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181 lines
6.8 KiB
Python
181 lines
6.8 KiB
Python
# raman_module.py
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import os
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import time as time_mod
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import numpy as np
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import pandas as pd
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# clr / ATRWrapper 依赖：在真实环境中使用 Windows + .NET wrapper
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# 本模块对缺少 clr 或 Wrapper 的情况提供“仿真”回退，方便离线/调试运行。
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try:
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import clr
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has_clr = True
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except Exception:
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clr = None
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has_clr = False
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# 本函数返回 (success: bool, file_prefix: str|None, df: pandas.DataFrame|None)
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def run_raman_test(integration_time=5000, laser_power=200,
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save_csv=True, save_plot=True,
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normalize=False, norm_max=None,
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max_wavenum=1300):
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"""
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拉曼测试流程（更稳健的实现）：
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- 若能加载 ATRWrapper 则使用之
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- 否则生成模拟光谱（方便调试）
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返回 (success, file_prefix, df)
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"""
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timestamp = time_mod.strftime("%Y%m%d_%H%M%S")
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file_prefix = f"raman_{timestamp}"
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wrapper = None
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used_real_device = False
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try:
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if has_clr:
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try:
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# 请根据你的 DLL 路径调整
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dll_path = r"D:\Raman\Raman_RS\ATRWrapper\ATRWrapper.dll"
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if os.path.exists(dll_path):
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clr.AddReference(dll_path)
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else:
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# 试图直接 AddReference 名称（若已在 PATH）
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try:
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clr.AddReference("ATRWrapper")
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except Exception:
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pass
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from Optosky.Wrapper import ATRWrapper # May raise
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wrapper = ATRWrapper()
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used_real_device = True
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except Exception as e:
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# 无法加载真实 wrapper -> fallback
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print("⚠️ 未能加载 ATRWrapper，使用模拟数据。详细:", e)
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wrapper = None
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if wrapper is None:
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# 生成模拟光谱（方便调试）
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# 模拟波数轴 50..1300
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WaveNum = np.linspace(50, max_wavenum, 1024)
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# 合成几条高斯峰 + 噪声
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def gauss(x, mu, sig, A):
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return A * np.exp(-0.5 * ((x - mu) / sig) ** 2)
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Spect_data = (gauss(WaveNum, 200, 8, 1000) +
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gauss(WaveNum, 520, 12, 600) +
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gauss(WaveNum, 810, 20, 400) +
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50 * np.random.normal(scale=1.0, size=WaveNum.shape))
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Spect_bLC = Spect_data - np.min(Spect_data) * 0.05 # 简单 baseline
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Spect_smooth = np.convolve(Spect_bLC, np.ones(3) / 3, mode="same")
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df = pd.DataFrame({
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"WaveNum": WaveNum,
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"Raw_Spect": Spect_data,
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"BaseLineCorrected": Spect_bLC,
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"Smooth_Spect": Spect_smooth
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})
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success = True
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file_prefix = f"raman_sim_{timestamp}"
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# 保存 CSV / 绘图 等同真实设备
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else:
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# 使用真实设备 API（根据你提供的 wrapper 调用）
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On_flag = wrapper.OpenDevice()
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print("通讯连接状态:", On_flag)
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if not On_flag:
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wrapper.CloseDevice()
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return False, None, None
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wrapper.SetIntegrationTime(int(integration_time))
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wrapper.SetLdPower(int(laser_power), 1)
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# 可能的冷却设置（如果 wrapper 支持）
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try:
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wrapper.SetCool(-5)
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except Exception:
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pass
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Spect = wrapper.AcquireSpectrum()
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Spect_data = np.array(Spect.get_Data())
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if not Spect.get_Success():
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print("光谱采集失败")
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try:
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wrapper.CloseDevice()
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except Exception:
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pass
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return False, None, None
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WaveNum = np.array(wrapper.GetWaveNum())
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Spect_bLC = np.array(wrapper.BaseLineCorrect(Spect_data))
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Spect_smooth = np.array(wrapper.SmoothBoxcar(Spect_bLC, 3))
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df = pd.DataFrame({
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"WaveNum": WaveNum,
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"Raw_Spect": Spect_data,
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"BaseLineCorrected": Spect_bLC,
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"Smooth_Spect": Spect_smooth
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})
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wrapper.CloseDevice()
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success = True
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# 如果需要限定波数范围
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mask = df["WaveNum"] <= max_wavenum
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df = df[mask].reset_index(drop=True)
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# 可选归一化
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if normalize:
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arr = df["Smooth_Spect"].values
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mn, mx = arr.min(), arr.max()
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if mx == mn:
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df["Smooth_Spect"] = 0.0
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else:
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scale = 1.0 if norm_max is None else float(norm_max)
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df["Smooth_Spect"] = (arr - mn) / (mx - mn) * scale
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# 同时处理其它列（可选）
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arr_raw = df["Raw_Spect"].values
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mn_r, mx_r = arr_raw.min(), arr_raw.max()
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if mx_r == mn_r:
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df["Raw_Spect"] = 0.0
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else:
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scale = 1.0 if norm_max is None else float(norm_max)
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df["Raw_Spect"] = (arr_raw - mn_r) / (mx_r - mn_r) * scale
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# 保存 CSV
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if save_csv:
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csv_filename = f"{file_prefix}.csv"
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df.to_csv(csv_filename, index=False)
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print("✅ CSV 文件已生成:", csv_filename)
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# 绘图（使用 matplotlib），注意：不要启用 GUI 后台
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if save_plot:
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try:
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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plt.figure(figsize=(8, 5))
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plt.plot(df["WaveNum"], df["Raw_Spect"], linestyle='-', alpha=0.6, label="原始")
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plt.plot(df["WaveNum"], df["BaseLineCorrected"], linestyle='--', alpha=0.8, label="基线校正")
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plt.plot(df["WaveNum"], df["Smooth_Spect"], linewidth=1.2, label="平滑")
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plt.xlabel("WaveNum (cm^-1)")
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plt.ylabel("Intensity (a.u.)")
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plt.title(f"Raman {file_prefix}")
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plt.grid(True)
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plt.legend()
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plt.tight_layout()
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plot_filename = f"{file_prefix}.png"
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plt.savefig(plot_filename, dpi=300, bbox_inches="tight")
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plt.close()
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# 小短暂等待以确保文件系统刷新
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time_mod.sleep(0.2)
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print("✅ 图像已生成:", plot_filename)
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except Exception as e:
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print("⚠️ 绘图失败:", e)
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return success, file_prefix, df
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except Exception as e:
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print("拉曼测试异常:", e)
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try:
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if wrapper is not None:
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try:
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wrapper.CloseDevice()
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except Exception:
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pass
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except Exception:
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pass
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return False, None, None
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