partReport.py 107 KB

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  1. import pandas as pd
  2. import numpy as np
  3. import time
  4. import os
  5. from docx.shared import Inches
  6. from docx.shared import Pt, RGBColor
  7. from docx import Document
  8. from docx.enum.table import WD_TABLE_ALIGNMENT, WD_CELL_VERTICAL_ALIGNMENT
  9. from docx.oxml.ns import qn
  10. from docx.enum.text import WD_ALIGN_PARAGRAPH
  11. from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
  12. import report
  13. # 频度信息表生成
  14. def makeInfoTable(data, doc):
  15. rows = (int(len(data.columns) / 6) + 1)
  16. columnsList = np.arange(0, rows * 6, 6)
  17. dataList = []
  18. for i in columnsList:
  19. res = data.iloc[:, i:i + 6]
  20. res = res.reset_index()
  21. dataList.append(res)
  22. table_f_2 = doc.add_table(rows=rows * 6, cols=7, style='Light Shading Accent 1')
  23. for i, row in enumerate(table_f_2.rows):
  24. for j, cell in enumerate(row.cells):
  25. # 获取单元格中的段落对象
  26. paragraph = cell.paragraphs[0]
  27. if i == columnsList[0]:
  28. # 第一行 显示前6个指标的列名
  29. if len(dataList[0].columns) > j:
  30. r = paragraph.add_run(dataList[0].columns[j])
  31. r.font.bold = True
  32. r.font.size = Pt(10.5)
  33. r.font.name = 'Times New Roman'
  34. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  35. else:
  36. paragraph.add_run('')
  37. elif len(columnsList) > 1 and i > columnsList[0] and i < columnsList[1]:
  38. if len(dataList[0].columns) > j:
  39. r = paragraph.add_run(str(dataList[0].iloc[i - 1, j]))
  40. r.font.size = Pt(10.5)
  41. r.font.name = 'Times New Roman'
  42. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  43. else:
  44. paragraph.add_run('')
  45. elif i == columnsList[1]:
  46. # 第6行 显示前6个指 标的列名
  47. if len(dataList[1].columns) > j:
  48. r = paragraph.add_run(dataList[1].columns[j])
  49. r.font.bold = True
  50. r.font.size = Pt(10.5)
  51. r.font.name = 'Times New Roman'
  52. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  53. else:
  54. paragraph.add_run('')
  55. elif len(columnsList) > 2 and i > columnsList[1] and i < columnsList[2]:
  56. if len(dataList[1].columns) > j:
  57. r = paragraph.add_run(str(dataList[1].iloc[i - 7, j]))
  58. r.font.size = Pt(10.5)
  59. r.font.name = 'Times New Roman'
  60. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  61. else:
  62. paragraph.add_run('')
  63. elif i == columnsList[2]:
  64. # 第6*2行 显示前6个指 标的列名
  65. if len(dataList[2].columns) > j:
  66. r = paragraph.add_run(dataList[2].columns[j])
  67. r.font.bold = True
  68. r.font.size = Pt(10.5)
  69. r.font.name = 'Times New Roman'
  70. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  71. else:
  72. paragraph.add_run('')
  73. elif len(columnsList) > 3 and i > columnsList[2] and i < columnsList[3]:
  74. if len(dataList[2].columns) > j:
  75. r = paragraph.add_run(str(dataList[2].iloc[i - 13, j]))
  76. r.font.size = Pt(10.5)
  77. r.font.name = 'Times New Roman'
  78. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  79. else:
  80. paragraph.add_run('')
  81. elif i == columnsList[3]:
  82. # 第6*3行 显示前6个指 标的列名
  83. if len(dataList[3].columns) > j:
  84. r = paragraph.add_run(dataList[3].columns[j])
  85. r.font.bold = True
  86. r.font.size = Pt(10.5)
  87. r.font.name = 'Times New Roman'
  88. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  89. else:
  90. paragraph.add_run('')
  91. elif len(columnsList) > 4 and i > columnsList[3] and i < columnsList[4]:
  92. if len(dataList[3].columns) > j:
  93. r = paragraph.add_run(str(dataList[3].iloc[i - 19, j]))
  94. r.font.size = Pt(10.5)
  95. r.font.name = 'Times New Roman'
  96. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  97. else:
  98. paragraph.add_run('')
  99. elif i == columnsList[4]:
  100. # 第6*4行 显示前6个指 标的列名
  101. if len(dataList[4].columns) > j:
  102. r = paragraph.add_run(dataList[4].columns[j])
  103. r.font.bold = True
  104. r.font.size = Pt(10.5)
  105. r.font.name = 'Times New Roman'
  106. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  107. else:
  108. paragraph.add_run('')
  109. elif len(columnsList) > 5 and i > columnsList[4] and i < columnsList[5]:
  110. if len(dataList[4].columns) > j:
  111. r = paragraph.add_run(str(dataList[4].iloc[i - 25, j]))
  112. r.font.size = Pt(10.5)
  113. r.font.name = 'Times New Roman'
  114. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  115. else:
  116. paragraph.add_run('')
  117. elif i == columnsList[5]:
  118. # 第6*5行 显示前6个指 标的列名
  119. if len(dataList[5].columns) > j:
  120. r = paragraph.add_run(dataList[5].columns[j])
  121. r.font.bold = True
  122. r.font.size = Pt(10.5)
  123. r.font.name = 'Times New Roman'
  124. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  125. else:
  126. paragraph.add_run('')
  127. elif len(columnsList) > 6 and i > columnsList[5] and i < columnsList[6]:
  128. if len(dataList[5].columns) > j:
  129. r = paragraph.add_run(str(dataList[5].iloc[i - 31, j]))
  130. r.font.size = Pt(10.5)
  131. r.font.name = 'Times New Roman'
  132. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  133. else:
  134. paragraph.add_run('')
  135. elif i == columnsList[6]:
  136. # 第6*6行 显示前6个指 标的列名
  137. if len(dataList[6].columns) > j:
  138. r = paragraph.add_run(dataList[6].columns[j])
  139. r.font.bold = True
  140. r.font.size = Pt(10.5)
  141. r.font.name = 'Times New Roman'
  142. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  143. else:
  144. paragraph.add_run('')
  145. elif len(columnsList) > 7 and i > columnsList[6] and i < columnsList[7]:
  146. if len(dataList[6].columns) > j:
  147. r = paragraph.add_run(str(dataList[6].iloc[i - 37, j]))
  148. r.font.size = Pt(10.5)
  149. r.font.name = 'Times New Roman'
  150. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  151. else:
  152. paragraph.add_run('')
  153. elif i == columnsList[7]:
  154. # 第6*7行 显示前6个指 标的列名
  155. if len(dataList[7].columns) > j:
  156. r = paragraph.add_run(dataList[7].columns[j])
  157. r.font.bold = True
  158. r.font.size = Pt(10.5)
  159. r.font.name = 'Times New Roman'
  160. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  161. else:
  162. paragraph.add_run('')
  163. elif len(columnsList) >= 8 and i > columnsList[7] and i < columnsList[8]:
  164. if len(dataList[7].columns) > j:
  165. r = paragraph.add_run(str(dataList[7].iloc[i - 43, j]))
  166. r.font.size = Pt(10.5)
  167. r.font.name = 'Times New Roman'
  168. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  169. else:
  170. paragraph.add_run('')
  171. elif i == columnsList[8]:
  172. if len(dataList[8].columns) > j:
  173. # 第6*8行 显示前6个指 标的列名
  174. r = paragraph.add_run(dataList[8].columns[j])
  175. r.font.bold = True
  176. r.font.size = Pt(10.5)
  177. r.font.name = 'Times New Roman'
  178. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  179. else:
  180. paragraph.add_run('')
  181. elif len(columnsList) >= 9 and i > columnsList[8] and i < columnsList[9]:
  182. if len(dataList[8].columns) > j:
  183. r = paragraph.add_run(str(dataList[8].iloc[i - 49, j]))
  184. r.font.size = Pt(10.5)
  185. r.font.name = 'Times New Roman'
  186. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  187. else:
  188. paragraph.add_run('')
  189. elif i == columnsList[9]:
  190. # 第6*9行 显示前6个指 标的列名
  191. if len(dataList[9].columns) > j:
  192. r = paragraph.add_run(dataList[9].columns[j])
  193. r.font.bold = True
  194. r.font.size = Pt(10.5)
  195. r.font.name = 'Times New Roman'
  196. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  197. else:
  198. paragraph.add_run('')
  199. elif len(columnsList) >= 10 and i > columnsList[9] and i <= 60:
  200. if len(dataList[9].columns) > j:
  201. r = paragraph.add_run(str(dataList[9].iloc[i - 55, j]))
  202. r.font.size = Pt(10.5)
  203. r.font.name = 'Times New Roman'
  204. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  205. else:
  206. paragraph.add_run('')
  207. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  208. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  209. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  210. # 生成子报告: 物理指标 常规养分指标 一般化学性指标 重金属指标
  211. indexClassificationList = {
  212. '物理指标': ['pH', '土壤质地', '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)',
  213. '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', '水稳>5mm(%)', '水稳3mm~5mm(%)',
  214. '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(%)', '洗失量(吸管法需填)', '风干试样含水量(分析基)'],
  215. '常规养分指标': ['pH','有机质', '全氮', '全磷', '全钾', '有效磷', '速效钾', '有效硫', '有效硼', '有效铁', '有效锰', '有效铜', '有效锌', '有效钼', '有效硅', '缓效钾'],
  216. '一般化学性指标': ['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',
  217. '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',
  218. '水溶性SO₄²⁻含量', '离子总量', '碳酸钙'],
  219. '重金属指标': ['pH', '总汞', '总砷', '总铅', '总镉', '总铬', '总镍']
  220. }
  221. # 生成物理指标审核报告
  222. def getphysicsReport(originData,data,type, changeFileUrl, saveFileUrl, check_1_data,
  223. check_3_data,
  224. check_5_data ,
  225. check_8_data, # 样品编号替换为编号
  226. check_10_data,
  227. check_12_data,
  228. check_14_data ):
  229. """
  230. :param type: 指标类型
  231. :param changeFileUrl: 选择的数据文件路径
  232. :param saveFileUrl: 保存的文件路径
  233. :param check_1_data: 土壤容重数据
  234. :param check_3_data: 水稳审核数据
  235. :param check_5_data: 盐离子数据
  236. :param check_8_data: 水溶性离子数据
  237. :param check_10_data: 有机质及氮磷钾数据
  238. :param check_12_data: 有效养分数据
  239. :param check_14_data: 重金属数据
  240. :return:
  241. """
  242. # 生成报告
  243. name = os.path.basename(changeFileUrl)
  244. n = name.split('.')
  245. areaName = n[0].replace('数据', '')
  246. # 生成一个新的文件夹用于存放审核报告相关的数据
  247. nowTime = time.strftime("%Y-%m-%d %H时%M分%S秒", time.localtime())
  248. dir_name = f'{areaName}-{type}数据审核报告'
  249. mkdir_path = saveFileUrl + '/' + dir_name + nowTime
  250. if not os.path.exists(mkdir_path):
  251. os.mkdir(mkdir_path)
  252. # 获取相应指标数据
  253. physicsData = data[indexClassificationList[type]]
  254. physicsDataNum = originData[indexClassificationList[type]]
  255. report.getFrequencyImage(physicsData, mkdir_path)
  256. physicsData['序号'] = data['序号']
  257. physicsData['原样品编号'] = data['原样品编号']
  258. physicsData['样品编号'] = data['样品编号']
  259. physicsData['地理位置'] = data['地理位置']
  260. physicsData['母质'] = data['母质']
  261. physicsData['土壤类型'] = data['土壤类型']
  262. physicsData['土地利用类型'] = data['土地利用类型']
  263. physicsData['土壤质地'] = data['土壤质地']
  264. physicsDataNum['序号'] = originData['序号']
  265. physicsDataNum['原样品编号'] = originData['原样品编号']
  266. physicsDataNum['样品编号'] = originData['样品编号']
  267. physicsDataNum['地理位置'] = originData['地理位置']
  268. physicsDataNum['母质'] = originData['母质']
  269. physicsDataNum['土壤类型'] = originData['土壤类型']
  270. physicsDataNum['土地利用类型'] = originData['土地利用类型']
  271. physicsDataNum['土壤质地'] = originData['土壤质地']
  272. physicsDataNum['原样品编号'] = originData['原样品编号'].astype(str)
  273. # 生成相应审核报告
  274. # 根据选择的路径读取数据
  275. physicsData['原样品编号'] = physicsData['原样品编号'].astype(str)
  276. # todo 有数据后这里去掉注释
  277. # checkData = pd.read_excel(changeFileUrl, sheet_name='检测方法')
  278. # 上面这个地址,可以传递给函数中,用于保存表格和图片
  279. # 调用函数 开始生成报告相关内容
  280. # 表1相关数据
  281. typeData = report.getSimpleNum(physicsData)
  282. lenNum_1 = len(typeData['sData'])
  283. lenNum_1_f = len(typeData['allData'])
  284. table_1_data = pd.DataFrame({
  285. '类型': typeData['sData'].index,
  286. '数量': typeData['sData'],
  287. '合计': [typeData['sData'].sum() for _ in range(lenNum_1)]
  288. })
  289. # 表2数据
  290. table_2_data = report.getDataComplete(physicsData)
  291. table_2_data = table_2_data.reset_index()
  292. table_2_data.columns = ['指标名称', '实测数量', '应测数量']
  293. # 表3数据
  294. # table_3_data = report.checkMethod(checkData, mkdir_path)
  295. # 数据修约 表4
  296. report.getNum(physicsDataNum, mkdir_path)
  297. # 数据填报项审核 表5
  298. report.dataReportResult(physicsData, mkdir_path)
  299. # 表6数据 土壤质地类型不一致
  300. middData = physicsData[['原样品编号', '样品编号']].astype(str)
  301. middData['编号'] = middData['原样品编号']
  302. del middData['原样品编号']
  303. check_1_data = pd.merge(check_1_data, middData, how='left', on='编号')
  304. check_1_data = check_1_data.replace(np.nan, '')
  305. typeNotSame = check_1_data[check_1_data['土壤质地'] != check_1_data['土壤类型(判断)']]
  306. table_6_data = typeNotSame[['编号', '样品编号', '土壤质地', '土壤类型(判断)']]
  307. allNeedData = pd.DataFrame({})
  308. allNeedData['原样品编号'] = check_1_data['编号']
  309. getSimpleDataNumber = pd.merge(allNeedData, physicsData[['原样品编号', '样品编号']], how='left', on="原样品编号")
  310. allNeedData['样品编号'] = getSimpleDataNumber['样品编号']
  311. allNeedData['土地利用类型'] = check_1_data['土地利用类型']
  312. allNeedData['审核结果'] = check_1_data['审核结果'] + check_3_data['审核结果']
  313. allNeedData['外业'] = ['' for _ in range(len(check_1_data))]
  314. table_7_data = allNeedData[allNeedData['审核结果'] != '']
  315. del table_7_data['审核结果']
  316. # 写进表格
  317. with pd.ExcelWriter(f'{mkdir_path}/超阈值样品统计表.xlsx', engine='openpyxl') as writer:
  318. table_7_data.to_excel(writer, index=False, sheet_name='超阈值数据')
  319. # 表8数据
  320. table_8_data = report.getPHData(physicsData, mkdir_path)
  321. # 表13 所有存疑数据
  322. with pd.ExcelWriter(f'{mkdir_path}/数据审核过程存疑数据一览表.xlsx', engine='openpyxl') as writer:
  323. allNeedData[allNeedData['审核结果'] != ''].to_excel(writer, index=False, sheet_name='存疑数据')
  324. # 附表: 频度分析图
  325. # report.getFrequencyImage(physicsData, mkdir_path)
  326. table_f_2_data = report.getFrequencyInformation(data, mkdir_path)
  327. # 新建一个文档
  328. doc = Document()
  329. # 添加标题
  330. doc.add_heading(f"{areaName}第三次全国土壤普查数据审核报告", level=0)
  331. # 添加一级标题
  332. doc.add_heading('一、数据完整性审核', level=1)
  333. doc.add_heading('1、土地利用类型与检测指标符合性审核', level=2)
  334. # 插入表格1
  335. paragraph_1 = doc.add_paragraph()
  336. paragraph_1.add_run(f"表1:{areaName}三普样品数量统计表(表层)").bold = True
  337. # 设置居中
  338. paragraph_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
  339. table_1 = doc.add_table(rows=lenNum_1 + 1, cols=3, style='Light Shading Accent 1')
  340. table_1.alignment = WD_TABLE_ALIGNMENT.CENTER
  341. # 遍历表格 插入数据
  342. # 遍历表格的所有单元格,并填充内容
  343. for i, row in enumerate(table_1.rows):
  344. for j, cell in enumerate(row.cells):
  345. # 获取单元格中的段落对象
  346. paragraph = cell.paragraphs[0]
  347. if i == 0:
  348. r = paragraph.add_run(str(table_1_data.columns[j]))
  349. r.font.bold = True
  350. else:
  351. r = paragraph.add_run(str(table_1_data.iloc[i - 1, j]))
  352. r.font.size = Pt(10.5)
  353. r.font.name = 'Times New Roman'
  354. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  355. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  356. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  357. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  358. # 合并单元格 合并第3列的第二行和第三行
  359. if lenNum_1 > 1:
  360. table_1.cell(2, 2).text = ''
  361. table_1.cell(1, 2).merge(table_1.cell(2, 2))
  362. ############test##############
  363. doc.add_heading('2、指标名称与实际检测样品数量完整性审核', level=2)
  364. # 插入表格2
  365. paragraph_2 = doc.add_paragraph()
  366. 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
  367. table_2 = doc.add_table(rows=len(table_2_data) + 1, cols=3, style='Light Shading Accent 1')
  368. paragraph_2.alignment = WD_ALIGN_PARAGRAPH.CENTER
  369. table_2.alignment = WD_TABLE_ALIGNMENT.CENTER
  370. for i, row in enumerate(table_2.rows):
  371. for j, cell in enumerate(row.cells):
  372. # 获取单元格中的段落对象
  373. paragraph = cell.paragraphs[0]
  374. if i == 0:
  375. r = paragraph.add_run(str(table_2_data.columns[j]))
  376. r.font.bold = True
  377. else:
  378. r = paragraph.add_run(str(table_2_data.iloc[i - 1, j]))
  379. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  380. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  381. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  382. r.font.size = Pt(10.5)
  383. r.font.name = 'Times New Roman'
  384. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  385. doc.add_heading('二、数据规范性审核', level=1)
  386. doc.add_heading('1、数据填报规范性审核', level=2)
  387. # 插入表3
  388. paragraph_3 = doc.add_paragraph()
  389. 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
  390. # table_3 = doc.add_table(rows=2, cols=2)
  391. paragraph_3.alignment = WD_ALIGN_PARAGRAPH.CENTER
  392. # table_3.alignment = WD_TABLE_ALIGNMENT.CENTER
  393. # 写入数据 这里数据写不下 嵌入链接
  394. 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)
  395. doc.add_heading('2、数值修约规范性审核', level=2)
  396. # 插入表4
  397. paragraph_4 = doc.add_paragraph()
  398. paragraph_4.add_run(f'表4:{areaName}土壤检测数据数值修约结果表').bold = True
  399. # table_4 = doc.add_table(rows=2, cols=2)
  400. paragraph_4.alignment = WD_ALIGN_PARAGRAPH.CENTER
  401. # table_4.alignment = WD_TABLE_ALIGNMENT.CENTER
  402. 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)
  403. # 填入数据 这里数据也放不下 嵌入链接
  404. doc.add_heading('3、数据未检出的填报规范性审核', level=2)
  405. # 插入表5
  406. paragraph_5 = doc.add_paragraph()
  407. 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
  408. # table_5 = doc.add_table(rows=2, cols=2)
  409. paragraph_5.alignment = WD_ALIGN_PARAGRAPH.CENTER
  410. # table_5.alignment = WD_TABLE_ALIGNMENT.CENTER
  411. # 写入数据 这里数据也放不下 嵌入链接
  412. 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)
  413. doc.add_heading('4、土壤质地填报规范性审核', level=2)
  414. # 插入表6
  415. paragraph_6 = doc.add_paragraph()
  416. paragraph_6.add_run(f'表6:{areaName}土壤质地填报审核结果表').bold = True
  417. table_6 = doc.add_table(rows=len(table_6_data) + 1, cols=4, style='Light Shading Accent 1')
  418. paragraph_6.alignment = WD_ALIGN_PARAGRAPH.CENTER
  419. table_6.alignment = WD_TABLE_ALIGNMENT.CENTER
  420. # 提取结果表中数据
  421. # 写入数据 土壤质地类型不一致的数据提取出来
  422. for i, row in enumerate(table_6.rows):
  423. for j, cell in enumerate(row.cells):
  424. # 获取单元格中的段落对象
  425. paragraph = cell.paragraphs[0]
  426. if i == 0:
  427. r = paragraph.add_run(str(table_6_data.columns[j]))
  428. r.font.bold = True
  429. else:
  430. r = paragraph.add_run(str(table_6_data.iloc[i - 1, j]))
  431. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  432. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  433. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  434. r.font.size = Pt(10.5)
  435. r.font.name = 'Times New Roman'
  436. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  437. doc.add_heading('三、数据合理性审核', level=1)
  438. doc.add_heading('1、阈值法审核', level=2)
  439. # 插入表格
  440. paragraph_7 = doc.add_paragraph()
  441. paragraph_7.add_run(f'表7:{areaName}土壤检测数据超阈值样品统计表').bold = True
  442. # table_7 = doc.add_table(rows=2, cols=2)
  443. # paragraph_7.alignment = WD_ALIGN_PARAGRAPH.CENTER
  444. # table_7.alignment = WD_TABLE_ALIGNMENT.CENTER
  445. # 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
  446. 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:超阈值样品统计表.xlsx', level=4)
  447. # 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
  448. doc.add_heading('2、极值法审核', level=2)
  449. doc.add_heading('(1)pH', level=3)
  450. # 插入ph分布图
  451. if os.path.isfile(f'{mkdir_path}/PH值分布图.png'):
  452. doc.add_picture(f'{mkdir_path}/PH值分布图.png', width=Inches(6.0))
  453. paragraph_t_1 = doc.add_paragraph()
  454. paragraph_t_1.add_run(f'图1:pH值分布情况').bold = True
  455. paragraph_t_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
  456. # 插入频度统计表
  457. paragraph_8 = doc.add_paragraph()
  458. paragraph_8.add_run('表8:pH数据统计表').bold = True
  459. table_8 = doc.add_table(rows=6, cols=2, style='Light Shading Accent 1')
  460. t_8 = table_8_data['频度分析']
  461. t_8 = t_8.reset_index()
  462. t_8.columns = ['指标', '数据']
  463. paragraph_8.alignment = WD_ALIGN_PARAGRAPH.CENTER
  464. table_8.alignment = WD_TABLE_ALIGNMENT.CENTER
  465. for i, row in enumerate(table_8.rows):
  466. for j, cell in enumerate(row.cells):
  467. # 获取单元格中的段落对象
  468. paragraph = cell.paragraphs[0]
  469. if i == 0:
  470. r = paragraph.add_run(str(t_8.columns[j]))
  471. r.font.bold = True
  472. else:
  473. r = paragraph.add_run(str(t_8.iloc[i - 1, j]))
  474. r.font.size = Pt(10.5)
  475. r.font.name = 'Times New Roman'
  476. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  477. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  478. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  479. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  480. # 插入异常数据提取表格 todo 这里数据多的话也可能写不下 最好是嵌入一下
  481. if not table_8_data['异常数据'].empty:
  482. paragraph_9 = doc.add_paragraph()
  483. paragraph_9.add_run('表9:pH异常数据统计表').bold = True
  484. table_9 = doc.add_table(rows=len(table_8_data['异常数据']) + 1, cols=6, style='Light Shading Accent 1')
  485. t_9 = table_8_data['异常数据']
  486. paragraph_9.alignment = WD_ALIGN_PARAGRAPH.CENTER
  487. table_9.alignment = WD_TABLE_ALIGNMENT.CENTER
  488. for i, row in enumerate(table_9.rows):
  489. for j, cell in enumerate(row.cells):
  490. # 获取单元格中的段落对象
  491. paragraph = cell.paragraphs[0]
  492. if i == 0:
  493. r = paragraph.add_run(str(t_9.columns[j]))
  494. r.font.bold = True
  495. else:
  496. r = paragraph.add_run(str(t_9.iloc[i - 1, j]))
  497. r.font.size = Pt(10.5)
  498. r.font.name = 'Times New Roman'
  499. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  500. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  501. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  502. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  503. doc.add_heading('四、审核存疑数据', level=1)
  504. paragraph_12 = doc.add_paragraph()
  505. paragraph_12.add_run(f'表10:数据审核过程存疑数据一览表').bold = True
  506. paragraph_12.alignment = WD_ALIGN_PARAGRAPH.CENTER
  507. 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:物理指标数据审核过程存疑数据一览表.xlsx', level=4)
  508. doc.add_heading('五、附表', level=1)
  509. doc.add_heading('附表1:某区三普样品数量统计表(表层)', level=2)
  510. # 插入附表1
  511. table_1_f = doc.add_table(rows=lenNum_1 + 1, cols=3, style='Light Shading Accent 1')
  512. table_1_f.alignment = WD_TABLE_ALIGNMENT.CENTER
  513. # 遍历表格 插入数据
  514. # 遍历表格的所有单元格,并填充内容
  515. for i, row in enumerate(table_1_f.rows):
  516. for j, cell in enumerate(row.cells):
  517. # 获取单元格中的段落对象
  518. paragraph = cell.paragraphs[0]
  519. if i == 0:
  520. r = paragraph.add_run(str(table_1_data.columns[j]))
  521. r.font.bold = True
  522. else:
  523. r = paragraph.add_run(str(table_1_data.iloc[i - 1, j]))
  524. r.font.size = Pt(10.5)
  525. r.font.name = 'Times New Roman'
  526. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  527. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  528. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  529. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  530. # 合并单元格 合并第3列的第二行和第三行
  531. if lenNum_1 > 1:
  532. table_1_f.cell(2, 2).text = ''
  533. table_1_f.cell(1, 2).merge(table_1_f.cell(2, 2))
  534. doc.add_heading('附表2:各指标频度分析表', level=2)
  535. # 插入表格 写入数据
  536. table_f_2_data = table_f_2_data.replace(np.nan, '')
  537. makeInfoTable(table_f_2_data, doc)
  538. # table_f_2 = doc.add_table(rows=len(table_f_2_data) + 1, cols=6, style='Light Shading Accent 1')
  539. # for i, row in enumerate(table_f_2.rows):
  540. # for j, cell in enumerate(row.cells):
  541. # # 获取单元格中的段落对象
  542. # paragraph = cell.paragraphs[0]
  543. # if i == 0:
  544. # r = paragraph.add_run(str(table_f_2_data.columns[j]))
  545. # r.font.bold = True
  546. # else:
  547. # r = paragraph.add_run(str(table_f_2_data.iloc[i - 1, j]))
  548. # r.font.size = Pt(10.5)
  549. # r.font.name = 'Times New Roman'
  550. # r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  551. # paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  552. # paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  553. # paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  554. # 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)
  555. doc.add_heading('附表3:各指标频度分析图', level=2)
  556. # 插入频度信息的图形
  557. if os.path.isfile(f'{mkdir_path}/0.002mm以下颗粒含量分析图.png'):
  558. doc.add_picture(f'{mkdir_path}/0.002mm以下颗粒含量分析图.png', width=Inches(6.0))
  559. if os.path.isfile(f'{mkdir_path}/0.02~0.002mm颗粒含量分析图.png.png'):
  560. doc.add_picture(f'{mkdir_path}/0.02~0.002mm颗粒含量分析图.png', width=Inches(6.0))
  561. if os.path.isfile(f'{mkdir_path}/0.2~0.02mm颗粒含量分析图.png'):
  562. doc.add_picture(f'{mkdir_path}/0.2~0.02mm颗粒含量分析图.png', width=Inches(6.0))
  563. if os.path.isfile(f'{mkdir_path}/2~0.2mm颗粒含量分析图.png'):
  564. doc.add_picture(f'{mkdir_path}/2~0.2mm颗粒含量分析图.png', width=Inches(6.0))
  565. if os.path.isfile(f'{mkdir_path}/pH分析图.png'):
  566. doc.add_picture(f'{mkdir_path}/pH分析图.png', width=Inches(6.0))
  567. if os.path.isfile(f'{mkdir_path}/风干试样含水量(分析基)分析图.png'):
  568. doc.add_picture(f'{mkdir_path}/风干试样含水量(分析基)分析图.png', width=Inches(6.0))
  569. if os.path.isfile(f'{mkdir_path}/洗失量(吸管法需填)分析图.png'):
  570. doc.add_picture(f'{mkdir_path}/洗失量(吸管法需填)分析图.png', width=Inches(6.0))
  571. if os.path.isfile(f'{mkdir_path}/土壤容重1分析图.png'):
  572. doc.add_picture(f'{mkdir_path}/土壤容重1分析图.png', width=Inches(6.0))
  573. if os.path.isfile(f'{mkdir_path}/土壤容重2分析图.png'):
  574. doc.add_picture(f'{mkdir_path}/土壤容重2分析图.png', width=Inches(6.0))
  575. if os.path.isfile(f'{mkdir_path}/土壤容重3分析图.png'):
  576. doc.add_picture(f'{mkdir_path}/土壤容重3分析图.png', width=Inches(6.0))
  577. if os.path.isfile(f'{mkdir_path}/土壤容重4分析图.png'):
  578. doc.add_picture(f'{mkdir_path}/土壤容重4分析图.png', width=Inches(6.0))
  579. if os.path.isfile(f'{mkdir_path}/土壤容重平均值分析图.png'):
  580. doc.add_picture(f'{mkdir_path}/土壤容重平均值分析图.png', width=Inches(6.0))
  581. if os.path.isfile(f'{mkdir_path}/水稳0.5mm~1mm分析图.png'):
  582. doc.add_picture(f'{mkdir_path}/水稳0.5mm~1mm分析图.png', width=Inches(6.0))
  583. if os.path.isfile(f'{mkdir_path}/水稳0.25mm~0.5mm分析图.png'):
  584. doc.add_picture(f'{mkdir_path}/水稳0.25mm~0.5mm分析图.png', width=Inches(6.0))
  585. if os.path.isfile(f'{mkdir_path}/水稳1mm~2mm分析图.png'):
  586. doc.add_picture(f'{mkdir_path}/水稳1mm~2mm分析图.png', width=Inches(6.0))
  587. if os.path.isfile(f'{mkdir_path}/水稳2mm~3mm分析图.png'):
  588. doc.add_picture(f'{mkdir_path}/水稳2mm~3mm分析图.png', width=Inches(6.0))
  589. if os.path.isfile(f'{mkdir_path}/水稳3mm~5mm分析图.png'):
  590. doc.add_picture(f'{mkdir_path}/水稳3mm~5mm分析图.png', width=Inches(6.0))
  591. if os.path.isfile(f'{mkdir_path}/水稳5mm分析图.png'):
  592. doc.add_picture(f'{mkdir_path}/水稳5mm分析图.png', width=Inches(6.0))
  593. doc.add_heading('附表4:数值修约标准', level=2)
  594. # 读取数据 插入表格 写入数据
  595. numData = pd.read_excel('./img/数值修约要求.xlsx', sheet_name='Sheet1')
  596. table_2_f = doc.add_table(rows=len(numData) + 1, cols=2, style='Light Shading Accent 1')
  597. table_2_f.alignment = WD_TABLE_ALIGNMENT.CENTER
  598. for i, row in enumerate(table_2_f.rows):
  599. for j, cell in enumerate(row.cells):
  600. # 获取单元格中的段落对象
  601. paragraph = cell.paragraphs[0]
  602. if i == 0:
  603. r = paragraph.add_run(str(numData.columns[j]))
  604. r.font.bold = True
  605. else:
  606. r = paragraph.add_run(str(numData.iloc[i - 1, j]))
  607. r.font.size = Pt(10.5)
  608. r.font.name = 'Times New Roman'
  609. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  610. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  611. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  612. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  613. # 处理样式 遍历所有的段落 修改字体
  614. # 遍历并打印每个段落的文本
  615. paragraphs = doc.paragraphs
  616. for paragraph in paragraphs:
  617. for run in paragraph.runs:
  618. run.font.color.rgb = RGBColor(0, 0, 0)
  619. run.font.name = 'Times New Roman'
  620. run.font.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  621. # 保存Word文档
  622. doc.save(f'{mkdir_path}/{areaName}-{type}审核报告.docx')
  623. # 生成常规养分指标审核报告
  624. def getConventionalNutrientIndicators(originData,data,type, changeFileUrl, saveFileUrl, check_1_data,
  625. check_3_data,
  626. check_5_data,
  627. check_8_data, # 样品编号替换为编号
  628. check_10_data,
  629. check_12_data,
  630. check_14_data ):
  631. # 生成报告
  632. name = os.path.basename(changeFileUrl)
  633. n = name.split('.')
  634. areaName = n[0].replace('数据', '')
  635. # 生成一个新的文件夹用于存放审核报告相关的数据
  636. nowTime = time.strftime("%Y-%m-%d %H时%M分%S秒", time.localtime())
  637. dir_name = f'{areaName}-{type}数据审核报告'
  638. mkdir_path = saveFileUrl + '/' + dir_name + nowTime
  639. if not os.path.exists(mkdir_path):
  640. os.mkdir(mkdir_path)
  641. # 根据选择的路径读取数据
  642. ConventionalNutrientData = data[indexClassificationList[type]]
  643. ConventionalNutrientDataNum = originData[indexClassificationList[type]]
  644. report.getFrequencyImage(ConventionalNutrientData, mkdir_path)
  645. ConventionalNutrientData['序号'] = data['序号']
  646. ConventionalNutrientData['原样品编号'] = data['原样品编号']
  647. ConventionalNutrientData['样品编号'] = data['样品编号']
  648. ConventionalNutrientData['地理位置'] = data['地理位置']
  649. ConventionalNutrientData['母质'] = data['母质']
  650. ConventionalNutrientData['土壤类型'] = data['土壤类型']
  651. ConventionalNutrientData['土地利用类型'] = data['土地利用类型']
  652. # ConventionalNutrientData['土壤质地'] = data['土壤质地']
  653. ConventionalNutrientDataNum['序号'] = originData['序号']
  654. ConventionalNutrientDataNum['原样品编号'] = originData['原样品编号']
  655. ConventionalNutrientDataNum['样品编号'] = originData['样品编号']
  656. ConventionalNutrientDataNum['地理位置'] = originData['地理位置']
  657. ConventionalNutrientDataNum['母质'] = originData['母质']
  658. ConventionalNutrientDataNum['土壤类型'] = originData['土壤类型']
  659. ConventionalNutrientDataNum['土地利用类型'] = originData['土地利用类型']
  660. # 生成相应审核报告
  661. ConventionalNutrientData['原样品编号'] = ConventionalNutrientData['原样品编号'].astype(str)
  662. ConventionalNutrientDataNum['原样品编号'] = ConventionalNutrientDataNum['原样品编号'].astype(str)
  663. data['原样品编号'] = data['原样品编号'].astype(str)
  664. # checkData = pd.read_excel(changeFileUrl, sheet_name='检测方法')
  665. # 上面这个地址,可以纯递给函数中,用于保存表格和图片
  666. # 调用函数 开始生成报告相关内容
  667. # 表1相关数据
  668. typeData = report.getSimpleNum(ConventionalNutrientData)
  669. lenNum_1 = len(typeData['sData'])
  670. lenNum_1_f = len(typeData['allData'])
  671. table_1_data = pd.DataFrame({
  672. '类型': typeData['sData'].index,
  673. '数量': typeData['sData'],
  674. '合计': [typeData['sData'].sum() for _ in range(lenNum_1)]
  675. })
  676. # 表2数据
  677. table_2_data = report.getDataComplete(ConventionalNutrientData)
  678. table_2_data = table_2_data.reset_index()
  679. table_2_data.columns = ['指标名称', '实测数量', '应测数量']
  680. # 表3数据
  681. # table_3_data = report.checkMethod(checkData, mkdir_path)
  682. # 数据修约 表4
  683. report.getNum(ConventionalNutrientDataNum, mkdir_path)
  684. # 数据填报项审核 表5
  685. report.dataReportResult(ConventionalNutrientData, mkdir_path)
  686. # 表6数据 土壤质地类型不一致
  687. middData = data[['原样品编号', '样品编号']].astype(str)
  688. middData['编号'] = middData['原样品编号']
  689. del middData['原样品编号']
  690. check_1_data = pd.merge(check_1_data, middData, how='left', on='编号')
  691. check_1_data = check_1_data.replace(np.nan, '')
  692. # typeNotSame = check_1_data[check_1_data['土壤质地'] != check_1_data['土壤类型(判断)']]
  693. # table_6_data = typeNotSame[['编号', '样品编号', '土壤质地', '土壤类型(判断)']]
  694. allNeedData = pd.DataFrame({})
  695. allNeedData['原样品编号'] = check_1_data['编号']
  696. getSimpleDataNumber = pd.merge(allNeedData, ConventionalNutrientData[['原样品编号', '样品编号']], how='left', on="原样品编号")
  697. allNeedData['样品编号'] = getSimpleDataNumber['样品编号']
  698. allNeedData['土地利用类型'] = check_1_data['土地利用类型']
  699. allNeedData['审核结果'] = check_10_data['审核结果'] + check_12_data['审核结果']
  700. allNeedData['外业'] = ['' for _ in range(len(check_1_data))]
  701. table_7_data = allNeedData[allNeedData['审核结果'] != '']
  702. del table_7_data['审核结果']
  703. # 写进表格
  704. with pd.ExcelWriter(f'{mkdir_path}/超阈值样品统计表.xlsx', engine='openpyxl') as writer:
  705. table_7_data.to_excel(writer, index=False, sheet_name='超阈值数据')
  706. # 表8数据
  707. table_8_data = report.getPHData(ConventionalNutrientData, mkdir_path)
  708. # 表10 数据
  709. table_10_data = report.getNAndC(ConventionalNutrientData, mkdir_path)
  710. # 表11 数据:全磷和有效磷异常数据统计
  711. table_11_data = report.getPData(ConventionalNutrientData, mkdir_path)
  712. report.getKData(ConventionalNutrientData, mkdir_path)
  713. # 表13 所有存疑数据
  714. with pd.ExcelWriter(f'{mkdir_path}/数据审核过程存疑数据一览表.xlsx', engine='openpyxl') as writer:
  715. allNeedData[allNeedData['审核结果'] != ''].to_excel(writer, index=False, sheet_name='存疑数据')
  716. # 附表: 频度分析图
  717. # report.getFrequencyImage(ConventionalNutrientData, mkdir_path)
  718. table_f_2_data = report.getFrequencyInformation(data, mkdir_path)
  719. # 新建一个文档
  720. doc = Document()
  721. # 添加标题
  722. doc.add_heading(f"{areaName}第三次全国土壤普查数据审核报告", level=0)
  723. # 添加一级标题
  724. doc.add_heading('一、数据完整性审核', level=1)
  725. doc.add_heading('1、土地利用类型与检测指标符合性审核', level=2)
  726. # 插入表格1
  727. paragraph_1 = doc.add_paragraph()
  728. paragraph_1.add_run(f"表1:{areaName}三普样品数量统计表(表层)").bold = True
  729. # 设置居中
  730. paragraph_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
  731. table_1 = doc.add_table(rows=lenNum_1 + 1, cols=3, style='Light Shading Accent 1')
  732. table_1.alignment = WD_TABLE_ALIGNMENT.CENTER
  733. # 遍历表格 插入数据
  734. # 遍历表格的所有单元格,并填充内容
  735. for i, row in enumerate(table_1.rows):
  736. for j, cell in enumerate(row.cells):
  737. # 获取单元格中的段落对象
  738. paragraph = cell.paragraphs[0]
  739. if i == 0:
  740. r = paragraph.add_run(str(table_1_data.columns[j]))
  741. r.font.bold = True
  742. else:
  743. r = paragraph.add_run(str(table_1_data.iloc[i - 1, j]))
  744. r.font.size = Pt(10.5)
  745. r.font.name = 'Times New Roman'
  746. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  747. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  748. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  749. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  750. # 合并单元格 合并第3列的第二行和第三行
  751. if lenNum_1 > 1:
  752. table_1.cell(2, 2).text = ''
  753. table_1.cell(1, 2).merge(table_1.cell(2, 2))
  754. ############test##############
  755. doc.add_heading('2、指标名称与实际检测样品数量完整性审核', level=2)
  756. # 插入表格2
  757. paragraph_2 = doc.add_paragraph()
  758. 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
  759. table_2 = doc.add_table(rows=len(table_2_data) + 1, cols=3, style='Light Shading Accent 1')
  760. paragraph_2.alignment = WD_ALIGN_PARAGRAPH.CENTER
  761. table_2.alignment = WD_TABLE_ALIGNMENT.CENTER
  762. for i, row in enumerate(table_2.rows):
  763. for j, cell in enumerate(row.cells):
  764. # 获取单元格中的段落对象
  765. paragraph = cell.paragraphs[0]
  766. if i == 0:
  767. r = paragraph.add_run(str(table_2_data.columns[j]))
  768. r.font.bold = True
  769. else:
  770. r = paragraph.add_run(str(table_2_data.iloc[i - 1, j]))
  771. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  772. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  773. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  774. r.font.size = Pt(10.5)
  775. r.font.name = 'Times New Roman'
  776. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  777. doc.add_heading('二、数据规范性审核', level=1)
  778. doc.add_heading('1、数据填报规范性审核', level=2)
  779. # 插入表3
  780. paragraph_3 = doc.add_paragraph()
  781. 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
  782. # table_3 = doc.add_table(rows=2, cols=2)
  783. paragraph_3.alignment = WD_ALIGN_PARAGRAPH.CENTER
  784. # table_3.alignment = WD_TABLE_ALIGNMENT.CENTER
  785. # 写入数据 这里数据写不下 嵌入链接
  786. 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)
  787. doc.add_heading('2、数值修约规范性审核', level=2)
  788. # 插入表4
  789. paragraph_4 = doc.add_paragraph()
  790. paragraph_4.add_run(f'表4:{areaName}土壤检测数据数值修约结果表').bold = True
  791. # table_4 = doc.add_table(rows=2, cols=2)
  792. paragraph_4.alignment = WD_ALIGN_PARAGRAPH.CENTER
  793. # table_4.alignment = WD_TABLE_ALIGNMENT.CENTER
  794. 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)
  795. # 填入数据 这里数据也放不下 嵌入链接
  796. doc.add_heading('3、数据未检出的填报规范性审核', level=2)
  797. # 插入表5
  798. paragraph_5 = doc.add_paragraph()
  799. 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
  800. # table_5 = doc.add_table(rows=2, cols=2)
  801. paragraph_5.alignment = WD_ALIGN_PARAGRAPH.CENTER
  802. # table_5.alignment = WD_TABLE_ALIGNMENT.CENTER
  803. # 写入数据 这里数据也放不下 嵌入链接
  804. 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)
  805. # doc.add_heading('4、土壤质地填报规范性审核', level=2)
  806. # # 插入表6
  807. # paragraph_6 = doc.add_paragraph()
  808. # paragraph_6.add_run(f'表6:{areaName}土壤质地填报审核结果表').bold = True
  809. # table_6 = doc.add_table(rows=len(table_6_data) + 1, cols=4, style='Light Shading Accent 1')
  810. # paragraph_6.alignment = WD_ALIGN_PARAGRAPH.CENTER
  811. # table_6.alignment = WD_TABLE_ALIGNMENT.CENTER
  812. # # 提取结果表中数据
  813. # # 写入数据 土壤质地类型不一致的数据提取出来
  814. # for i, row in enumerate(table_6.rows):
  815. # for j, cell in enumerate(row.cells):
  816. # # 获取单元格中的段落对象
  817. # paragraph = cell.paragraphs[0]
  818. # if i == 0:
  819. # r = paragraph.add_run(str(table_6_data.columns[j]))
  820. # r.font.bold = True
  821. # else:
  822. # r = paragraph.add_run(str(table_6_data.iloc[i - 1, j]))
  823. # paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  824. # paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  825. # paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  826. # r.font.size = Pt(10.5)
  827. # r.font.name = 'Times New Roman'
  828. # r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  829. doc.add_heading('三、数据合理性审核', level=1)
  830. doc.add_heading('1、阈值法审核', level=2)
  831. # 插入表格
  832. paragraph_7 = doc.add_paragraph()
  833. paragraph_7.add_run(f'表6:{areaName}土壤检测数据超阈值样品统计表').bold = True
  834. # table_7 = doc.add_table(rows=2, cols=2)
  835. # paragraph_7.alignment = WD_ALIGN_PARAGRAPH.CENTER
  836. # table_7.alignment = WD_TABLE_ALIGNMENT.CENTER
  837. # 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
  838. 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)
  839. # 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
  840. doc.add_heading('2、极值法审核', level=2)
  841. doc.add_heading('(1)pH', level=3)
  842. # 插入ph分布图
  843. if os.path.isfile(f'{mkdir_path}/PH值分布图.png'):
  844. doc.add_picture(f'{mkdir_path}/PH值分布图.png', width=Inches(6.0))
  845. paragraph_t_1 = doc.add_paragraph()
  846. paragraph_t_1.add_run(f'图1:pH值分布情况').bold = True
  847. paragraph_t_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
  848. # 插入频度统计表
  849. paragraph_8 = doc.add_paragraph()
  850. paragraph_8.add_run('表7:pH数据统计表').bold = True
  851. table_8 = doc.add_table(rows=6, cols=2, style='Light Shading Accent 1')
  852. t_8 = table_8_data['频度分析']
  853. t_8 = t_8.reset_index()
  854. t_8.columns = ['指标', '数据']
  855. paragraph_8.alignment = WD_ALIGN_PARAGRAPH.CENTER
  856. table_8.alignment = WD_TABLE_ALIGNMENT.CENTER
  857. for i, row in enumerate(table_8.rows):
  858. for j, cell in enumerate(row.cells):
  859. # 获取单元格中的段落对象
  860. paragraph = cell.paragraphs[0]
  861. if i == 0:
  862. r = paragraph.add_run(str(t_8.columns[j]))
  863. r.font.bold = True
  864. else:
  865. r = paragraph.add_run(str(t_8.iloc[i - 1, j]))
  866. r.font.size = Pt(10.5)
  867. r.font.name = 'Times New Roman'
  868. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  869. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  870. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  871. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  872. # 插入异常数据提取表格 todo 这里数据多的话也可能写不下 最好是嵌入一下
  873. if not table_8_data['异常数据'].empty:
  874. paragraph_9 = doc.add_paragraph()
  875. paragraph_9.add_run('表8:pH异常数据统计表').bold = True
  876. table_9 = doc.add_table(rows=len(table_8_data['异常数据']) + 1, cols=6, style='Light Shading Accent 1')
  877. t_9 = table_8_data['异常数据']
  878. paragraph_9.alignment = WD_ALIGN_PARAGRAPH.CENTER
  879. table_9.alignment = WD_TABLE_ALIGNMENT.CENTER
  880. for i, row in enumerate(table_9.rows):
  881. for j, cell in enumerate(row.cells):
  882. # 获取单元格中的段落对象
  883. paragraph = cell.paragraphs[0]
  884. if i == 0:
  885. r = paragraph.add_run(str(t_9.columns[j]))
  886. r.font.bold = True
  887. else:
  888. r = paragraph.add_run(str(t_9.iloc[i - 1, j]))
  889. r.font.size = Pt(10.5)
  890. r.font.name = 'Times New Roman'
  891. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  892. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  893. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  894. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  895. doc.add_heading('3、关联分析法审核', level=2)
  896. if os.path.isfile(f'{mkdir_path}/有机质与全氮相关性分析图.png'):
  897. doc.add_picture(f'{mkdir_path}/有机质与全氮相关性分析图.png', width=Inches(6.0))
  898. paragraph_t_2 = doc.add_paragraph()
  899. paragraph_t_2.add_run(f'图2:有机质与全氮相关关系').bold = True
  900. paragraph_t_2.alignment = WD_ALIGN_PARAGRAPH.CENTER
  901. # 插入碳氮比异常数据
  902. if not table_10_data.empty:
  903. paragraph_10 = doc.add_paragraph()
  904. paragraph_10.add_run('表9:碳氮比异常数据统计表').bold = True
  905. table_10 = doc.add_table(rows=len(table_10_data) + 1, cols=8, style='Light Shading Accent 1')
  906. paragraph_10.alignment = WD_ALIGN_PARAGRAPH.CENTER
  907. table_10.alignment = WD_TABLE_ALIGNMENT.CENTER
  908. for i, row in enumerate(table_10.rows):
  909. for j, cell in enumerate(row.cells):
  910. # 获取单元格中的段落对象
  911. paragraph = cell.paragraphs[0]
  912. if i == 0:
  913. r = paragraph.add_run(str(table_10_data.columns[j]))
  914. r.font.bold = True
  915. else:
  916. r = paragraph.add_run(str(table_10_data.iloc[i - 1, j]))
  917. r.font.size = Pt(10.5)
  918. r.font.name = 'Times New Roman'
  919. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  920. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  921. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  922. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  923. doc.add_heading('4、指标综合分析', level=2)
  924. # 插入图片
  925. if os.path.isfile(f'{mkdir_path}/全磷分布图.png'):
  926. doc.add_picture(f'{mkdir_path}/全磷分布图.png', width=Inches(6.0))
  927. paragraph_t_3 = doc.add_paragraph()
  928. paragraph_t_3.add_run(f'图3:全磷分布图').bold = True
  929. paragraph_t_3.alignment = WD_ALIGN_PARAGRAPH.CENTER
  930. if os.path.isfile(f'{mkdir_path}/有效磷分布图.png'):
  931. doc.add_picture(f'{mkdir_path}/有效磷分布图.png', width=Inches(6.0))
  932. paragraph_t_4 = doc.add_paragraph()
  933. paragraph_t_4.add_run(f'图4:有效磷分布图').bold = True
  934. paragraph_t_4.alignment = WD_ALIGN_PARAGRAPH.CENTER
  935. # 插入图片
  936. if os.path.isfile(f'{mkdir_path}/有效磷占全磷比分布图.png'):
  937. doc.add_picture(f'{mkdir_path}/有效磷占全磷比分布图.png', width=Inches(6.0))
  938. paragraph_t_5 = doc.add_paragraph()
  939. paragraph_t_5.add_run(f'图5:有效磷含量占全磷含量比例').bold = True
  940. paragraph_t_5.alignment = WD_ALIGN_PARAGRAPH.CENTER
  941. # 插入表格
  942. if not table_11_data.empty:
  943. paragraph_11 = doc.add_paragraph()
  944. paragraph_11.add_run('表10:全磷与有效磷异常样品统计表').bold = True
  945. table_11 = doc.add_table(rows=len(table_11_data) + 1, cols=7, style='Light Shading Accent 1')
  946. paragraph_11.alignment = WD_ALIGN_PARAGRAPH.CENTER
  947. table_11.alignment = WD_TABLE_ALIGNMENT.CENTER
  948. for i, row in enumerate(table_11.rows):
  949. for j, cell in enumerate(row.cells):
  950. # 获取单元格中的段落对象
  951. paragraph = cell.paragraphs[0]
  952. if i == 0:
  953. r = paragraph.add_run(str(table_11_data.columns[j]))
  954. r.font.bold = True
  955. else:
  956. r = paragraph.add_run(str(table_11_data.iloc[i - 1, j]))
  957. r.font.size = Pt(10.5)
  958. r.font.name = 'Times New Roman'
  959. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  960. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  961. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  962. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  963. else:
  964. paragraph_11 = doc.add_paragraph()
  965. paragraph_11.add_run('表10:全磷与有效磷异常样品统计表').bold = True
  966. paragraph_11_info = doc.add_paragraph()
  967. paragraph_11_info.add_run('无异常数据')
  968. paragraph_11.alignment = WD_ALIGN_PARAGRAPH.CENTER
  969. paragraph_11_info.alignment = WD_ALIGN_PARAGRAPH.CENTER
  970. # 全钾、速效钾、缓效钾
  971. if os.path.isfile(f'{mkdir_path}/全钾与速效钾缓效钾之和关系统计图.png'):
  972. 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))
  973. paragraph_t_6 = doc.add_paragraph()
  974. 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
  975. paragraph_t_6.alignment = WD_ALIGN_PARAGRAPH.CENTER
  976. if os.path.isfile(f'{mkdir_path}/速效钾与缓效钾关系统计图.png'):
  977. doc.add_picture(f'{mkdir_path}/速效钾与缓效钾关系统计图.png', width=Inches(6.0))
  978. paragraph_t_7 = doc.add_paragraph()
  979. paragraph_t_7.add_run(f'图7:速效钾与缓效钾关系统计图').bold = True
  980. paragraph_t_7.alignment = WD_ALIGN_PARAGRAPH.CENTER
  981. doc.add_heading('四、审核存疑数据', level=1)
  982. paragraph_12 = doc.add_paragraph()
  983. paragraph_12.add_run(f'表11:数据审核过程存疑数据一览表').bold = True
  984. paragraph_12.alignment = WD_ALIGN_PARAGRAPH.CENTER
  985. 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)
  986. doc.add_heading('五、附表', level=1)
  987. doc.add_heading('附表1:某区三普样品数量统计表(表层)', level=2)
  988. # 插入附表1
  989. table_1_f = doc.add_table(rows=lenNum_1 + 1, cols=3, style='Light Shading Accent 1')
  990. table_1_f.alignment = WD_TABLE_ALIGNMENT.CENTER
  991. # 遍历表格 插入数据
  992. # 遍历表格的所有单元格,并填充内容
  993. for i, row in enumerate(table_1_f.rows):
  994. for j, cell in enumerate(row.cells):
  995. # 获取单元格中的段落对象
  996. paragraph = cell.paragraphs[0]
  997. if i == 0:
  998. r = paragraph.add_run(str(table_1_data.columns[j]))
  999. r.font.bold = True
  1000. else:
  1001. r = paragraph.add_run(str(table_1_data.iloc[i - 1, j]))
  1002. r.font.size = Pt(10.5)
  1003. r.font.name = 'Times New Roman'
  1004. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1005. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1006. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1007. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1008. # 合并单元格 合并第3列的第二行和第三行
  1009. if lenNum_1 > 1:
  1010. table_1_f.cell(2, 2).text = ''
  1011. table_1_f.cell(1, 2).merge(table_1_f.cell(2, 2))
  1012. doc.add_heading('附表2:各指标频度分析表', level=2)
  1013. # 插入表格 写入数据
  1014. table_f_2_data = table_f_2_data.replace(np.nan, '')
  1015. makeInfoTable(table_f_2_data, doc)
  1016. # table_f_2 = doc.add_table(rows=len(table_f_2_data) + 1, cols=6, style='Light Shading Accent 1')
  1017. # for i, row in enumerate(table_f_2.rows):
  1018. # for j, cell in enumerate(row.cells):
  1019. # # 获取单元格中的段落对象
  1020. # paragraph = cell.paragraphs[0]
  1021. # if i == 0:
  1022. # r = paragraph.add_run(str(table_f_2_data.columns[j]))
  1023. # r.font.bold = True
  1024. # else:
  1025. # r = paragraph.add_run(str(table_f_2_data.iloc[i - 1, j]))
  1026. # r.font.size = Pt(10.5)
  1027. # r.font.name = 'Times New Roman'
  1028. # r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1029. # paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1030. # paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1031. # paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1032. # 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)
  1033. doc.add_heading('附表3:各指标频度分析图', level=2)
  1034. # 插入频度信息的图形
  1035. if os.path.isfile(f'{mkdir_path}/pH分析图.png'):
  1036. doc.add_picture(f'{mkdir_path}/pH分析图.png', width=Inches(6.0))
  1037. if os.path.isfile(f'{mkdir_path}/缓效钾分析图.png'):
  1038. doc.add_picture(f'{mkdir_path}/缓效钾分析图.png', width=Inches(6.0))
  1039. if os.path.isfile(f'{mkdir_path}/全氮分析图.png'):
  1040. doc.add_picture(f'{mkdir_path}/全氮分析图.png', width=Inches(6.0))
  1041. if os.path.isfile(f'{mkdir_path}/全钾分析图.png'):
  1042. doc.add_picture(f'{mkdir_path}/全钾分析图.png', width=Inches(6.0))
  1043. if os.path.isfile(f'{mkdir_path}/全磷分析图.png'):
  1044. doc.add_picture(f'{mkdir_path}/全磷分析图.png', width=Inches(6.0))
  1045. if os.path.isfile(f'{mkdir_path}/速效钾分析图.png'):
  1046. doc.add_picture(f'{mkdir_path}/速效钾分析图.png', width=Inches(6.0))
  1047. if os.path.isfile(f'{mkdir_path}/有机质分析图.png'):
  1048. doc.add_picture(f'{mkdir_path}/有机质分析图.png', width=Inches(6.0))
  1049. if os.path.isfile(f'{mkdir_path}/有效硅分析图.png'):
  1050. doc.add_picture(f'{mkdir_path}/有效硅分析图.png', width=Inches(6.0))
  1051. if os.path.isfile(f'{mkdir_path}/有效磷分析图.png'):
  1052. doc.add_picture(f'{mkdir_path}/有效磷分析图.png', width=Inches(6.0))
  1053. if os.path.isfile(f'{mkdir_path}/有效硫分析图.png'):
  1054. doc.add_picture(f'{mkdir_path}/有效硫分析图.png', width=Inches(6.0))
  1055. if os.path.isfile(f'{mkdir_path}/有效锰分析图.png'):
  1056. doc.add_picture(f'{mkdir_path}/有效锰分析图.png', width=Inches(6.0))
  1057. if os.path.isfile(f'{mkdir_path}/有效钼分析图.png'):
  1058. doc.add_picture(f'{mkdir_path}/有效钼分析图.png', width=Inches(6.0))
  1059. if os.path.isfile(f'{mkdir_path}/有效硼分析图.png'):
  1060. doc.add_picture(f'{mkdir_path}/有效硼分析图.png', width=Inches(6.0))
  1061. if os.path.isfile(f'{mkdir_path}/有效铁分析图.png'):
  1062. doc.add_picture(f'{mkdir_path}/有效铁分析图.png', width=Inches(6.0))
  1063. if os.path.isfile(f'{mkdir_path}/有效铜分析图.png'):
  1064. doc.add_picture(f'{mkdir_path}/有效铜分析图.png', width=Inches(6.0))
  1065. if os.path.isfile(f'{mkdir_path}/有效锌分析图.png'):
  1066. doc.add_picture(f'{mkdir_path}/有效锌分析图.png', width=Inches(6.0))
  1067. doc.add_heading('附表4:数值修约标准', level=2)
  1068. # 读取数据 插入表格 写入数据
  1069. numData = pd.read_excel('./img/数值修约要求.xlsx', sheet_name='Sheet1')
  1070. table_2_f = doc.add_table(rows=len(numData) + 1, cols=2, style='Light Shading Accent 1')
  1071. table_2_f.alignment = WD_TABLE_ALIGNMENT.CENTER
  1072. for i, row in enumerate(table_2_f.rows):
  1073. for j, cell in enumerate(row.cells):
  1074. # 获取单元格中的段落对象
  1075. paragraph = cell.paragraphs[0]
  1076. if i == 0:
  1077. r = paragraph.add_run(str(numData.columns[j]))
  1078. r.font.bold = True
  1079. else:
  1080. r = paragraph.add_run(str(numData.iloc[i - 1, j]))
  1081. r.font.size = Pt(10.5)
  1082. r.font.name = 'Times New Roman'
  1083. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1084. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1085. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1086. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1087. # 处理样式 遍历所有的段落 修改字体
  1088. # 遍历并打印每个段落的文本
  1089. paragraphs = doc.paragraphs
  1090. for paragraph in paragraphs:
  1091. for run in paragraph.runs:
  1092. run.font.color.rgb = RGBColor(0, 0, 0)
  1093. run.font.name = 'Times New Roman'
  1094. run.font.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1095. # 保存Word文档
  1096. doc.save(f'{mkdir_path}/{areaName}-{type}审核报告.docx')
  1097. # 生成一般化学性指标审核报告
  1098. def getChemicalIndicators(originData,data,type, changeFileUrl, saveFileUrl, check_1_data,
  1099. check_3_data,
  1100. check_5_data,
  1101. check_8_data, # 样品编号替换为编号
  1102. check_10_data,
  1103. check_12_data,
  1104. check_14_data):
  1105. # 生成报告
  1106. name = os.path.basename(changeFileUrl)
  1107. n = name.split('.')
  1108. areaName = n[0].replace('数据', '')
  1109. # 生成一个新的文件夹用于存放审核报告相关的数据
  1110. nowTime = time.strftime("%Y-%m-%d %H时%M分%S秒", time.localtime())
  1111. dir_name = f'{areaName}-{type}数据审核报告'
  1112. mkdir_path = saveFileUrl + '/' + dir_name + nowTime
  1113. if not os.path.exists(mkdir_path):
  1114. os.mkdir(mkdir_path)
  1115. # 根据选择的路径读取数据
  1116. cheemicalData = data[indexClassificationList[type]]
  1117. cheemicalDataNum = originData[indexClassificationList[type]]
  1118. report.getFrequencyImage(cheemicalData, mkdir_path)
  1119. cheemicalData['序号'] = data['序号']
  1120. cheemicalData['原样品编号'] = data['原样品编号']
  1121. cheemicalData['样品编号'] = data['样品编号']
  1122. cheemicalData['地理位置'] = data['地理位置']
  1123. cheemicalData['母质'] = data['母质']
  1124. cheemicalData['土壤类型'] = data['土壤类型']
  1125. cheemicalData['土地利用类型'] = data['土地利用类型']
  1126. # cheemicalData['土壤质地'] = data['土壤质地']
  1127. cheemicalData['原样品编号'] = cheemicalData['原样品编号'].astype(str)
  1128. # checkData = pd.read_excel(changeFileUrl, sheet_name='检测方法')
  1129. cheemicalDataNum['序号'] = originData['序号']
  1130. cheemicalDataNum['原样品编号'] = originData['原样品编号']
  1131. cheemicalDataNum['样品编号'] = originData['样品编号']
  1132. cheemicalDataNum['地理位置'] = originData['地理位置']
  1133. cheemicalDataNum['母质'] = originData['母质']
  1134. cheemicalDataNum['土壤类型'] = originData['土壤类型']
  1135. cheemicalDataNum['土地利用类型'] = originData['土地利用类型']
  1136. # cheemicalData['土壤质地'] = data['土壤质地']
  1137. cheemicalDataNum['原样品编号'] = cheemicalDataNum['原样品编号'].astype(str)
  1138. # 上面这个地址,可以纯递给函数中,用于保存表格和图片
  1139. # 调用函数 开始生成报告相关内容
  1140. # 表1相关数据
  1141. typeData = report.getSimpleNum(cheemicalData)
  1142. lenNum_1 = len(typeData['sData'])
  1143. lenNum_1_f = len(typeData['allData'])
  1144. table_1_data = pd.DataFrame({
  1145. '类型': typeData['sData'].index,
  1146. '数量': typeData['sData'],
  1147. '合计': [typeData['sData'].sum() for _ in range(lenNum_1)]
  1148. })
  1149. # 表2数据
  1150. table_2_data = report.getDataComplete(cheemicalData)
  1151. table_2_data = table_2_data.reset_index()
  1152. table_2_data.columns = ['指标名称', '实测数量', '应测数量']
  1153. # 表3数据
  1154. # table_3_data = report.checkMethod(checkData, mkdir_path)
  1155. # 数据修约 表4
  1156. report.getNum(cheemicalDataNum, mkdir_path)
  1157. # 数据填报项审核 表5
  1158. report.dataReportResult(cheemicalData, mkdir_path)
  1159. # 表6数据 土壤质地类型不一致
  1160. middData = data[['原样品编号', '样品编号']].astype(str)
  1161. middData['编号'] = middData['原样品编号']
  1162. del middData['原样品编号']
  1163. check_1_data = pd.merge(check_1_data, middData, how='left', on='编号')
  1164. check_1_data = check_1_data.replace(np.nan, '')
  1165. # typeNotSame = check_1_data[check_1_data['土壤质地'] != check_1_data['土壤类型(判断)']]
  1166. # table_6_data = typeNotSame[['编号', '样品编号', '土壤质地', '土壤类型(判断)']]
  1167. allNeedData = pd.DataFrame({})
  1168. allNeedData['原样品编号'] = check_1_data['编号']
  1169. getSimpleDataNumber = pd.merge(allNeedData, data[['原样品编号', '样品编号']], how='left', on="原样品编号")
  1170. allNeedData['样品编号'] = getSimpleDataNumber['样品编号']
  1171. allNeedData['土地利用类型'] = check_1_data['土地利用类型']
  1172. allNeedData['审核结果'] = check_5_data['审核结果'] + check_8_data['审核结果']
  1173. allNeedData['外业'] = ['' for _ in range(len(check_1_data))]
  1174. table_7_data = allNeedData[allNeedData['审核结果'] != '']
  1175. del table_7_data['审核结果']
  1176. # 写进表格
  1177. with pd.ExcelWriter(f'{mkdir_path}/超阈值样品统计表.xlsx', engine='openpyxl') as writer:
  1178. table_7_data.to_excel(writer, index=False, sheet_name='超阈值数据')
  1179. # 表8数据
  1180. table_8_data = report.getPHData(cheemicalData, mkdir_path)
  1181. report.cationExchangeCapacity(cheemicalData, mkdir_path)
  1182. report.changeCation(cheemicalData, mkdir_path)
  1183. report.manyTypes(cheemicalData, mkdir_path)
  1184. # 表13 所有存疑数据
  1185. with pd.ExcelWriter(f'{mkdir_path}/数据审核过程存疑数据一览表.xlsx', engine='openpyxl') as writer:
  1186. allNeedData[allNeedData['审核结果'] != ''].to_excel(writer, index=False, sheet_name='存疑数据')
  1187. # 附表: 频度分析图
  1188. # report.getFrequencyImage(cheemicalData, mkdir_path)
  1189. table_f_2_data = report.getFrequencyInformation(data, mkdir_path)
  1190. # 新建一个文档
  1191. doc = Document()
  1192. # 添加标题
  1193. doc.add_heading(f"{areaName}第三次全国土壤普查数据审核报告", level=0)
  1194. # 添加一级标题
  1195. doc.add_heading('一、数据完整性审核', level=1)
  1196. doc.add_heading('1、土地利用类型与检测指标符合性审核', level=2)
  1197. # 插入表格1
  1198. paragraph_1 = doc.add_paragraph()
  1199. paragraph_1.add_run(f"表1:{areaName}三普样品数量统计表(表层)").bold = True
  1200. # 设置居中
  1201. paragraph_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1202. table_1 = doc.add_table(rows=lenNum_1 + 1, cols=3, style='Light Shading Accent 1')
  1203. table_1.alignment = WD_TABLE_ALIGNMENT.CENTER
  1204. # 遍历表格 插入数据
  1205. # 遍历表格的所有单元格,并填充内容
  1206. for i, row in enumerate(table_1.rows):
  1207. for j, cell in enumerate(row.cells):
  1208. # 获取单元格中的段落对象
  1209. paragraph = cell.paragraphs[0]
  1210. if i == 0:
  1211. r = paragraph.add_run(str(table_1_data.columns[j]))
  1212. r.font.bold = True
  1213. else:
  1214. r = paragraph.add_run(str(table_1_data.iloc[i - 1, j]))
  1215. r.font.size = Pt(10.5)
  1216. r.font.name = 'Times New Roman'
  1217. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1218. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1219. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1220. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1221. # 合并单元格 合并第3列的第二行和第三行
  1222. if lenNum_1 > 1:
  1223. table_1.cell(2, 2).text = ''
  1224. table_1.cell(1, 2).merge(table_1.cell(2, 2))
  1225. ############test##############
  1226. doc.add_heading('2、指标名称与实际检测样品数量完整性审核', level=2)
  1227. # 插入表格2
  1228. paragraph_2 = doc.add_paragraph()
  1229. 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
  1230. table_2 = doc.add_table(rows=len(table_2_data) + 1, cols=3, style='Light Shading Accent 1')
  1231. paragraph_2.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1232. table_2.alignment = WD_TABLE_ALIGNMENT.CENTER
  1233. for i, row in enumerate(table_2.rows):
  1234. for j, cell in enumerate(row.cells):
  1235. # 获取单元格中的段落对象
  1236. paragraph = cell.paragraphs[0]
  1237. if i == 0:
  1238. r = paragraph.add_run(str(table_2_data.columns[j]))
  1239. r.font.bold = True
  1240. else:
  1241. r = paragraph.add_run(str(table_2_data.iloc[i - 1, j]))
  1242. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1243. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1244. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1245. r.font.size = Pt(10.5)
  1246. r.font.name = 'Times New Roman'
  1247. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1248. doc.add_heading('二、数据规范性审核', level=1)
  1249. doc.add_heading('1、数据填报规范性审核', level=2)
  1250. # 插入表3
  1251. paragraph_3 = doc.add_paragraph()
  1252. 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
  1253. # table_3 = doc.add_table(rows=2, cols=2)
  1254. paragraph_3.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1255. # table_3.alignment = WD_TABLE_ALIGNMENT.CENTER
  1256. # 写入数据 这里数据写不下 嵌入链接
  1257. 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)
  1258. doc.add_heading('2、数值修约规范性审核', level=2)
  1259. # 插入表4
  1260. paragraph_4 = doc.add_paragraph()
  1261. paragraph_4.add_run(f'表4:{areaName}土壤检测数据数值修约结果表').bold = True
  1262. # table_4 = doc.add_table(rows=2, cols=2)
  1263. paragraph_4.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1264. # table_4.alignment = WD_TABLE_ALIGNMENT.CENTER
  1265. 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)
  1266. # 填入数据 这里数据也放不下 嵌入链接
  1267. doc.add_heading('3、数据未检出的填报规范性审核', level=2)
  1268. # 插入表5
  1269. paragraph_5 = doc.add_paragraph()
  1270. 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
  1271. # table_5 = doc.add_table(rows=2, cols=2)
  1272. paragraph_5.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1273. # table_5.alignment = WD_TABLE_ALIGNMENT.CENTER
  1274. # 写入数据 这里数据也放不下 嵌入链接
  1275. 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)
  1276. # doc.add_heading('4、土壤质地填报规范性审核', level=2)
  1277. # # 插入表6
  1278. # paragraph_6 = doc.add_paragraph()
  1279. # paragraph_6.add_run(f'表6:{areaName}土壤质地填报审核结果表').bold = True
  1280. # table_6 = doc.add_table(rows=len(table_6_data) + 1, cols=4, style='Light Shading Accent 1')
  1281. # paragraph_6.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1282. # table_6.alignment = WD_TABLE_ALIGNMENT.CENTER
  1283. # # 提取结果表中数据
  1284. # # 写入数据 土壤质地类型不一致的数据提取出来
  1285. # for i, row in enumerate(table_6.rows):
  1286. # for j, cell in enumerate(row.cells):
  1287. # # 获取单元格中的段落对象
  1288. # paragraph = cell.paragraphs[0]
  1289. # if i == 0:
  1290. # r = paragraph.add_run(str(table_6_data.columns[j]))
  1291. # r.font.bold = True
  1292. # else:
  1293. # r = paragraph.add_run(str(table_6_data.iloc[i - 1, j]))
  1294. # paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1295. # paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1296. # paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1297. # r.font.size = Pt(10.5)
  1298. # r.font.name = 'Times New Roman'
  1299. # r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1300. doc.add_heading('三、数据合理性审核', level=1)
  1301. doc.add_heading('1、阈值法审核', level=2)
  1302. # 插入表格
  1303. paragraph_7 = doc.add_paragraph()
  1304. paragraph_7.add_run(f'表6:{areaName}土壤检测数据超阈值样品统计表').bold = True
  1305. # table_7 = doc.add_table(rows=2, cols=2)
  1306. # paragraph_7.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1307. # table_7.alignment = WD_TABLE_ALIGNMENT.CENTER
  1308. # 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
  1309. 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)
  1310. # 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
  1311. doc.add_heading('2、极值法审核', level=2)
  1312. doc.add_heading('(1)pH', level=3)
  1313. # 插入ph分布图
  1314. if os.path.isfile(f'{mkdir_path}/pH值分布图.png'):
  1315. doc.add_picture(f'{mkdir_path}/pH值分布图.png', width=Inches(6.0))
  1316. paragraph_t_1 = doc.add_paragraph()
  1317. paragraph_t_1.add_run(f'图1:pH值分布情况').bold = True
  1318. paragraph_t_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1319. # 插入频度统计表
  1320. paragraph_8 = doc.add_paragraph()
  1321. paragraph_8.add_run('表7:pH数据统计表').bold = True
  1322. table_8 = doc.add_table(rows=6, cols=2, style='Light Shading Accent 1')
  1323. t_8 = table_8_data['频度分析']
  1324. t_8 = t_8.reset_index()
  1325. t_8.columns = ['指标', '数据']
  1326. paragraph_8.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1327. table_8.alignment = WD_TABLE_ALIGNMENT.CENTER
  1328. for i, row in enumerate(table_8.rows):
  1329. for j, cell in enumerate(row.cells):
  1330. # 获取单元格中的段落对象
  1331. paragraph = cell.paragraphs[0]
  1332. if i == 0:
  1333. r = paragraph.add_run(str(t_8.columns[j]))
  1334. r.font.bold = True
  1335. else:
  1336. r = paragraph.add_run(str(t_8.iloc[i - 1, j]))
  1337. r.font.size = Pt(10.5)
  1338. r.font.name = 'Times New Roman'
  1339. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1340. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1341. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1342. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1343. # 插入异常数据提取表格 todo 这里数据多的话也可能写不下 最好是嵌入一下
  1344. if not table_8_data['异常数据'].empty:
  1345. paragraph_9 = doc.add_paragraph()
  1346. paragraph_9.add_run('表8:pH异常数据统计表').bold = True
  1347. table_9 = doc.add_table(rows=len(table_8_data['异常数据']) + 1, cols=6, style='Light Shading Accent 1')
  1348. t_9 = table_8_data['异常数据']
  1349. paragraph_9.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1350. table_9.alignment = WD_TABLE_ALIGNMENT.CENTER
  1351. for i, row in enumerate(table_9.rows):
  1352. for j, cell in enumerate(row.cells):
  1353. # 获取单元格中的段落对象
  1354. paragraph = cell.paragraphs[0]
  1355. if i == 0:
  1356. r = paragraph.add_run(str(t_9.columns[j]))
  1357. r.font.bold = True
  1358. else:
  1359. r = paragraph.add_run(str(t_9.iloc[i - 1, j]))
  1360. r.font.size = Pt(10.5)
  1361. r.font.name = 'Times New Roman'
  1362. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1363. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1364. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1365. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1366. doc.add_heading('3、指标综合分析', level=2)
  1367. # 阳离子交换量与交换性盐总量关系
  1368. 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'):
  1369. 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))
  1370. paragraph_t_8 = doc.add_paragraph()
  1371. 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
  1372. paragraph_t_8.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1373. # 交换性盐总量与交换性盐相关关系
  1374. 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'):
  1375. 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))
  1376. paragraph_t_9 = doc.add_paragraph()
  1377. 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
  1378. paragraph_t_9.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1379. 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'):
  1380. 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))
  1381. paragraph_t_10 = doc.add_paragraph()
  1382. 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
  1383. paragraph_t_10.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1384. # 水溶性盐、电导率、离子总量
  1385. if os.path.isfile(f'{mkdir_path}/全盐量分布图.png'):
  1386. doc.add_picture(f'{mkdir_path}/全盐量分布图.png', width=Inches(6.0))
  1387. paragraph_t_11 = doc.add_paragraph()
  1388. paragraph_t_11.add_run(f'图11:全盐量分布图').bold = True
  1389. paragraph_t_11.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1390. if os.path.isfile(f'{mkdir_path}/全盐量与电导率相关性分析图.png'):
  1391. doc.add_picture(f'{mkdir_path}/全盐量与电导率相关性分析图.png', width=Inches(6.0))
  1392. paragraph_t_12 = doc.add_paragraph()
  1393. paragraph_t_12.add_run(f'图12:全盐量与电导率相关性分析图').bold = True
  1394. paragraph_t_12.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1395. if os.path.isfile(f'{mkdir_path}/离子总量与水溶性盐总量关系图.png'):
  1396. 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))
  1397. paragraph_t_13 = doc.add_paragraph()
  1398. 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
  1399. paragraph_t_13.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1400. doc.add_heading('四、审核存疑数据', level=1)
  1401. paragraph_12 = doc.add_paragraph()
  1402. paragraph_12.add_run(f'表9:数据审核过程存疑数据一览表').bold = True
  1403. paragraph_12.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1404. 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)
  1405. doc.add_heading('五、附表', level=1)
  1406. doc.add_heading('附表1:某区三普样品数量统计表(表层)', level=2)
  1407. # 插入附表1
  1408. table_1_f = doc.add_table(rows=lenNum_1 + 1, cols=3, style='Light Shading Accent 1')
  1409. table_1_f.alignment = WD_TABLE_ALIGNMENT.CENTER
  1410. # 遍历表格 插入数据
  1411. # 遍历表格的所有单元格,并填充内容
  1412. for i, row in enumerate(table_1_f.rows):
  1413. for j, cell in enumerate(row.cells):
  1414. # 获取单元格中的段落对象
  1415. paragraph = cell.paragraphs[0]
  1416. if i == 0:
  1417. r = paragraph.add_run(str(table_1_data.columns[j]))
  1418. r.font.bold = True
  1419. else:
  1420. r = paragraph.add_run(str(table_1_data.iloc[i - 1, j]))
  1421. r.font.size = Pt(10.5)
  1422. r.font.name = 'Times New Roman'
  1423. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1424. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1425. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1426. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1427. # 合并单元格 合并第3列的第二行和第三行
  1428. if lenNum_1 > 1:
  1429. table_1_f.cell(2, 2).text = ''
  1430. table_1_f.cell(1, 2).merge(table_1_f.cell(2, 2))
  1431. doc.add_heading('附表2:各指标频度分析表', level=2)
  1432. # 插入表格 写入数据
  1433. table_f_2_data = table_f_2_data.replace(np.nan, '')
  1434. makeInfoTable(table_f_2_data, doc)
  1435. # table_f_2 = doc.add_table(rows=len(table_f_2_data) + 1, cols=6, style='Light Shading Accent 1')
  1436. # for i, row in enumerate(table_f_2.rows):
  1437. # for j, cell in enumerate(row.cells):
  1438. # # 获取单元格中的段落对象
  1439. # paragraph = cell.paragraphs[0]
  1440. # if i == 0:
  1441. # r = paragraph.add_run(str(table_f_2_data.columns[j]))
  1442. # r.font.bold = True
  1443. # else:
  1444. # r = paragraph.add_run(str(table_f_2_data.iloc[i - 1, j]))
  1445. # r.font.size = Pt(10.5)
  1446. # r.font.name = 'Times New Roman'
  1447. # r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1448. # paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1449. # paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1450. # paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1451. # 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)
  1452. doc.add_heading('附表3:各指标频度分析图', level=2)
  1453. # 插入频度信息的图形
  1454. if os.path.isfile(f'{mkdir_path}/pH分析图.png'):
  1455. doc.add_picture(f'{mkdir_path}/pH分析图.png', width=Inches(6.0))
  1456. if os.path.isfile(f'{mkdir_path}/电导率分析图.png'):
  1457. doc.add_picture(f'{mkdir_path}/电导率分析图.png', width=Inches(6.0))
  1458. if os.path.isfile(f'{mkdir_path}/交换性钙分析图.png'):
  1459. doc.add_picture(f'{mkdir_path}/交换性钙分析图.png', width=Inches(6.0))
  1460. if os.path.isfile(f'{mkdir_path}/交换性钾分析图.png'):
  1461. doc.add_picture(f'{mkdir_path}/交换性钾分析图.png', width=Inches(6.0))
  1462. if os.path.isfile(f'{mkdir_path}/交换性镁分析图.png'):
  1463. doc.add_picture(f'{mkdir_path}/交换性镁分析图.png', width=Inches(6.0))
  1464. if os.path.isfile(f'{mkdir_path}/交换性钠分析图.png'):
  1465. doc.add_picture(f'{mkdir_path}/交换性钠分析图.png', width=Inches(6.0))
  1466. if os.path.isfile(f'{mkdir_path}/交换性盐基总量分析图.png'):
  1467. doc.add_picture(f'{mkdir_path}/交换性盐基总量分析图.png', width=Inches(6.0))
  1468. if os.path.isfile(f'{mkdir_path}/全盐量分析图.png'):
  1469. doc.add_picture(f'{mkdir_path}/全盐量分析图.png', width=Inches(6.0))
  1470. if os.path.isfile(f'{mkdir_path}/阳离子交换量分析图.png'):
  1471. doc.add_picture(f'{mkdir_path}/阳离子交换量分析图.png', width=Inches(6.0))
  1472. doc.add_heading('附表4:数值修约标准', level=2)
  1473. # 读取数据 插入表格 写入数据
  1474. numData = pd.read_excel('./img/数值修约要求.xlsx', sheet_name='Sheet1')
  1475. table_2_f = doc.add_table(rows=len(numData) + 1, cols=2, style='Light Shading Accent 1')
  1476. table_2_f.alignment = WD_TABLE_ALIGNMENT.CENTER
  1477. for i, row in enumerate(table_2_f.rows):
  1478. for j, cell in enumerate(row.cells):
  1479. # 获取单元格中的段落对象
  1480. paragraph = cell.paragraphs[0]
  1481. if i == 0:
  1482. r = paragraph.add_run(str(numData.columns[j]))
  1483. r.font.bold = True
  1484. else:
  1485. r = paragraph.add_run(str(numData.iloc[i - 1, j]))
  1486. r.font.size = Pt(10.5)
  1487. r.font.name = 'Times New Roman'
  1488. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1489. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1490. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1491. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1492. # 处理样式 遍历所有的段落 修改字体
  1493. # 遍历并打印每个段落的文本
  1494. paragraphs = doc.paragraphs
  1495. for paragraph in paragraphs:
  1496. for run in paragraph.runs:
  1497. run.font.color.rgb = RGBColor(0, 0, 0)
  1498. run.font.name = 'Times New Roman'
  1499. run.font.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1500. # run.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1501. # 保存Word文档
  1502. doc.save(f'{mkdir_path}/{areaName}审核报告.docx')
  1503. # 生成重金属指标审核报告
  1504. def getHeavyMetalIndicators(originData, data, type, changeFileUrl, saveFileUrl, check_1_data,
  1505. check_3_data,
  1506. check_5_data,
  1507. check_8_data, # 样品编号替换为编号
  1508. check_10_data,
  1509. check_12_data,
  1510. check_14_data):
  1511. # 生成报告
  1512. name = os.path.basename(changeFileUrl)
  1513. n = name.split('.')
  1514. areaName = n[0].replace('数据', '')
  1515. # 生成一个新的文件夹用于存放审核报告相关的数据
  1516. nowTime = time.strftime("%Y-%m-%d %H时%M分%S秒", time.localtime())
  1517. dir_name = f'{areaName}-{type}数据审核报告'
  1518. mkdir_path = saveFileUrl + '/' + dir_name + nowTime
  1519. if not os.path.exists(mkdir_path):
  1520. os.mkdir(mkdir_path)
  1521. heavyMetaData = data[indexClassificationList[type]]
  1522. heavyMetaDataNum = originData[indexClassificationList[type]]
  1523. report.getFrequencyImage(heavyMetaData, mkdir_path)
  1524. heavyMetaData['序号'] = data['序号']
  1525. heavyMetaData['原样品编号'] = data['原样品编号']
  1526. heavyMetaData['样品编号'] = data['样品编号']
  1527. heavyMetaData['地理位置'] = data['地理位置']
  1528. heavyMetaData['母质'] = data['母质']
  1529. heavyMetaData['土壤类型'] = data['土壤类型']
  1530. heavyMetaData['土地利用类型'] = data['土地利用类型']
  1531. # heavyMetaData['土壤质地'] = data['土壤质地']
  1532. heavyMetaData['原样品编号'] = heavyMetaData['原样品编号'].astype(str)
  1533. # checkData = pd.read_excel(changeFileUrl, sheet_name='检测方法')
  1534. heavyMetaDataNum['序号'] = originData['序号']
  1535. heavyMetaDataNum['原样品编号'] = originData['原样品编号']
  1536. heavyMetaDataNum['样品编号'] = originData['样品编号']
  1537. heavyMetaDataNum['地理位置'] = originData['地理位置']
  1538. heavyMetaDataNum['母质'] = originData['母质']
  1539. heavyMetaDataNum['土壤类型'] = originData['土壤类型']
  1540. heavyMetaDataNum['土地利用类型'] = originData['土地利用类型']
  1541. # heavyMetaData['土壤质地'] = data['土壤质地']
  1542. heavyMetaDataNum['原样品编号'] = heavyMetaDataNum['原样品编号'].astype(str)
  1543. # 上面这个地址,可以纯递给函数中,用于保存表格和图片
  1544. # 调用函数 开始生成报告相关内容
  1545. # 表1相关数据
  1546. typeData = report.getSimpleNum(heavyMetaData)
  1547. lenNum_1 = len(typeData['sData'])
  1548. lenNum_1_f = len(typeData['allData'])
  1549. table_1_data = pd.DataFrame({
  1550. '类型': typeData['sData'].index,
  1551. '数量': typeData['sData'],
  1552. '合计': [typeData['sData'].sum() for _ in range(lenNum_1)]
  1553. })
  1554. # 表2数据
  1555. table_2_data = report.getDataComplete(heavyMetaData)
  1556. table_2_data = table_2_data.reset_index()
  1557. table_2_data.columns = ['指标名称', '实测数量', '应测数量']
  1558. # 表3数据
  1559. # table_3_data = report.checkMethod(checkData, mkdir_path)
  1560. # 数据修约 表4
  1561. report.getNum(heavyMetaDataNum, mkdir_path)
  1562. # 数据填报项审核 表5
  1563. report.dataReportResult(heavyMetaData, mkdir_path)
  1564. # 表6数据 土壤质地类型不一致
  1565. middData = heavyMetaData[['原样品编号', '样品编号']].astype(str)
  1566. middData['编号'] = middData['原样品编号']
  1567. del middData['原样品编号']
  1568. check_1_data = pd.merge(check_1_data, middData, how='left', on='编号')
  1569. check_1_data = check_1_data.replace(np.nan, '')
  1570. # typeNotSame = check_1_data[check_1_data['土壤质地'] != check_1_data['土壤类型(判断)']]
  1571. # table_6_data = typeNotSame[['编号', '样品编号', '土壤质地', '土壤类型(判断)']]
  1572. allNeedData = pd.DataFrame({})
  1573. allNeedData['原样品编号'] = check_1_data['编号']
  1574. getSimpleDataNumber = pd.merge(allNeedData, heavyMetaData[['原样品编号', '样品编号']], how='left', on="原样品编号")
  1575. allNeedData['样品编号'] = getSimpleDataNumber['样品编号']
  1576. allNeedData['土地利用类型'] = check_1_data['土地利用类型']
  1577. allNeedData['审核结果'] = check_14_data['审核结果']
  1578. allNeedData['外业'] = ['' for _ in range(len(check_1_data))]
  1579. table_7_data = allNeedData[allNeedData['审核结果'] != '']
  1580. del table_7_data['审核结果']
  1581. # 写进表格
  1582. with pd.ExcelWriter(f'{mkdir_path}/超阈值样品统计表.xlsx', engine='openpyxl') as writer:
  1583. table_7_data.to_excel(writer, index=False, sheet_name='超阈值数据')
  1584. # 表8数据
  1585. table_8_data = report.getPHData(heavyMetaData, mkdir_path)
  1586. # 表12数据 重金属超标
  1587. caOverData = pd.merge(check_1_data[['编号', '土地利用类型']], check_14_data[
  1588. ['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',
  1589. on=['编号'])
  1590. caOverData['原样品编号'] = caOverData['编号']
  1591. caOverData = pd.merge(caOverData, heavyMetaData[['原样品编号', '样品编号']], how='left', on='原样品编号')
  1592. first_column = caOverData.pop('样品编号')
  1593. caOverData.insert(0, '样品编号', first_column)
  1594. caOverData_need = caOverData[caOverData['审核结果'] != '']
  1595. # 写进表格
  1596. with pd.ExcelWriter(f'{mkdir_path}/重金属超筛选值情况统计.xlsx', engine='openpyxl') as writer:
  1597. caOverData_need.to_excel(writer, index=False, sheet_name='重金属超筛选值情况统计')
  1598. # 表13 所有存疑数据
  1599. with pd.ExcelWriter(f'{mkdir_path}/数据审核过程存疑数据一览表.xlsx', engine='openpyxl') as writer:
  1600. allNeedData[allNeedData['审核结果'] != ''].to_excel(writer, index=False, sheet_name='存疑数据')
  1601. # 附表: 频度分析图
  1602. # report.getFrequencyImage(heavyMetaData, mkdir_path)
  1603. table_f_2_data = report.getFrequencyInformation(data, mkdir_path)
  1604. # 新建一个文档
  1605. doc = Document()
  1606. # 添加标题
  1607. doc.add_heading(f"{areaName}第三次全国土壤普查数据审核报告", level=0)
  1608. # 添加一级标题
  1609. doc.add_heading('一、数据完整性审核', level=1)
  1610. doc.add_heading('1、土地利用类型与检测指标符合性审核', level=2)
  1611. # 插入表格1
  1612. paragraph_1 = doc.add_paragraph()
  1613. paragraph_1.add_run(f"表1:{areaName}三普样品数量统计表(表层)").bold = True
  1614. # 设置居中
  1615. paragraph_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1616. table_1 = doc.add_table(rows=lenNum_1 + 1, cols=3, style='Light Shading Accent 1')
  1617. table_1.alignment = WD_TABLE_ALIGNMENT.CENTER
  1618. # 遍历表格 插入数据
  1619. # 遍历表格的所有单元格,并填充内容
  1620. for i, row in enumerate(table_1.rows):
  1621. for j, cell in enumerate(row.cells):
  1622. # 获取单元格中的段落对象
  1623. paragraph = cell.paragraphs[0]
  1624. if i == 0:
  1625. r = paragraph.add_run(str(table_1_data.columns[j]))
  1626. r.font.bold = True
  1627. else:
  1628. r = paragraph.add_run(str(table_1_data.iloc[i - 1, j]))
  1629. r.font.size = Pt(10.5)
  1630. r.font.name = 'Times New Roman'
  1631. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1632. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1633. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1634. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1635. # 合并单元格 合并第3列的第二行和第三行
  1636. if lenNum_1 > 1:
  1637. table_1.cell(2, 2).text = ''
  1638. table_1.cell(1, 2).merge(table_1.cell(2, 2))
  1639. ############test##############
  1640. doc.add_heading('2、指标名称与实际检测样品数量完整性审核', level=2)
  1641. # 插入表格2
  1642. paragraph_2 = doc.add_paragraph()
  1643. 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
  1644. table_2 = doc.add_table(rows=len(table_2_data) + 1, cols=3, style='Light Shading Accent 1')
  1645. paragraph_2.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1646. table_2.alignment = WD_TABLE_ALIGNMENT.CENTER
  1647. for i, row in enumerate(table_2.rows):
  1648. for j, cell in enumerate(row.cells):
  1649. # 获取单元格中的段落对象
  1650. paragraph = cell.paragraphs[0]
  1651. if i == 0:
  1652. r = paragraph.add_run(str(table_2_data.columns[j]))
  1653. r.font.bold = True
  1654. else:
  1655. r = paragraph.add_run(str(table_2_data.iloc[i - 1, j]))
  1656. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1657. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1658. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1659. r.font.size = Pt(10.5)
  1660. r.font.name = 'Times New Roman'
  1661. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1662. doc.add_heading('二、数据规范性审核', level=1)
  1663. doc.add_heading('1、数据填报规范性审核', level=2)
  1664. # 插入表3
  1665. paragraph_3 = doc.add_paragraph()
  1666. 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
  1667. # table_3 = doc.add_table(rows=2, cols=2)
  1668. paragraph_3.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1669. # table_3.alignment = WD_TABLE_ALIGNMENT.CENTER
  1670. # 写入数据 这里数据写不下 嵌入链接
  1671. 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)
  1672. doc.add_heading('2、数值修约规范性审核', level=2)
  1673. # 插入表4
  1674. paragraph_4 = doc.add_paragraph()
  1675. paragraph_4.add_run(f'表4:{areaName}土壤检测数据数值修约结果表').bold = True
  1676. # table_4 = doc.add_table(rows=2, cols=2)
  1677. paragraph_4.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1678. # table_4.alignment = WD_TABLE_ALIGNMENT.CENTER
  1679. 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)
  1680. # 填入数据 这里数据也放不下 嵌入链接
  1681. doc.add_heading('3、数据未检出的填报规范性审核', level=2)
  1682. # 插入表5
  1683. paragraph_5 = doc.add_paragraph()
  1684. 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
  1685. # table_5 = doc.add_table(rows=2, cols=2)
  1686. paragraph_5.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1687. # table_5.alignment = WD_TABLE_ALIGNMENT.CENTER
  1688. # 写入数据 这里数据也放不下 嵌入链接
  1689. 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)
  1690. # doc.add_heading('4、土壤质地填报规范性审核', level=2)
  1691. # 插入表6
  1692. # paragraph_6 = doc.add_paragraph()
  1693. # paragraph_6.add_run(f'表6:{areaName}土壤质地填报审核结果表').bold = True
  1694. # table_6 = doc.add_table(rows=len(table_6_data) + 1, cols=4, style='Light Shading Accent 1')
  1695. # paragraph_6.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1696. # table_6.alignment = WD_TABLE_ALIGNMENT.CENTER
  1697. # # 提取结果表中数据
  1698. # # 写入数据 土壤质地类型不一致的数据提取出来
  1699. # for i, row in enumerate(table_6.rows):
  1700. # for j, cell in enumerate(row.cells):
  1701. # # 获取单元格中的段落对象
  1702. # paragraph = cell.paragraphs[0]
  1703. # if i == 0:
  1704. # r = paragraph.add_run(str(table_6_data.columns[j]))
  1705. # r.font.bold = True
  1706. # else:
  1707. # r = paragraph.add_run(str(table_6_data.iloc[i - 1, j]))
  1708. # paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1709. # paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1710. # paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1711. # r.font.size = Pt(10.5)
  1712. # r.font.name = 'Times New Roman'
  1713. # r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1714. doc.add_heading('三、数据合理性审核', level=1)
  1715. doc.add_heading('1、阈值法审核', level=2)
  1716. # 插入表格
  1717. paragraph_7 = doc.add_paragraph()
  1718. paragraph_7.add_run(f'表6:{areaName}土壤检测数据超阈值样品统计表').bold = True
  1719. # table_7 = doc.add_table(rows=2, cols=2)
  1720. # paragraph_7.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1721. # table_7.alignment = WD_TABLE_ALIGNMENT.CENTER
  1722. # 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
  1723. 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)
  1724. # 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
  1725. doc.add_heading('2、极值法审核', level=2)
  1726. doc.add_heading('(1)pH', level=3)
  1727. # 插入ph分布图
  1728. if os.path.isfile(f'{mkdir_path}/pH值分布图.png'):
  1729. doc.add_picture(f'{mkdir_path}/pH值分布图.png', width=Inches(6.0))
  1730. paragraph_t_1 = doc.add_paragraph()
  1731. paragraph_t_1.add_run(f'图1:pH值分布情况').bold = True
  1732. paragraph_t_1.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1733. # 插入频度统计表
  1734. paragraph_8 = doc.add_paragraph()
  1735. paragraph_8.add_run('表7:pH数据统计表').bold = True
  1736. table_8 = doc.add_table(rows=6, cols=2, style='Light Shading Accent 1')
  1737. t_8 = table_8_data['频度分析']
  1738. t_8 = t_8.reset_index()
  1739. t_8.columns = ['指标', '数据']
  1740. paragraph_8.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1741. table_8.alignment = WD_TABLE_ALIGNMENT.CENTER
  1742. for i, row in enumerate(table_8.rows):
  1743. for j, cell in enumerate(row.cells):
  1744. # 获取单元格中的段落对象
  1745. paragraph = cell.paragraphs[0]
  1746. if i == 0:
  1747. r = paragraph.add_run(str(t_8.columns[j]))
  1748. r.font.bold = True
  1749. else:
  1750. r = paragraph.add_run(str(t_8.iloc[i - 1, j]))
  1751. r.font.size = Pt(10.5)
  1752. r.font.name = 'Times New Roman'
  1753. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1754. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1755. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1756. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1757. # 插入异常数据提取表格 todo 这里数据多的话也可能写不下 最好是嵌入一下
  1758. if not table_8_data['异常数据'].empty:
  1759. paragraph_9 = doc.add_paragraph()
  1760. paragraph_9.add_run('表8:pH异常数据统计表').bold = True
  1761. table_9 = doc.add_table(rows=len(table_8_data['异常数据']) + 1, cols=6, style='Light Shading Accent 1')
  1762. t_9 = table_8_data['异常数据']
  1763. paragraph_9.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1764. table_9.alignment = WD_TABLE_ALIGNMENT.CENTER
  1765. for i, row in enumerate(table_9.rows):
  1766. for j, cell in enumerate(row.cells):
  1767. # 获取单元格中的段落对象
  1768. paragraph = cell.paragraphs[0]
  1769. if i == 0:
  1770. r = paragraph.add_run(str(t_9.columns[j]))
  1771. r.font.bold = True
  1772. else:
  1773. r = paragraph.add_run(str(t_9.iloc[i - 1, j]))
  1774. r.font.size = Pt(10.5)
  1775. r.font.name = 'Times New Roman'
  1776. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1777. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1778. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1779. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1780. doc.add_heading('4、指标综合分析', level=2)
  1781. doc.add_heading('表9:重金属超筛选值情况统计', level=4)
  1782. # todo 获取重金属数据
  1783. doc.add_heading('四、审核存疑数据', level=1)
  1784. paragraph_12 = doc.add_paragraph()
  1785. paragraph_12.add_run(f'表10:数据审核过程存疑数据一览表').bold = True
  1786. paragraph_12.alignment = WD_ALIGN_PARAGRAPH.CENTER
  1787. 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)
  1788. doc.add_heading('五、附表', level=1)
  1789. doc.add_heading('附表1:某区三普样品数量统计表(表层)', level=2)
  1790. # 插入附表1
  1791. table_1_f = doc.add_table(rows=lenNum_1 + 1, cols=3, style='Light Shading Accent 1')
  1792. table_1_f.alignment = WD_TABLE_ALIGNMENT.CENTER
  1793. # 遍历表格 插入数据
  1794. # 遍历表格的所有单元格,并填充内容
  1795. for i, row in enumerate(table_1_f.rows):
  1796. for j, cell in enumerate(row.cells):
  1797. # 获取单元格中的段落对象
  1798. paragraph = cell.paragraphs[0]
  1799. if i == 0:
  1800. r = paragraph.add_run(str(table_1_data.columns[j]))
  1801. r.font.bold = True
  1802. else:
  1803. r = paragraph.add_run(str(table_1_data.iloc[i - 1, j]))
  1804. r.font.size = Pt(10.5)
  1805. r.font.name = 'Times New Roman'
  1806. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1807. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1808. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1809. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1810. # 合并单元格 合并第3列的第二行和第三行
  1811. if lenNum_1 > 1:
  1812. table_1_f.cell(2, 2).text = ''
  1813. table_1_f.cell(1, 2).merge(table_1_f.cell(2, 2))
  1814. doc.add_heading('附表2:各指标频度分析表', level=2)
  1815. # 插入表格 写入数据
  1816. table_f_2_data = table_f_2_data.replace(np.nan, '')
  1817. makeInfoTable(table_f_2_data, doc)
  1818. # table_f_2 = doc.add_table(rows=len(table_f_2_data) + 1, cols=6, style='Light Shading Accent 1')
  1819. # for i, row in enumerate(table_f_2.rows):
  1820. # for j, cell in enumerate(row.cells):
  1821. # # 获取单元格中的段落对象
  1822. # paragraph = cell.paragraphs[0]
  1823. # if i == 0:
  1824. # r = paragraph.add_run(str(table_f_2_data.columns[j]))
  1825. # r.font.bold = True
  1826. # else:
  1827. # r = paragraph.add_run(str(table_f_2_data.iloc[i - 1, j]))
  1828. # r.font.size = Pt(10.5)
  1829. # r.font.name = 'Times New Roman'
  1830. # r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1831. # paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1832. # paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1833. # paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1834. # 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)
  1835. doc.add_heading('附表3:各指标频度分析图', level=2)
  1836. # 插入频度信息的图形
  1837. if os.path.isfile(f'{mkdir_path}/pH分析图.png'):
  1838. doc.add_picture(f'{mkdir_path}/pH分析图.png', width=Inches(6.0))
  1839. if os.path.isfile(f'{mkdir_path}/总镉分析图.png'):
  1840. doc.add_picture(f'{mkdir_path}/总镉分析图.png', width=Inches(6.0))
  1841. if os.path.isfile(f'{mkdir_path}/总铬分析图.png'):
  1842. doc.add_picture(f'{mkdir_path}/总铬分析图.png', width=Inches(6.0))
  1843. if os.path.isfile(f'{mkdir_path}/总汞分析图.png'):
  1844. doc.add_picture(f'{mkdir_path}/总汞分析图.png', width=Inches(6.0))
  1845. if os.path.isfile(f'{mkdir_path}/总镍分析图.png'):
  1846. doc.add_picture(f'{mkdir_path}/总镍分析图.png', width=Inches(6.0))
  1847. if os.path.isfile(f'{mkdir_path}/总砷分析图.png'):
  1848. doc.add_picture(f'{mkdir_path}/总砷分析图.png', width=Inches(6.0))
  1849. if os.path.isfile(f'{mkdir_path}/总铅分析图.png'):
  1850. doc.add_picture(f'{mkdir_path}/总铅分析图.png', width=Inches(6.0))
  1851. doc.add_heading('附表4:数值修约标准', level=2)
  1852. # 读取数据 插入表格 写入数据
  1853. numData = pd.read_excel('./img/数值修约要求.xlsx', sheet_name='Sheet1')
  1854. table_2_f = doc.add_table(rows=len(numData) + 1, cols=2, style='Light Shading Accent 1')
  1855. table_2_f.alignment = WD_TABLE_ALIGNMENT.CENTER
  1856. for i, row in enumerate(table_2_f.rows):
  1857. for j, cell in enumerate(row.cells):
  1858. # 获取单元格中的段落对象
  1859. paragraph = cell.paragraphs[0]
  1860. if i == 0:
  1861. r = paragraph.add_run(str(numData.columns[j]))
  1862. r.font.bold = True
  1863. else:
  1864. r = paragraph.add_run(str(numData.iloc[i - 1, j]))
  1865. r.font.size = Pt(10.5)
  1866. r.font.name = 'Times New Roman'
  1867. r.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1868. paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
  1869. paragraph.paragraph_format.alignment = WD_TABLE_ALIGNMENT.CENTER # 对齐
  1870. paragraph.paragraph_format.line_spacing = 1 # 段落行间距
  1871. # 处理样式 遍历所有的段落 修改字体
  1872. # 遍历并打印每个段落的文本
  1873. paragraphs = doc.paragraphs
  1874. for paragraph in paragraphs:
  1875. for run in paragraph.runs:
  1876. run.font.color.rgb = RGBColor(0, 0, 0)
  1877. run.font.name = 'Times New Roman'
  1878. run.font.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1879. # run.element.rPr.rFonts.set(qn('w:eastAsia'), u'仿宋_GB2312')
  1880. # 保存Word文档
  1881. doc.save(f'{mkdir_path}/{areaName}-{type}审核报告.docx')