mirror of
https://github.com/deepmodeling/Uni-Lab-OS
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89 lines
3.8 KiB
Python
89 lines
3.8 KiB
Python
"""虚拟样品演示设备 — 用于前端 sample tracking 功能的极简 demo"""
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import asyncio
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import logging
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import random
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import time
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from typing import Any, Dict, List, Optional
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class VirtualSampleDemo:
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"""虚拟样品追踪演示设备，提供两种典型返回模式：
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- measure_samples: 等长输入输出 (前端按 index 自动对齐)
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- split_and_measure: 输出比输入长，附带 samples 列标注归属
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"""
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def __init__(self, device_id: Optional[str] = None, config: Optional[Dict[str, Any]] = None, **kwargs):
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if device_id is None and "id" in kwargs:
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device_id = kwargs.pop("id")
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if config is None and "config" in kwargs:
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config = kwargs.pop("config")
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self.device_id = device_id or "unknown_sample_demo"
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self.config = config or {}
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self.logger = logging.getLogger(f"VirtualSampleDemo.{self.device_id}")
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self.data: Dict[str, Any] = {"status": "Idle"}
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# ------------------------------------------------------------------
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# Action 1: 等长输入输出，无 samples 列
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# ------------------------------------------------------------------
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async def measure_samples(self, concentrations: List[float]) -> Dict[str, Any]:
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"""模拟光度测量。absorbance = concentration * 0.05 + noise
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入参和出参 list 长度相等，前端按 index 自动对齐。
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"""
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self.logger.info(f"measure_samples: concentrations={concentrations}")
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absorbance = [round(c * 0.05 + random.gauss(0, 0.005), 4) for c in concentrations]
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return {"concentrations": concentrations, "absorbance": absorbance}
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# ------------------------------------------------------------------
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# Action 2: 输出比输入长，带 samples 列
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# ------------------------------------------------------------------
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async def split_and_measure(self, volumes: List[float], split_count: int = 3) -> Dict[str, Any]:
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"""将每个样品均分为 split_count 份后逐份测量。
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返回的 list 长度 = len(volumes) * split_count，
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附带 samples 列标注每行属于第几个输入样品 (0-based index)。
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"""
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self.logger.info(f"split_and_measure: volumes={volumes}, split_count={split_count}")
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out_volumes: List[float] = []
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readings: List[float] = []
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samples: List[int] = []
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for idx, vol in enumerate(volumes):
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split_vol = round(vol / split_count, 2)
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for _ in range(split_count):
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out_volumes.append(split_vol)
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readings.append(round(random.uniform(0.1, 1.0), 4))
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samples.append(idx)
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return {"volumes": out_volumes, "readings": readings, "samples": samples}
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# ------------------------------------------------------------------
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# Action 3: 入参和出参都带 samples 列（不等长）
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# ------------------------------------------------------------------
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async def analyze_readings(self, readings: List[float], samples: List[int]) -> Dict[str, Any]:
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"""对 split_and_measure 的输出做二次分析。
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入参 readings/samples 长度相同但 > 原始样品数，
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出参同样带 samples 列，长度与入参一致。
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"""
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self.logger.info(f"analyze_readings: readings={readings}, samples={samples}")
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scores: List[float] = []
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passed: List[bool] = []
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threshold = 0.4
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for r in readings:
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score = round(r * 100 + random.gauss(0, 2), 2)
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scores.append(score)
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passed.append(r >= threshold)
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return {"scores": scores, "passed": passed, "samples": samples}
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# ------------------------------------------------------------------
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# 状态属性
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# ------------------------------------------------------------------
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@property
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def status(self) -> str:
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return self.data.get("status", "Idle")
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