 设置中文字体
|
|
|
+
|
|
|
+# 生成报告
|
|
|
+def getReport(data,changeFileUrl, saveFileUrl, check_1_data,
|
|
|
+ check_3_data,
|
|
|
+ check_5_data ,
|
|
|
+ check_8_data, # 样品编号替换为编号
|
|
|
+ check_10_data,
|
|
|
+ check_12_data,
|
|
|
+ check_14_data ):
|
|
|
+ # 根据选择的路径读取数据
|
|
|
+ data['原样品编号'] = data['原样品编号'].astype(str)
|
|
|
+ # checkData = pd.read_excel(changeFileUrl, sheet_name='检测方法')
|
|
|
+ # 生成报告
|
|
|
+ name = os.path.basename(changeFileUrl)
|
|
|
+ n = name.split('.')
|
|
|
+ areaName = n[0].replace('数据', '')
|
|
|
+ # 生成一个新的文件夹用于存放审核报告相关的数据
|
|
|
+ nowTime = time.strftime("%Y-%m-%d %H时%M分%S秒", time.localtime())
|
|
|
+ dir_name = f'{areaName}数据审核报告'
|
|
|
+ mkdir_path = saveFileUrl + '/' + dir_name + nowTime
|
|
|
+ if not os.path.exists(mkdir_path):
|
|
|
+ os.mkdir(mkdir_path)
|
|
|
+ # 上面这个地址，可以纯递给函数中，用于保存表格和图片
|
|
|
+ # 调用函数 开始生成报告相关内容
|
|
|
+ # 表1相关数据
|
|
|
+ typeData = report.getSimpleNum(data)
|
|
|
+ lenNum_1 = len(typeData['sData'])
|
|
|
+ lenNum_1_f = len(typeData['allData'])
|
|
|
+ table_1_data = pd.DataFrame({
|
|
|
+ '类型': typeData['sData'].index,
|
|
|
+ '数量': typeData['sData'],
|
|
|
+ '合计': [typeData['sData'].sum() for _ in range(lenNum_1)]
|
|
|
+ })
|
|
|
+ # 表2数据
|
|
|
+ table_2_data = report.getDataComplete(data)
|
|
|
+ table_2_data = table_2_data.reset_index()
|
|
|
+ table_2_data.columns = ['指标名称', '实测数量', '应测数量']
|
|
|
+ # 表3数据
|
|
|
+ # table_3_data = report.checkMethod(checkData, mkdir_path)
|
|
|
+ # 数据修约 表4
|
|
|
+ report.getNum(data, mkdir_path)
|
|
|
+ # 数据填报项审核 表5
|
|
|
+ report.dataReportResult(data, mkdir_path)
|
|
|
+ # 表6数据 土壤质地类型不一致
|
|
|
+ middData = data[['原样品编号', '样品编号']].astype(str)
|
|
|
+ middData['编号'] = middData['原样品编号']
|
|
|
+ del middData['原样品编号']
|
|
|
+ check_1_data = pd.merge(check_1_data,middData, how='left', on='编号')
|
|
|
+ check_1_data = check_1_data.replace(np.nan,'')
|
|
|
+ typeNotSame = check_1_data[check_1_data['土壤质地'] != check_1_data['土壤类型(判断)']]
|
|
|
+ table_6_data = typeNotSame[['编号','样品编号', '土壤质地', '土壤类型(判断)']]
|
|
|
+
|
|
|
+ allNeedData = pd.DataFrame({})
|
|
|
+ allNeedData['原样品编号'] = check_1_data['编号']
|
|
|
+ getSimpleDataNumber = pd.merge(allNeedData, data[['原样品编号', '样品编号']], how='left', on="原样品编号")
|
|
|
+ allNeedData['样品编号'] = getSimpleDataNumber['样品编号']
|
|
|
+ allNeedData['土地利用类型'] = check_1_data['土地利用类型']
|
|
|
+ allNeedData['审核结果'] = check_1_data['审核结果'] + check_3_data['审核结果'] + check_5_data['审核结果'] + check_8_data['审核结果'] + check_10_data['审核结果'] + check_12_data['审核结果'] + check_14_data['审核结果']
|
|
|
+ allNeedData['外业'] = ['' for _ in range(len(check_1_data))]
|
|
|
+ table_7_data = allNeedData[allNeedData['审核结果'] != '']
|
|
|
+ del table_7_data['审核结果']
|
|
|
+ # 写进表格
|
|
|
+ with pd.ExcelWriter(f'{mkdir_path}/超阈值样品统计表.xlsx', engine='openpyxl') as writer:
|
|
|
+ table_7_data.to_excel(writer, index=False, sheet_name='超阈值数据')
|
|
|
+ # 表8数据
|
|
|
+ table_8_data = report.getPHData(data, mkdir_path)
|
|
|
+ # 表10 数据
|
|
|
+ table_10_data = report.getNAndC(data, mkdir_path)
|
|
|
+ # 表11 数据：全磷和有效磷异常数据统计
|
|
|
+ table_11_data = report.getPData(data, mkdir_path)
|
|
|
+ # 表12数据 重金属超标
|
|
|
+ caOverData = pd.merge(check_1_data[['编号','土地利用类型']],check_14_data[['编号','pH','镉mg/kg','汞mg/kg', '砷mg/kg','铅mg/kg', '铬mg/kg','镍mg/kg', '审核结果']] , how='outer', on=['编号'])
|
|
|
+ caOverData['原样品编号'] = caOverData['编号']
|
|
|
+ caOverData = pd.merge(caOverData, data[['原样品编号', '样品编号']], how='left', on='原样品编号')
|
|
|
+ first_column = caOverData.pop('样品编号')
|
|
|
+ caOverData.insert(0, '样品编号', first_column)
|
|
|
+ caOverData_need = caOverData[caOverData['审核结果'] != '']
|
|
|
+ report.getKData(data, mkdir_path)
|
|
|
+ report.cationExchangeCapacity(data,mkdir_path)
|
|
|
+ report.changeCation(data, mkdir_path)
|
|
|
+ report.manyTypes(data, mkdir_path)
|
|
|
+ # 写进表格
|
|
|
+ with pd.ExcelWriter(f'{mkdir_path}/重金属超筛选值情况统计.xlsx', engine='openpyxl') as writer:
|
|
|
+ caOverData_need.to_excel(writer, index=False, sheet_name='重金属超筛选值情况统计')
|
|
|
+ # 表13 所有存疑数据
|
|
|
+ with pd.ExcelWriter(f'{mkdir_path}/数据审核过程存疑数据一览表.xlsx', engine='openpyxl') as writer:
|
|
|
+ allNeedData[allNeedData['审核结果'] != ''].to_excel(writer, index=False, sheet_name='存疑数据')
|
|
|
+ # 附表： 频度分析图
|
|
|
+ report.getFrequencyImage(data, mkdir_path)
|
|
|
+ table_f_2_data = report.getFrequencyInformation(data, mkdir_path)
|
|
|
+ print('table_f_2_data---', table_f_2_data)
|
|
|
+ # 新建一个文档
|
|
|
+ doc = Document()
|
|
|
+ # 添加标题
|
|
|
+ doc.add_heading(f"{areaName}第三次全国土壤普查数据审核报告", level=0)
|
|
|
+ # 添加一级标题
|
|
|
+ doc.add_heading('一、数据完整性审核', level=1)
|
|
|
+ doc.add_heading('1、土地利用类型与检测指标符合性审核', level=2)
|
|
|
+ # 插入表格1
|
|
|
+ paragraph_1 = doc.add_paragraph()
|
|
|
+ paragraph_1.add_run(f"表1：{areaName}三普样品数量统计表（表层）").bold = True
|
|
|
+ # 设置居中
|
|
|
+ paragraph_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ table_1 = doc.add_table(rows=lenNum_1 +1, cols=3, style='Light Shading Accent 1')
|
|
|
+ table_1.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ # 遍历表格 插入数据
|
|
|
+ # 遍历表格的所有单元格，并填充内容
|
|
|
+ for i, row in enumerate(table_1.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(table_1_data.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r = paragraph.add_run(str(table_1_data.iloc[i-1, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ #合并单元格 合并第3列的第二行和第三行
|
|
|
+ if lenNum_1 > 1:
|
|
|
+ table_1.cell(2, 2).text= ''
|
|
|
+ table_1.cell(1, 2).merge(table_1.cell(2, 2))
|
|
|
+ # table_1.cell(1, 2).vertical_alignment = WD_CELL_VERTICAL_ALIGNMENT.CENTER
|
|
|
+ # table_1.cell(2, 2).vertical_alignment = WD_CELL_VERTICAL_ALIGNMENT.CENTER
|
|
|
+ ############test##############
|
|
|
+ doc.add_heading('2、指标名称与实际检测样品数量完整性审核', level=2)
|
|
|
+ # 插入表格2
|
|
|
+ paragraph_2 = doc.add_paragraph()
|
|
|
+ paragraph_2.add_run(f'表2：{areaName}指标名称与实际检测样品数量统计表').bold = True
|
|
|
+ table_2 = doc.add_table(rows=len(table_2_data) + 1, cols=3, style='Light Shading Accent 1')
|
|
|
+ paragraph_2.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ table_2.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ for i, row in enumerate(table_2.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(table_2_data.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r = paragraph.add_run(str(table_2_data.iloc[i-1, j]))
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+
|
|
|
+
|
|
|
+ doc.add_heading('二、数据规范性审核', level=1)
|
|
|
+ doc.add_heading('1、数据填报规范性审核', level=2)
|
|
|
+ # 插入表3
|
|
|
+ paragraph_3 = doc.add_paragraph()
|
|
|
+ paragraph_3.add_run(f'表3：{areaName}土壤检测数据检测方法填报审核结果表').bold = True
|
|
|
+ # table_3 = doc.add_table(rows=2, cols=2)
|
|
|
+ paragraph_3.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # table_3.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ # 写入数据 这里数据写不下 嵌入链接
|
|
|
+ doc.add_heading('为避免数据量过多无法显示，请至数据保存文件夹中查看数据表:检测方法审核结果.xlsx', level=4)
|
|
|
+ doc.add_heading('2、数值修约规范性审核', level=2)
|
|
|
+ # 插入表4
|
|
|
+ paragraph_4 = doc.add_paragraph()
|
|
|
+ paragraph_4.add_run(f'表4：{areaName}土壤检测数据数值修约结果表').bold = True
|
|
|
+ # table_4 = doc.add_table(rows=2, cols=2)
|
|
|
+ paragraph_4.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # table_4.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ doc.add_heading('为避免数据量过多无法显示，请至数据保存文件夹中查看数据表:数值修约审核.xlsx', level=4)
|
|
|
+ # 填入数据 这里数据也放不下 嵌入链接
|
|
|
+ doc.add_heading('3、数据未检出的填报规范性审核', level=2)
|
|
|
+ # 插入表5
|
|
|
+ paragraph_5 = doc.add_paragraph()
|
|
|
+ paragraph_5.add_run(f'表5：{areaName}土壤检测数据未检出项填报审核结果表').bold = True
|
|
|
+ # table_5 = doc.add_table(rows=2, cols=2)
|
|
|
+ paragraph_5.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # table_5.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ # 写入数据 这里数据也放不下 嵌入链接
|
|
|
+ doc.add_heading('为避免数据量过多无法显示，请至数据保存文件夹中查看数据表:数据填报项审核结果.xlsx', level=4)
|
|
|
+ doc.add_heading('4、土壤质地填报规范性审核', level=2)
|
|
|
+ # 插入表6
|
|
|
+ paragraph_6 = doc.add_paragraph()
|
|
|
+ paragraph_6.add_run(f'表6：{areaName}土壤质地填报审核结果表').bold = True
|
|
|
+ table_6 = doc.add_table(rows=len(table_6_data)+1, cols=4, style='Light Shading Accent 1')
|
|
|
+ paragraph_6.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ table_6.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ # 提取结果表中数据
|
|
|
+ # 写入数据 土壤质地类型不一致的数据提取出来
|
|
|
+ for i, row in enumerate(table_6.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(table_6_data.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r=paragraph.add_run(str(table_6_data.iloc[i-1, j]))
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ doc.add_heading('三、数据合理性审核', level=1)
|
|
|
+ doc.add_heading('1、阈值法审核', level=2)
|
|
|
+ # 插入表格
|
|
|
+ paragraph_7 = doc.add_paragraph()
|
|
|
+ paragraph_7.add_run(f'表7：{areaName}土壤检测数据超阈值样品统计表').bold = True
|
|
|
+ # table_7 = doc.add_table(rows=2, cols=2)
|
|
|
+ # paragraph_7.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # table_7.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ # 写入数据 点击查看数据 这里也不一定写的下 最好是嵌入链接
|
|
|
+ doc.add_heading('为避免数据量过多无法显示，请至数据保存文件夹中查看数据表:数据审核过程存疑数据一览表.xlsx', level=4)
|
|
|
+ # todo 合并所有数据 审核结果不为空的数据 写入表格保存到指定文件夹
|
|
|
+ doc.add_heading('2、极值法审核', level=2)
|
|
|
+ doc.add_heading('（1）pH', level=3)
|
|
|
+ # 插入ph分布图
|
|
|
+ if os.path.isfile(f'{mkdir_path}/PH值分布图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/PH值分布图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_1 = doc.add_paragraph()
|
|
|
+ paragraph_t_1.add_run(f'图1：pH值分布情况').bold = True
|
|
|
+ paragraph_t_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # 插入频度统计表
|
|
|
+ paragraph_8 = doc.add_paragraph()
|
|
|
+ paragraph_8.add_run('表8：pH数据统计表').bold = True
|
|
|
+ table_8 = doc.add_table(rows=6, cols=2, style='Light Shading Accent 1')
|
|
|
+ t_8 = table_8_data['频度分析']
|
|
|
+ t_8 = t_8.reset_index()
|
|
|
+ t_8.columns = ['指标', '数据']
|
|
|
+ paragraph_8.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ table_8.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ for i, row in enumerate(table_8.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(t_8.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r=paragraph.add_run(str(t_8.iloc[i-1, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ # 插入异常数据提取表格 todo 这里数据多的话也可能写不下 最好是嵌入一
|
|
|
+ t_9 = table_8_data['异常数据']
|
|
|
+ if not t_9.empty:
|
|
|
+ paragraph_9 = doc.add_paragraph()
|
|
|
+ paragraph_9.add_run('表9：pH异常数据统计表').bold = True
|
|
|
+ table_9 = doc.add_table(rows=len(table_8_data['异常数据']) + 1, cols=6, style='Light Shading Accent 1')
|
|
|
+ paragraph_9.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ table_9.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ for i, row in enumerate(table_9.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(t_9.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r=paragraph.add_run(str(t_9.iloc[i-1, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ doc.add_heading('3、关联分析法审核', level=2)
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有机质与全氮相关性分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有机质与全氮相关性分析图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_2 = doc.add_paragraph()
|
|
|
+ paragraph_t_2.add_run(f'图2：有机质与全氮相关关系').bold = True
|
|
|
+ paragraph_t_2.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # 插入碳氮比异常数据
|
|
|
+ if not table_10_data.empty:
|
|
|
+ paragraph_10 = doc.add_paragraph()
|
|
|
+ paragraph_10.add_run('表10：碳氮比异常数据统计表').bold = True
|
|
|
+ table_10 = doc.add_table(rows=len(table_10_data)+1, cols=8, style='Light Shading Accent 1')
|
|
|
+ paragraph_10.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ table_10.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ for i, row in enumerate(table_10.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(table_10_data.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r=paragraph.add_run(str(table_10_data.iloc[i-1, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ doc.add_heading('4、指标综合分析', level=2)
|
|
|
+ # 插入图片
|
|
|
+ if os.path.isfile(f'{mkdir_path}/全磷分布图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/全磷分布图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_3 = doc.add_paragraph()
|
|
|
+ paragraph_t_3.add_run(f'图3：全磷分布图').bold = True
|
|
|
+ paragraph_t_3.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效磷分布图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效磷分布图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_4 = doc.add_paragraph()
|
|
|
+ paragraph_t_4.add_run(f'图4：有效磷分布图').bold = True
|
|
|
+ paragraph_t_4.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # 插入图片
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效磷占全磷比分布图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效磷占全磷比分布图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_5 = doc.add_paragraph()
|
|
|
+ paragraph_t_5.add_run(f'图5：有效磷含量占全磷含量比例').bold = True
|
|
|
+ paragraph_t_5.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # 插入表格
|
|
|
+ if not table_11_data.empty:
|
|
|
+ paragraph_11 = doc.add_paragraph()
|
|
|
+ paragraph_11.add_run('表11：全磷与有效磷异常样品统计表').bold = True
|
|
|
+ table_11 = doc.add_table(rows=len(table_11_data)+1, cols=7, style='Light Shading Accent 1')
|
|
|
+ paragraph_11.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ table_11.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ for i, row in enumerate(table_11.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(table_11_data.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r=paragraph.add_run(str(table_11_data.iloc[i-1, j]))
|
|
|
+
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ else:
|
|
|
+ paragraph_11 = doc.add_paragraph()
|
|
|
+ paragraph_11.add_run('表11：全磷与有效磷异常样品统计表').bold = True
|
|
|
+ paragraph_11_info = doc.add_paragraph()
|
|
|
+ paragraph_11_info.add_run('无异常数据')
|
|
|
+ paragraph_11.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ paragraph_11_info.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # 全钾、速效钾、缓效钾
|
|
|
+ if os.path.isfile(f'{mkdir_path}/全钾与速效钾缓效钾之和关系统计图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/全钾与速效钾缓效钾之和关系统计图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_6 = doc.add_paragraph()
|
|
|
+ paragraph_t_6.add_run(f'图6：全钾与速效钾缓效钾之和关系统计图').bold = True
|
|
|
+ paragraph_t_6.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+
|
|
|
+ if os.path.isfile(f'{mkdir_path}/速效钾与缓效钾关系统计图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/速效钾与缓效钾关系统计图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_7 = doc.add_paragraph()
|
|
|
+ paragraph_t_7.add_run(f'图7：速效钾与缓效钾关系统计图').bold = True
|
|
|
+ paragraph_t_7.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ doc.add_heading('表12：重金属超筛选值情况统计', level=4)
|
|
|
+ doc.add_heading('为避免数据量过多无法显示，请至数据保存文件夹中查看数据表:重金属超筛选值情况统计表.xlsx', level=4)
|
|
|
+ # todo 获取重金属数据
|
|
|
+ # 阳离子交换量与交换性盐总量关系
|
|
|
+ if os.path.isfile(f'{mkdir_path}/阳离子交换量与交换性盐基总量相关关系.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/阳离子交换量与交换性盐基总量相关关系.png', width=Inches(6.0))
|
|
|
+ paragraph_t_8 = doc.add_paragraph()
|
|
|
+ paragraph_t_8.add_run(f'图8：阳离子交换量与交换性盐总量关系图').bold = True
|
|
|
+ paragraph_t_8.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ # 交换性盐总量与交换性盐相关关系
|
|
|
+ if os.path.isfile(f'{mkdir_path}/交换性盐基总量与交换性盐相关关系(pH小于等于7.5).png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/交换性盐基总量与交换性盐相关关系(pH小于等于7.5).png', width=Inches(6.0))
|
|
|
+ paragraph_t_9 = doc.add_paragraph()
|
|
|
+ paragraph_t_9.add_run(f'图9：交换性盐基总量和交换性钙镁钠钾分项指标关系（pH≤7.5）').bold = True
|
|
|
+ paragraph_t_9.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+
|
|
|
+ if os.path.isfile(f'{mkdir_path}/交换性盐基总量与交换性盐相关关系(pH大于7.5).png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/交换性盐基总量与交换性盐相关关系(pH大于7.5).png', width=Inches(6.0))
|
|
|
+ paragraph_t_10 = doc.add_paragraph()
|
|
|
+ paragraph_t_10.add_run(f'图10：交换性盐基总量和交换性钙镁钠钾分项指标关系（pH大于7.5）').bold = True
|
|
|
+ paragraph_t_10.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+
|
|
|
+ # 水溶性盐、电导率、离子总量
|
|
|
+ if os.path.isfile(f'{mkdir_path}/全盐量分布图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/全盐量分布图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_11 = doc.add_paragraph()
|
|
|
+ paragraph_t_11.add_run(f'图11：全盐量分布图').bold = True
|
|
|
+ paragraph_t_11.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+
|
|
|
+ if os.path.isfile(f'{mkdir_path}/全盐量与电导率相关性分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/全盐量与电导率相关性分析图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_12 = doc.add_paragraph()
|
|
|
+ paragraph_t_12.add_run(f'图12：全盐量与电导率相关性分析图').bold = True
|
|
|
+ paragraph_t_12.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+
|
|
|
+ if os.path.isfile(f'{mkdir_path}/离子总量与水溶性盐总量关系图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/离子总量与水溶性盐总量关系图.png', width=Inches(6.0))
|
|
|
+ paragraph_t_13 = doc.add_paragraph()
|
|
|
+ paragraph_t_13.add_run(f'图13：水溶性盐总量与离子总量关系分析图').bold = True
|
|
|
+ paragraph_t_13.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+
|
|
|
+ doc.add_heading('四、审核存疑数据', level=1)
|
|
|
+ paragraph_12 = doc.add_paragraph()
|
|
|
+ paragraph_12.add_run(f'表13：数据审核过程存疑数据一览表').bold = True
|
|
|
+ paragraph_12.alignment = WD_ALIGN_PARAGRAPH.CENTER
|
|
|
+ doc.add_heading('为避免数据量过多无法显示，请至数据保存文件夹中查看数据表:数据审核过程存疑数据一览表.xlsx', level=4)
|
|
|
+ doc.add_heading('五、附表', level=1)
|
|
|
+ doc.add_heading('附表1：某区三普样品数量统计表（表层）', level=2)
|
|
|
+ # 插入附表1
|
|
|
+ table_1_f = doc.add_table(rows=lenNum_1 +1, cols=3, style='Light Shading Accent 1')
|
|
|
+ table_1_f.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ # 遍历表格 插入数据
|
|
|
+ # 遍历表格的所有单元格，并填充内容
|
|
|
+ for i, row in enumerate(table_1_f.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(table_1_data.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r = paragraph.add_run(str(table_1_data.iloc[i-1, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ #合并单元格 合并第3列的第二行和第三行
|
|
|
+ if lenNum_1 >1 :
|
|
|
+ table_1_f.cell(2, 2).text = ''
|
|
|
+ table_1_f.cell(1, 2).merge(table_1_f.cell(2, 2))
|
|
|
+ doc.add_heading('附表2：各指标频度分析表', level=2)
|
|
|
+ # 插入表格 写入数据
|
|
|
+ table_f_2_data = table_f_2_data.replace(np.nan, '')
|
|
|
+ # table_f_2 = doc.add_table(rows=len(table_f_2_data)+1, cols=6, style='Light Shading Accent 1')
|
|
|
+ rows = (int(len(table_f_2_data.columns) / 6)+1)
|
|
|
+ columnsList = np.arange(0, rows * 6, 6)
|
|
|
+ dataList = []
|
|
|
+
|
|
|
+ for i in columnsList:
|
|
|
+ res = table_f_2_data.iloc[:, i:i + 6]
|
|
|
+ res = res.reset_index()
|
|
|
+ dataList.append(res)
|
|
|
+ table_f_2 = doc.add_table(rows=rows * 6, cols=7, style='Light Shading Accent 1')
|
|
|
+ for i, row in enumerate(table_f_2.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == columnsList[0]:
|
|
|
+ # 第一行 显示前6个指标的列名
|
|
|
+ if len(dataList[0].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[0].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) > 1 and i > columnsList[0] and i < columnsList[1]:
|
|
|
+ if len(dataList[0].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[0].iloc[i - 1, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[1]:
|
|
|
+ # 第6行 显示前6个指 标的列名
|
|
|
+ if len(dataList[1].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[1].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) > 2 and i > columnsList[1] and i < columnsList[2]:
|
|
|
+ if len(dataList[1].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[1].iloc[i - 7, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[2]:
|
|
|
+ # 第6*2行 显示前6个指 标的列名
|
|
|
+ if len(dataList[2].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[2].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) > 3 and i > columnsList[2] and i < columnsList[3]:
|
|
|
+ if len(dataList[2].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[2].iloc[i - 13, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[3]:
|
|
|
+ # 第6*3行 显示前6个指 标的列名
|
|
|
+ if len(dataList[3].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[3].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) > 4 and i > columnsList[3] and i < columnsList[4]:
|
|
|
+ if len(dataList[3].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[3].iloc[i - 19, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[4]:
|
|
|
+ # 第6*4行 显示前6个指 标的列名
|
|
|
+ if len(dataList[4].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[4].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) > 5 and i > columnsList[4] and i < columnsList[5]:
|
|
|
+ if len(dataList[4].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[4].iloc[i - 25, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[5]:
|
|
|
+ # 第6*5行 显示前6个指 标的列名
|
|
|
+ if len(dataList[5].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[5].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) > 6 and i > columnsList[5] and i < columnsList[6]:
|
|
|
+ if len(dataList[5].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[5].iloc[i - 31, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[6]:
|
|
|
+ # 第6*6行 显示前6个指 标的列名
|
|
|
+ if len(dataList[6].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[6].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) > 7 and i > columnsList[6] and i < columnsList[7]:
|
|
|
+ if len(dataList[6].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[6].iloc[i - 37, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[7]:
|
|
|
+ # 第6*7行 显示前6个指 标的列名
|
|
|
+ if len(dataList[7].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[7].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) >= 8 and i > columnsList[7] and i < columnsList[8]:
|
|
|
+ if len(dataList[7].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[7].iloc[i - 43, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[8]:
|
|
|
+ if len(dataList[8].columns) > j:
|
|
|
+ # 第6*8行 显示前6个指 标的列名
|
|
|
+ r = paragraph.add_run(dataList[8].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) >= 9 and i > columnsList[8] and i < columnsList[9]:
|
|
|
+ if len(dataList[8].columns) > j:
|
|
|
+ r = paragraph.add_run(str(dataList[8].iloc[i - 49, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif i == columnsList[9]:
|
|
|
+ # 第6*9行 显示前6个指 标的列名
|
|
|
+ if len(dataList[9].columns) > j:
|
|
|
+ r = paragraph.add_run(dataList[9].columns[j])
|
|
|
+ r.font.bold = True
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ elif len(columnsList) >= 10 and i > columnsList[9] and i <= 60:
|
|
|
+ if len(dataList[9].columns) > j:
|
|
|
+ print(dataList[9].columns)
|
|
|
+ print(str(dataList[9].iloc[i - 55, j]))
|
|
|
+ r = paragraph.add_run(str(dataList[9].iloc[i - 55, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ else:
|
|
|
+ paragraph.add_run('')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+
|
|
|
+
|
|
|
+ # for i, row in enumerate(table_f_2.rows):
|
|
|
+ # for j, cell in enumerate(row.cells):
|
|
|
+ # # 获取单元格中的段落对象
|
|
|
+ # paragraph = cell.paragraphs[0]
|
|
|
+ # if i == 0:
|
|
|
+ # r = paragraph.add_run(str(table_f_2_data.columns[j]))
|
|
|
+ # r.font.bold = True
|
|
|
+ # else:
|
|
|
+ # r=paragraph.add_run(str(table_f_2_data.iloc[i-1, j]))
|
|
|
+ # r.font.size = Pt(10.5)
|
|
|
+ # r.font.name = 'Times New Roman'
|
|
|
+ # r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ # paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ # paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ # paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+
|
|
|
+ # doc.add_heading('为避免数据量过多无法显示，请至数据保存文件夹中查看数据表:频度分析表.xlsx', level=4)
|
|
|
+ doc.add_heading('附表3：各指标频度分析图', level=2)
|
|
|
+ # 插入频度信息的图形
|
|
|
+ if os.path.isfile(f'{mkdir_path}/0.002mm以下颗粒含量分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/0.002mm以下颗粒含量分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/0.02~0.002mm颗粒含量分析图.png.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/0.02~0.002mm颗粒含量分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/0.2~0.02mm颗粒含量分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/0.2~0.02mm颗粒含量分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/2~0.2mm颗粒含量分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/2~0.2mm颗粒含量分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/pH分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/pH分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/电导率分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/电导率分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/风干试样含水量(分析基)分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/风干试样含水量(分析基)分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/缓效钾分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/缓效钾分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/交换性钙分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/交换性钙分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/交换性钾分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/交换性钾分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/交换性镁分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/交换性镁分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/交换性钠分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/交换性钠分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/交换性盐基总量分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/交换性盐基总量分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/全氮分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/全氮分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/全钾分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/全钾分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/全磷分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/全磷分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/全盐量分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/全盐量分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/速效钾分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/速效钾分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/洗失量（吸管法需填）分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/洗失量（吸管法需填）分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/阳离子交换量分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/阳离子交换量分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有机质分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有机质分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效硅分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效硅分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效磷分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效磷分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效硫分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效硫分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效锰分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效锰分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效钼分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效钼分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效硼分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效硼分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效铁分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效铁分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效铜分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效铜分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/有效锌分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/有效锌分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/总镉分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/总镉分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/总铬分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/总铬分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/总汞分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/总汞分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/总镍分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/总镍分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/总砷分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/总砷分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/总铅分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/总铅分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/土壤容重1分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/土壤容重1分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/土壤容重2分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/土壤容重2分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/土壤容重3分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/土壤容重3分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/土壤容重4分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/土壤容重4分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/土壤容重平均值分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/土壤容重平均值分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/水稳0.5mm~1mm分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/水稳0.5mm~1mm分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/水稳0.25mm~0.5mm分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/水稳0.25mm~0.5mm分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/水稳1mm~2mm分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/水稳1mm~2mm分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/水稳2mm~3mm分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/水稳2mm~3mm分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/水稳3mm~5mm分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/水稳3mm~5mm分析图.png', width=Inches(6.0))
|
|
|
+ if os.path.isfile(f'{mkdir_path}/水稳5mm分析图.png'):
|
|
|
+ doc.add_picture(f'{mkdir_path}/水稳5mm分析图.png', width=Inches(6.0))
|
|
|
+ doc.add_heading('附表4：数值修约标准', level=2)
|
|
|
+ # 读取数据 插入表格 写入数据
|
|
|
+ numData = pd.read_excel('./img/数值修约要求.xlsx', sheet_name='Sheet1')
|
|
|
+ table_2_f = doc.add_table(rows=len(numData)+1, cols=2, style='Light Shading Accent 1')
|
|
|
+ table_2_f.alignment = WD_TABLE_ALIGNMENT.CENTER
|
|
|
+ for i, row in enumerate(table_2_f.rows):
|
|
|
+ for j, cell in enumerate(row.cells):
|
|
|
+ # 获取单元格中的段落对象
|
|
|
+ paragraph = cell.paragraphs[0]
|
|
|
+ if i == 0:
|
|
|
+ r = paragraph.add_run(str(numData.columns[j]))
|
|
|
+ r.font.bold = True
|
|
|
+ else:
|
|
|
+ r=paragraph.add_run(str(numData.iloc[i-1, j]))
|
|
|
+ r.font.size = Pt(10.5)
|
|
|
+ r.font.name = 'Times New Roman'
|
|
|
+ r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
|
|
|
+ paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
|
|
|
+ paragraph.paragraph_format.line_spacing = 1 # 段落行间距
|
|
|
+ # 处理样式 遍历所有的段落 修改字体
|
|
|
+ # 遍历并打印每个段落的文本
|
|
|
+ paragraphs = doc.paragraphs
|
|
|
+ for paragraph in paragraphs:
|
|
|
+ for run in paragraph.runs:
|
|
|
+ run.font.color.rgb = RGBColor(0, 0, 0)
|
|
|
+ run.font.name = 'Times New Roman'
|
|
|
+ run.font.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+
|
|
|
+ # run.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
|
|
|
+ # 保存Word文档
|
|
|
+ doc.save(f'{mkdir_path}/{areaName}审核报告.docx')
|
|
|
+
|
|
|
+
|
|
|
+# 预处理数据
|
|
|
+def dealData(data):
|
|
|
+ simpleData = data.dropna(subset=['原样品编号'])
|
|
|
+ simpleData = simpleData[~simpleData['原样品编号'].str.contains('ZK')]
|
|
|
+ simpleData = simpleData.replace(r'[^.\w]+', '', regex=True)
|
|
|
+ # print('simpleData',simpleData)
|
|
|
+ simpleData = simpleData.replace('未检测', np.nan)
|
|
|
+ simpleData = simpleData.replace('', np.nan)
|
|
|
+ simpleData.iloc[:, 3:] = simpleData.iloc[:, 3:].apply(pd.to_numeric, errors='ignore')
|
|
|
+ strList = ['原样品编号', '样品编号', '地理位置', '土壤类型', '母质', '土地利用类型', '土壤质地']
|
|
|
+ for i in simpleData.columns:
|
|
|
+ if i not in strList:
|
|
|
+ simpleData[i] = pd.to_numeric(simpleData[i], errors='ignore')
|
|
|
+ # 处理重复样品
|
|
|
+ res = getRepeat(simpleData)
|
|
|
+ simpleData = simpleData._append(res).drop_duplicates(subset=['原样品编号'], keep='last')
|
|
|
+
|
|
|
+ return simpleData
|
|
|
+
|
|
|
+
|
|
|
+# 保存文件 可选择保存路径
|
|
|
+def saveFile():
|
|
|
+ # 根据选择的路径 保存文件
|
|
|
+ folder_selected = filedialog.askdirectory()
|
|
|
+ # 如果用户选择了文件夹，则打印路径
|
|
|
+ try:
|
|
|
+ if folder_selected:
|
|
|
+ # 新建一个文件夹
|
|
|
+ # 获取当前时间
|
|
|
+ nowTime = time.strftime("%Y-%m-%d %H时%M分%S秒", time.localtime())
|
|
|
+ dir_name = '土壤数据审核结果'
|
|
|
+ mkdir_path = folder_selected + '/' + dir_name + nowTime
|
|
|
+ global saveFileUrl
|
|
|
+ saveFileUrl = folder_selected
|
|
|
+ if not os.path.exists(mkdir_path):
|
|
|
+ os.mkdir(mkdir_path)
|
|
|
+ # 统一写入表格
|
|
|
+ # 创建一个html文件夹 保存图表
|
|
|
+ # 将 HTML 保存到文件
|
|
|
+ for i in htmlContent:
|
|
|
+ output_html_path = mkdir_path + '/'+ i['name'] + '.html'
|
|
|
+ with open(output_html_path, 'w', encoding='utf-8') as html_file:
|
|
|
+ html_file.write(i['content'])
|
|
|
+ # 生成存疑数据表
|
|
|
+ makeNormalWord(mkdir_path)
|
|
|
+ # 表1 表2 土壤容重机械组成数据 土壤容重与机械组成总体数据频度分析
|
|
|
+ with pd.ExcelWriter(mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx', engine='openpyxl') as writer:
|
|
|
+ resData_1_Style.to_excel(writer, index=False, sheet_name='土壤容重数据')
|
|
|
+ resData_2.to_excel(writer, sheet_name='频度分析')
|
|
|
+ autoColumns(mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx')
|
|
|
+ # 保存并写入频度统计图
|
|
|
+ nowTable = pd.read_excel(mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx',sheet_name='土壤容重数据')
|
|
|
+ getStatisticsImg(nowTable['土壤容重平均值(g/cm3)(计算)'],'土壤容重(g/cm3)','土壤容重',mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx',mkdir_path,'B19')
|
|
|
+ getStatisticsImg(nowTable['洗失量(吸管法需填)%'],'洗失量(吸管法需填)%','洗失量',mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx',mkdir_path,'C19')
|
|
|
+ getStatisticsImg(nowTable['2-0.2mm颗粒含量%'],'2-0.2mm颗粒含量%','2-0.2mm颗粒含量%',mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx',mkdir_path,'D19')
|
|
|
+ getStatisticsImg(nowTable['0.2-0.02mm颗粒含量%'],'0.2~0.02mm颗粒含量%','0.2~0.02mm颗粒含量%',mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx',mkdir_path,'E19')
|
|
|
+ getStatisticsImg(nowTable['0.02-0.002mm颗粒含量%'],'0.02~0.002mm颗粒含量%','0.02~0.002mm颗粒含量%',mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx',mkdir_path,'F19')
|
|
|
+ getStatisticsImg(nowTable['0.002mm以下颗粒含量%'],'0.002mm以下颗粒含量%','0.002mm以下颗粒含量%',mkdir_path + '/土壤容重数据-' + nowTime +'.xlsx',mkdir_path,'G19')
|
|
|
+ # 表3 表4
|
|
|
+ with pd.ExcelWriter(mkdir_path + '/水稳性大团聚体数据-' + nowTime +'.xlsx', engine='openpyxl') as writer:
|
|
|
+ resData_3_Style.to_excel(writer, index=False, sheet_name='水稳性大团聚体数据')
|
|
|
+ resData_4.to_excel(writer, sheet_name='频度分析')
|
|
|
+ autoColumns(mkdir_path + '/水稳性大团聚体数据-' + nowTime +'.xlsx')
|
|
|
+ # 保存并写入频度统计图
|
|
|
+ nowTable_sw = pd.read_excel(mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx', sheet_name='水稳性大团聚体数据')
|
|
|
+ imgData = nowTable_sw.dropna(subset=['有机质g/kg','总和(%)'])
|
|
|
+ if not imgData['有机质g/kg'].empty and not imgData['总和(%)'].empty:
|
|
|
+ getImg(imgData['有机质g/kg'],imgData['总和(%)'],mkdir_path,
|
|
|
+ '有机质与总和相关性分析图','水稳性大团聚体数据','有机质g/kg','总和(%)',imgData['编号'],
|
|
|
+ mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx','N1')
|
|
|
+ getStatisticsImg(nowTable_sw['总和(%)'], '总和(%)', '总和(%)',
|
|
|
+ mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx', mkdir_path, 'B19')
|
|
|
+ getStatisticsImg(nowTable_sw['＞5mm%'], '＞5mm%', '＞5mm%',
|
|
|
+ mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx', mkdir_path, 'C19')
|
|
|
+ getStatisticsImg(nowTable_sw['3-5mm%'], '3-5mm%', '3-5mm%',
|
|
|
+ mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx', mkdir_path, 'D19')
|
|
|
+ getStatisticsImg(nowTable_sw['2-3mm%'], '2-3mm%', '2-3mm%',
|
|
|
+ mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx', mkdir_path, 'E19')
|
|
|
+ getStatisticsImg(nowTable_sw['1-2mm%'], '1-2mm%', '1-2mm%',
|
|
|
+ mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx', mkdir_path, 'F19')
|
|
|
+ getStatisticsImg(nowTable_sw['0.5-1mm%'], '0.5-1mm%', '0.5-1mm%',
|
|
|
+ mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx', mkdir_path, 'G19')
|
|
|
+ getStatisticsImg(nowTable_sw['0.25-0.5mm%'], '0.25-0.5mm%', '0.25-0.5mm%',
|
|
|
+ mkdir_path + '/水稳性大团聚体数据-' + nowTime + '.xlsx', mkdir_path, 'H19')
|
|
|
+ # 表5 表6
|
|
|
+
|
|
|
+ with pd.ExcelWriter(mkdir_path + '/土壤离子数据-' + nowTime +'.xlsx', engine='openpyxl') as writer:
|
|
|
+ resData_5_Style.to_excel(writer, index=False, sheet_name='土壤离子数据')
|
|
|
+ resData_6.to_excel(writer, sheet_name='频度分析')
|
|
|
+ autoColumns(mkdir_path + '/土壤离子数据-' + nowTime +'.xlsx')
|
|
|
+ nowTable_lz= pd.read_excel(mkdir_path + '/土壤离子数据-' + nowTime + '.xlsx',
|
|
|
+ sheet_name='土壤离子数据')
|
|
|
+ getStatisticsImg(nowTable_lz['pH'], 'pH', 'pH',
|
|
|
+ mkdir_path + '/土壤离子数据-' + nowTime + '.xlsx', mkdir_path, 'B19')
|
|
|
+ # 表7 表8
|
|
|
+
|
|
|
+ with pd.ExcelWriter(mkdir_path + '/土壤水溶性盐数据-' + nowTime +'.xlsx', engine='openpyxl') as writer:
|
|
|
+ resData_8_Style.to_excel(writer, index=False, sheet_name='水溶性盐数据')
|
|
|
+ resData_7.to_excel(writer, sheet_name='频度分析')
|
|
|
+ autoColumns(mkdir_path + '/土壤水溶性盐数据-' + nowTime +'.xlsx')
|
|
|
+ nowTable_sr = pd.read_excel(mkdir_path + '/土壤水溶性盐数据-' + nowTime + '.xlsx',
|
|
|
+ sheet_name='水溶性盐数据')
|
|
|
+ imgData_sr = nowTable_sr.dropna(subset=['水溶性全盐量g/kg', '电导率ms/cm'])
|
|
|
+ getImg(imgData_sr['水溶性全盐量g/kg'],imgData_sr['电导率ms/cm'],mkdir_path,'水溶性盐电导率相关性分析图',
|
|
|
+ '水溶性盐数据', '水溶性全盐量g/kg','电导率ms/cm',
|
|
|
+ imgData_sr['样品编号'],mkdir_path + '/土壤水溶性盐数据-' + nowTime + '.xlsx','T1')
|
|
|
+ getStatisticsImg(nowTable_sr['水溶性全盐量g/kg'], '水溶性全盐量g/kg', '水溶性全盐量',
|
|
|
+ mkdir_path + '/土壤水溶性盐数据-' + nowTime + '.xlsx', mkdir_path, 'B19')
|
|
|
+ getStatisticsImg(nowTable_sr['电导率ms/cm'], '电导率ms/cm', '电导率',
|
|
|
+ mkdir_path + '/土壤水溶性盐数据-' + nowTime + '.xlsx', mkdir_path, 'C19')
|
|
|
+ # 表9 表10
|
|
|
+ with pd.ExcelWriter(mkdir_path + '/土壤氮磷钾数据-' + nowTime +'.xlsx', engine='openpyxl') as writer:
|
|
|
+ resData_10_Style.to_excel(writer, index=False, sheet_name='土壤氮磷钾数据')
|
|
|
+ resData_9.to_excel(writer, sheet_name='频度分析')
|
|
|
+ autoColumns(mkdir_path + '/土壤氮磷钾数据-' + nowTime +'.xlsx')
|
|
|
+
|
|
|
+ nowTable_NPK = pd.read_excel(mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx',
|
|
|
+ sheet_name='土壤氮磷钾数据')
|
|
|
+ # 插入分析图
|
|
|
+ imgData_NPK = nowTable_NPK.dropna(subset=['有机质g/kg', '全氮g/kg'])
|
|
|
+ cationImgData = nowTable_NPK.dropna(subset=['有机质g/kg', '阳离子交换量'])
|
|
|
+ getImg(imgData_NPK['有机质g/kg'],imgData_NPK['全氮g/kg'],mkdir_path,'有机质和全氮相关性分析图','土壤氮磷钾数据',
|
|
|
+ '有机质g/kg','全氮g/kg',imgData_NPK['编号'],mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx','P1')
|
|
|
+ getImg(cationImgData['有机质g/kg'], cationImgData['阳离子交换量'], mkdir_path, '有机质和阳离子交换量相关性分析图',
|
|
|
+ '土壤氮磷钾数据',
|
|
|
+ '有机质g/kg', '阳离子交换量', cationImgData['编号'], mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx',
|
|
|
+ 'P6')
|
|
|
+ getStatisticsImg(nowTable_NPK['有机质g/kg'], '有机质g/kg', '有机质',
|
|
|
+ mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx', mkdir_path, 'B19')
|
|
|
+ getStatisticsImg(nowTable_NPK['全氮g/kg'], '全氮g/kg', '全氮',
|
|
|
+ mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx', mkdir_path, 'C19')
|
|
|
+ getStatisticsImg(nowTable_NPK['全磷g/kg'], '全磷g/kg', '全磷',
|
|
|
+ mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx', mkdir_path, 'D19')
|
|
|
+ getStatisticsImg(nowTable_NPK['有效磷g/kg'], '有效磷g/kg', '有效磷',
|
|
|
+ mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx', mkdir_path, 'E19')
|
|
|
+ getStatisticsImg(nowTable_NPK['全钾g/kg'], '全钾g/kg', '全钾',
|
|
|
+ mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx', mkdir_path, 'F19')
|
|
|
+ getStatisticsImg(nowTable_NPK['缓效钾mg/kg'], '缓效钾mg/kg', '缓效钾',
|
|
|
+ mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx', mkdir_path, 'G19')
|
|
|
+ getStatisticsImg(nowTable_NPK['速效钾mg/kg'], '速效钾mg/kg', '速效钾',
|
|
|
+ mkdir_path + '/土壤氮磷钾数据-' + nowTime + '.xlsx', mkdir_path, 'H19')
|
|
|
+ # 表11 表12
|
|
|
+
|
|
|
+ with pd.ExcelWriter(mkdir_path + '/土壤金属指标数据-' + nowTime +'.xlsx', engine='openpyxl') as writer:
|
|
|
+ resData_12_Style.to_excel(writer, index=False, sheet_name='土壤金属指标数据')
|
|
|
+ resData_11.to_excel(writer, sheet_name='频度分析')
|
|
|
+ autoColumns(mkdir_path + '/土壤金属指标数据-' + nowTime +'.xlsx')
|
|
|
+
|
|
|
+ nowTable_js = pd.read_excel(mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx',
|
|
|
+ sheet_name='土壤金属指标数据')
|
|
|
+ # 插入相关性分析图
|
|
|
+ # 铁与ph相关性
|
|
|
+ imgDataF = nowTable_js.dropna(subset=['有效铁mg/kg', 'pH'])
|
|
|
+ getImg(imgDataF['有效铁mg/kg'], imgDataF['pH'], mkdir_path, '有效铁与ph相关性分析图',
|
|
|
+ '土壤金属指标数据',
|
|
|
+ '有效铁mg/kg', 'pH', imgDataF['编号'], mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx',
|
|
|
+ 'T1')
|
|
|
+ # 锰与ph相关性
|
|
|
+ imgDataMe = nowTable_js.dropna(subset=['有效锰mg/kg', 'pH'])
|
|
|
+ getImg(imgDataMe['有效锰mg/kg'], imgDataMe['pH'], mkdir_path, '有效锰与pH相关性分析图',
|
|
|
+ '土壤金属指标数据',
|
|
|
+ '有效锰mg/kg', 'pH', imgDataMe['编号'], mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx',
|
|
|
+ 'W1')
|
|
|
+ # 铜与ph相关性
|
|
|
+ imgDataCu = nowTable_js.dropna(subset=['有效铜mg/kg', 'pH'])
|
|
|
+ getImg(imgDataCu['有效铜mg/kg'], imgDataCu['pH'], mkdir_path, '有效铜与pH相关性分析图',
|
|
|
+ '土壤金属指标数据',
|
|
|
+ '有效铜mg/kg', 'pH', imgDataCu['编号'], mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx',
|
|
|
+ 'Z1')
|
|
|
+ # 锌与ph相关性
|
|
|
+ imgDataZn = nowTable_js.dropna(subset=['有效锌mg/kg', 'pH'])
|
|
|
+ getImg(imgDataZn['有效锌mg/kg'], imgDataZn['pH'], mkdir_path, '有效锌与pH相关性分析图',
|
|
|
+ '土壤金属指标数据',
|
|
|
+ '有效锌mg/kg', 'pH', imgDataZn['编号'], mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx',
|
|
|
+ 'AC1')
|
|
|
+ # 钼与ph相关性
|
|
|
+ imgDataMu = nowTable_js.dropna(subset=['有效钼mg/kg', 'pH'])
|
|
|
+ getImg(imgDataMu['有效钼mg/kg'], imgDataMu['pH'], mkdir_path, '有效钼与pH相关性分析图',
|
|
|
+ '土壤金属指标数据',
|
|
|
+ '有效钼mg/kg', 'pH', imgDataMu['编号'], mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx',
|
|
|
+ 'AF1')
|
|
|
+ getStatisticsImg(nowTable_js['有效硅mg/kg'], '有效硅mg/kg', '有效硅',
|
|
|
+ mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx', mkdir_path, 'B19')
|
|
|
+ getStatisticsImg(nowTable_js['有效铁mg/kg'], '有效铁mg/kg', '有效铁',
|
|
|
+ mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx', mkdir_path, 'C19')
|
|
|
+ getStatisticsImg(nowTable_js['有效锰mg/kg'], '有效锰mg/kg', '有效锰',
|
|
|
+ mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx', mkdir_path, 'D19')
|
|
|
+ getStatisticsImg(nowTable_js['有效铜mg/kg'], '有效铜mg/kg', '有效铜',
|
|
|
+ mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx', mkdir_path, 'E19')
|
|
|
+ getStatisticsImg(nowTable_js['有效锌mg/kg'], '有效锌mg/kg', '有效锌',
|
|
|
+ mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx', mkdir_path, 'F19')
|
|
|
+ getStatisticsImg(nowTable_js['有效硼mg/kg'], '有效硼mg/kg', '有效硼',
|
|
|
+ mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx', mkdir_path, 'G19')
|
|
|
+ getStatisticsImg(nowTable_js['有效钼mg/kg'], '有效钼mg/kg', '有效钼',
|
|
|
+ mkdir_path + '/土壤金属指标数据-' + nowTime + '.xlsx', mkdir_path, 'H19')
|
|
|
+ # 表13 表14
|
|
|
+ with pd.ExcelWriter(mkdir_path + '/土壤污染风险值数据-' + nowTime +'.xlsx', engine='openpyxl') as writer:
|
|
|
+ resData_14_Style.to_excel(writer, index=False, sheet_name='土壤污染风险值数据')
|
|
|
+ resData_13.to_excel(writer, sheet_name='频度分析')
|
|
|
+ autoColumns(mkdir_path + '/土壤污染风险值数据-' + nowTime +'.xlsx')
|
|
|
+ nowTable_wr = pd.read_excel(mkdir_path + '/土壤污染风险值数据-' + nowTime + '.xlsx',
|
|
|
+ sheet_name='土壤污染风险值数据')
|
|
|
+ getStatisticsImg(nowTable_wr['镉mg/kg'], '镉mg/kg', '镉',
|
|
|
+ mkdir_path + '/土壤污染风险值数据-' + nowTime + '.xlsx', mkdir_path, 'B19')
|
|
|
+ getStatisticsImg(nowTable_wr['汞mg/kg'], '汞mg/kg', '汞',
|
|
|
+ mkdir_path + '/土壤污染风险值数据-' + nowTime + '.xlsx', mkdir_path, 'C19')
|
|
|
+ getStatisticsImg(nowTable_wr['砷mg/kg'], '砷mg/kg', '砷',
|
|
|
+ mkdir_path + '/土壤污染风险值数据-' + nowTime + '.xlsx', mkdir_path, 'D19')
|
|
|
+ getStatisticsImg(nowTable_wr['铅mg/kg'], '铅mg/kg', '铅',
|
|
|
+ mkdir_path + '/土壤污染风险值数据-' + nowTime + '.xlsx', mkdir_path, 'E19')
|
|
|
+ getStatisticsImg(nowTable_wr['铬mg/kg'], '铬mg/kg', '铬',
|
|
|
+ mkdir_path + '/土壤污染风险值数据-' + nowTime + '.xlsx', mkdir_path, 'F19')
|
|
|
+ getStatisticsImg(nowTable_wr['镍mg/kg'], '镍mg/kg', '镍',
|
|
|
+ mkdir_path + '/土壤污染风险值数据-' + nowTime + '.xlsx', mkdir_path, 'G19')
|
|
|
+ show_info('保存完成，点击确定开始生成审核报告。')
|
|
|
+ readData = pd.read_excel(changeFileUrl, sheet_name='Sheet1', converters={'原样品编号': str})
|
|
|
+ if checkType == 'HUNDRED_DATA':
|
|
|
+ readData = readData.head(100)
|
|
|
+ dealDataRes = dealData(readData)
|
|
|
+ # 生成审核报告
|
|
|
+ getReport(dealDataRes,changeFileUrl, saveFileUrl, table_1_data, table_3_data, table_5_data,table_8_data,table_10_data,table_12_data,table_14_data)
|
|
|
+ partReport.getphysicsReport(dealDataRes,'物理指标', changeFileUrl, saveFileUrl, table_1_data, table_3_data, table_5_data,table_8_data,table_10_data,table_12_data,table_14_data)
|
|
|
+ partReport.getConventionalNutrientIndicators(dealDataRes,'常规养分指标', changeFileUrl, saveFileUrl, table_1_data, table_3_data, table_5_data,table_8_data,table_10_data,table_12_data,table_14_data)
|
|
|
+ partReport.getChemicalIndicators(dealDataRes,'一般化学性指标', changeFileUrl, saveFileUrl, table_1_data, table_3_data, table_5_data,table_8_data,table_10_data,table_12_data,table_14_data)
|
|
|
+ partReport.getHeavyMetalIndicators(dealDataRes,'重金属指标', changeFileUrl, saveFileUrl, table_1_data, table_3_data, table_5_data,table_8_data,table_10_data,table_12_data,table_14_data)
|
|
|
+ show_info('审核报告已生成！')
|
|
|
+ except Exception as err:
|
|
|
+ print('err', err)
|
|
|
+ show_error('出错了！')
|
|
|
+
|
|
|
+
|
|
|
+def show_info(info):
|
|
|
+ Messagebox.show_info(title='提示：', message=info)
|
|
|
+def show_error(info):
|
|
|
+ Messagebox.show_error(title='错误：', message=info)
|
|
|
+
|
|
|
+def getPass(num):
|
|
|
+ # 获取设备id 获取相应的注册码
|
|
|
+ d = Querybox()
|
|
|
+ c = d.get_string(prompt=f'本程序免费试用100条数据，使用更多请持您的申请码({num})电话联系管理员(19556503305)获取注册码，审核时输入注册码可使用全部功能。', title='输入注册码',
|
|
|
+ initialvalue=None, parent=None)
|
|
|
+ if getNum() == c: # 输入的注册码正确
|
|
|
+ # 存储标记文件
|
|
|
+ with open('./html/code.txt', 'w') as file:
|
|
|
+ file.write(
|
|
|
+ '7687698709809hjkjlipomuiyoiupoimvgfghuli376d8bf8f8855ad8de997fa5dac1bd24956aef0cbfa0cf8ac04053a7043e3d90248051f6f03f02b20430949504a5556fb112131fc81205768229ffa023831b04')
|
|
|
+ Messagebox.show_info('注册码提交成功，点击开始审核按钮进行审核！')
|
|
|
+ else:
|
|
|
+ Messagebox.show_error('注册码不正确！')
|
|
|
+def main():
|
|
|
+ # 创建一个GUI窗口
|
|
|
+ root = ttk.Window() # 使用 ttkbootstrap 创建窗口对象
|
|
|
+
|
|
|
+ root.geometry('500x400')
|
|
|
+ root.title("审核软件")
|
|
|
+ root.resizable(False, False)
|
|
|
+ root.iconbitmap("./img/icon.ico")
|
|
|
+ windowX = root.winfo_screenwidth()
|
|
|
+ windowY = root.winfo_screenheight()
|
|
|
+ cen_x = (windowX - 600) / 2
|
|
|
+ cen_y = (windowY - 500) / 2
|
|
|
+ root.geometry('%dx%d+%d+%d' % (600, 500, cen_x, cen_y))
|
|
|
+ numStr = uuid.getnode()
|
|
|
+ # 添加个标签
|
|
|
+ label1 = ttk.Label(root, text="土壤表层数据指标审核软件",font=("Segoe UI", 14), bootstyle=INFO)
|
|
|
+ label1.grid(row=1, column=1, padx=10, pady=10)
|
|
|
+ b1 = ttk.Button(root, text="选择文件", bootstyle=(INFO, OUTLINE),width=50,command=open_file) # 使用 ttkbootstrap 的组件
|
|
|
+ b1.grid(row=2, column=1, padx=10, pady=10)
|
|
|
+ b2 = ttk.Button(root, text="开始审核", bootstyle= (INFO, OUTLINE),width=50, command=lambda: checkData(changeFileUrl)) # OUTLINE 是指定边框线
|
|
|
+ b2.grid(row=3, column=1, padx=10, pady=10)
|
|
|
+ b3 = ttk.Button(root, text="保 存", bootstyle= SUCCESS,width=50,command=saveFile) # OUTLINE 是指定边框线
|
|
|
+ b3.grid(row=4, column=1, padx=10, pady=10)
|
|
|
+ b4 = ttk.Button(root, text="获取申请码", bootstyle=SUCCESS, width=50, command=lambda: getPass(numStr)) # OUTLINE 是指定边框线
|
|
|
+ b4.grid(row=5, column=1, padx=10, pady=10)
|
|
|
+ # 插入logo图片
|
|
|
+
|
|
|
+ # image = ttk.PhotoImage(file="./img/logo_2.jpg")
|
|
|
+
|
|
|
+ img = Image.open("./img/logo_2.jpg")
|
|
|
+ new_img = img.resize((50,50))
|
|
|
+ # 将图像转为tkinter可用的PhotoImage格式
|
|
|
+ photo = ImageTk.PhotoImage(new_img)
|
|
|
+
|
|
|
+ # 显示图像
|
|
|
+ # label = ttk.Label(root, image=photo)
|
|
|
+
|
|
|
+ # 创建一个 Label 并添加图片
|
|
|
+ label = ttk.Label(image=photo,width=50)
|
|
|
+ label.grid(row=7, column=1, padx=10, pady=10)
|
|
|
+ # 写入单位名称
|
|
|
+ label2 = ttk.Label(root, text="©2024 合肥环研生态环境科技有限公司 版权所有", bootstyle=SUCCESS)
|
|
|
+ label2.grid(row=8, column=1, padx=10, pady=10)
|
|
|
+ root.grid_columnconfigure(1, weight=1)
|
|
|
+ root.grid_rowconfigure(1, weight=1)
|
|
|
+ root.mainloop()
|
|
|
+
|
|
|
+if __name__ == '__main__':
|
|
|
+ main()
|
|
|
+
|
张世豪
3 months ago
