96 lines
3.4 KiB
Python
96 lines
3.4 KiB
Python
"""
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从模板文件重新提取所有检测项目的临床意义
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生成正确的 template_explanations.json
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"""
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from docx import Document
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import json
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def main():
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doc = Document('template_complete.docx')
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explanations = {}
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# 遍历所有表格
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for table in doc.tables:
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rows = list(table.rows)
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for i, row in enumerate(rows):
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cells = row.cells
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if not cells:
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continue
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first_cell = cells[0].text.strip()
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# 跳过空行、表头、模块标题
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if not first_cell:
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continue
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if 'Abb' in first_cell or '简称' in first_cell:
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continue
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if 'Clinical' in first_cell:
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continue
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# 检查是否是ABB行（短文本，不含占位符，不含中文模块名）
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if len(first_cell) > 40 or '{{' in first_cell:
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continue
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# 跳过模块标题（包含换行符和中文）
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if '\n' in first_cell and any('\u4e00' <= c <= '\u9fff' for c in first_cell):
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continue
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# 这是一个ABB，查找下一行的临床意义
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abb = first_cell
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# 在当前行或下一行查找临床意义
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clinical_text = None
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# 先检查当前行的其他单元格
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for cell in cells:
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text = cell.text.strip()
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if 'Clinical Significance:' in text and '临床意义：' in text:
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clinical_text = text
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break
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# 如果当前行没有，检查下一行
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if not clinical_text and i + 1 < len(rows):
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next_row = rows[i + 1]
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for cell in next_row.cells:
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text = cell.text.strip()
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if 'Clinical Significance:' in text and '临床意义：' in text:
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clinical_text = text
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break
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if clinical_text:
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# 提取英文和中文
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parts = clinical_text.split('临床意义：')
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if len(parts) == 2:
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en = parts[0].replace('Clinical Significance:', '').strip()
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cn = parts[1].strip()
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if en and cn:
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# 标准化ABB名称
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abb_key = abb.upper().strip()
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abb_key = abb_key.replace(' - ', '-').replace('（', '(').replace('）', ')')
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if abb_key not in explanations:
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explanations[abb_key] = {
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'clinical_en': en,
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'clinical_cn': cn
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}
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print(f'从模板提取了 {len(explanations)} 个项目的临床意义')
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# 保存
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with open('template_explanations.json', 'w', encoding='utf-8') as f:
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json.dump(explanations, f, ensure_ascii=False, indent=2)
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print(f'已保存到 template_explanations.json')
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# 验证 Color
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if 'COLOR' in explanations:
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print(f'\nCOLOR 验证:')
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print(f'EN: {explanations["COLOR"]["clinical_en"][:80]}...')
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print(f'CN: {explanations["COLOR"]["clinical_cn"][:80]}...')
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if __name__ == '__main__':
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main()
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