Files
AIclinicalresearch/extraction_service/test_execute_simple.py
HaHafeng 9b81aef9a7 feat(dc): Add multi-metric transformation feature (direction 1+2)
Summary:
- Implement intelligent multi-metric grouping detection algorithm
- Add direction 1: timepoint-as-row, metric-as-column (analysis format)
- Add direction 2: timepoint-as-column, metric-as-row (display format)
- Fix column name pattern detection (FMA___ issue)
- Maintain original Record ID order in output
- Add full-select/clear buttons in UI
- Integrate into TransformDialog with Radio selection
- Update 3 documentation files

Technical Details:
- Python: detect_metric_groups(), apply_multi_metric_to_long(), apply_multi_metric_to_matrix()
- Backend: 3 new methods in QuickActionService
- Frontend: MultiMetricPanel.tsx (531 lines)
- Total: ~1460 lines of new code

Status: Fully tested and verified, ready for production
2025-12-21 15:06:15 +08:00

66 lines
1.3 KiB
Python

"""简单的代码执行测试"""
import requests
import json
# 测试数据
test_data = [
{"patient_id": "P001", "age": 25, "gender": ""},
{"patient_id": "P002", "age": 65, "gender": ""},
{"patient_id": "P003", "age": 45, "gender": ""},
]
# 测试代码
test_code = """
df['age_group'] = df['age'].apply(lambda x: '老年' if x > 60 else '非老年')
print(f"处理完成,共 {len(df)} 行")
"""
print("=" * 60)
print("测试: Pandas代码执行")
print("=" * 60)
try:
response = requests.post(
"http://localhost:8000/api/dc/execute",
json={"data": test_data, "code": test_code},
timeout=10
)
print(f"\n状态码: {response.status_code}")
result = response.json()
print(json.dumps(result, indent=2, ensure_ascii=False))
if result.get("success"):
print("\n✅ 代码执行成功!")
print(f"结果数据: {len(result.get('result_data', []))}")
print(f"执行时间: {result.get('execution_time', 0):.3f}")
print(f"\n打印输出:\n{result.get('output', '')}")
print(f"\n结果数据示例:")
for row in result.get('result_data', [])[:3]:
print(f" {row}")
else:
print(f"\n❌ 代码执行失败: {result.get('error')}")
except Exception as e:
print(f"\n❌ 测试异常: {str(e)}")