mirror of
https://github.com/dptech-corp/Uni-Lab-OS.git
synced 2025-12-17 21:11:12 +00:00
添加Raman和xrd相关代码
This commit is contained in:
WenzheG
Xuwznln
180
unilabos/devices/opsky_Raman/raman_module.py
Normal file
180
unilabos/devices/opsky_Raman/raman_module.py
Normal file
@@ -0,0 +1,180 @@
|
||||
# raman_module.py
|
||||
import os
|
||||
import time as time_mod
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
# clr / ATRWrapper 依赖：在真实环境中使用 Windows + .NET wrapper
|
||||
# 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。
|
||||
try:
|
||||
import clr
|
||||
has_clr = True
|
||||
except Exception:
|
||||
clr = None
|
||||
has_clr = False
|
||||
|
||||
# 本函数返回 (success: bool, file_prefix: str|None, df: pandas.DataFrame|None)
|
||||
def run_raman_test(integration_time=5000, laser_power=200,
|
||||
save_csv=True, save_plot=True,
|
||||
normalize=False, norm_max=None,
|
||||
max_wavenum=1300):
|
||||
"""
|
||||
拉曼测试流程（更稳健的实现）：
|
||||
- 若能加载 ATRWrapper 则使用之
|
||||
- 否则生成模拟光谱（方便调试）
|
||||
返回 (success, file_prefix, df)
|
||||
"""
|
||||
timestamp = time_mod.strftime("%Y%m%d_%H%M%S")
|
||||
file_prefix = f"raman_{timestamp}"
|
||||
|
||||
wrapper = None
|
||||
used_real_device = False
|
||||
|
||||
try:
|
||||
if has_clr:
|
||||
try:
|
||||
# 请根据你的 DLL 路径调整
|
||||
dll_path = r"D:\Raman\Raman_RS\ATRWrapper\ATRWrapper.dll"
|
||||
if os.path.exists(dll_path):
|
||||
clr.AddReference(dll_path)
|
||||
else:
|
||||
# 试图直接 AddReference 名称（若已在 PATH）
|
||||
try:
|
||||
clr.AddReference("ATRWrapper")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
from Optosky.Wrapper import ATRWrapper # May raise
|
||||
wrapper = ATRWrapper()
|
||||
used_real_device = True
|
||||
except Exception as e:
|
||||
# 无法加载真实 wrapper -> fallback
|
||||
print("⚠️ 未能加载 ATRWrapper，使用模拟数据。详细:", e)
|
||||
wrapper = None
|
||||
|
||||
if wrapper is None:
|
||||
# 生成模拟光谱（方便调试）
|
||||
# 模拟波数轴 50..1300
|
||||
WaveNum = np.linspace(50, max_wavenum, 1024)
|
||||
# 合成几条高斯峰 + 噪声
|
||||
def gauss(x, mu, sig, A):
|
||||
return A * np.exp(-0.5 * ((x - mu) / sig) ** 2)
|
||||
Spect_data = (gauss(WaveNum, 200, 8, 1000) +
|
||||
gauss(WaveNum, 520, 12, 600) +
|
||||
gauss(WaveNum, 810, 20, 400) +
|
||||
50 * np.random.normal(scale=1.0, size=WaveNum.shape))
|
||||
Spect_bLC = Spect_data - np.min(Spect_data) * 0.05 # 简单 baseline
|
||||
Spect_smooth = np.convolve(Spect_bLC, np.ones(3) / 3, mode="same")
|
||||
df = pd.DataFrame({
|
||||
"WaveNum": WaveNum,
|
||||
"Raw_Spect": Spect_data,
|
||||
"BaseLineCorrected": Spect_bLC,
|
||||
"Smooth_Spect": Spect_smooth
|
||||
})
|
||||
success = True
|
||||
file_prefix = f"raman_sim_{timestamp}"
|
||||
# 保存 CSV / 绘图 等同真实设备
|
||||
else:
|
||||
# 使用真实设备 API（根据你提供的 wrapper 调用）
|
||||
On_flag = wrapper.OpenDevice()
|
||||
print("通讯连接状态:", On_flag)
|
||||
if not On_flag:
|
||||
wrapper.CloseDevice()
|
||||
return False, None, None
|
||||
|
||||
wrapper.SetIntegrationTime(int(integration_time))
|
||||
wrapper.SetLdPower(int(laser_power), 1)
|
||||
# 可能的冷却设置（如果 wrapper 支持）
|
||||
try:
|
||||
wrapper.SetCool(-5)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
Spect = wrapper.AcquireSpectrum()
|
||||
Spect_data = np.array(Spect.get_Data())
|
||||
if not Spect.get_Success():
|
||||
print("光谱采集失败")
|
||||
try:
|
||||
wrapper.CloseDevice()
|
||||
except Exception:
|
||||
pass
|
||||
return False, None, None
|
||||
WaveNum = np.array(wrapper.GetWaveNum())
|
||||
Spect_bLC = np.array(wrapper.BaseLineCorrect(Spect_data))
|
||||
Spect_smooth = np.array(wrapper.SmoothBoxcar(Spect_bLC, 3))
|
||||
df = pd.DataFrame({
|
||||
"WaveNum": WaveNum,
|
||||
"Raw_Spect": Spect_data,
|
||||
"BaseLineCorrected": Spect_bLC,
|
||||
"Smooth_Spect": Spect_smooth
|
||||
})
|
||||
wrapper.CloseDevice()
|
||||
success = True
|
||||
|
||||
# 如果需要限定波数范围
|
||||
mask = df["WaveNum"] <= max_wavenum
|
||||
df = df[mask].reset_index(drop=True)
|
||||
|
||||
# 可选归一化
|
||||
if normalize:
|
||||
arr = df["Smooth_Spect"].values
|
||||
mn, mx = arr.min(), arr.max()
|
||||
if mx == mn:
|
||||
df["Smooth_Spect"] = 0.0
|
||||
else:
|
||||
scale = 1.0 if norm_max is None else float(norm_max)
|
||||
df["Smooth_Spect"] = (arr - mn) / (mx - mn) * scale
|
||||
# 同时处理其它列（可选）
|
||||
arr_raw = df["Raw_Spect"].values
|
||||
mn_r, mx_r = arr_raw.min(), arr_raw.max()
|
||||
if mx_r == mn_r:
|
||||
df["Raw_Spect"] = 0.0
|
||||
else:
|
||||
scale = 1.0 if norm_max is None else float(norm_max)
|
||||
df["Raw_Spect"] = (arr_raw - mn_r) / (mx_r - mn_r) * scale
|
||||
|
||||
# 保存 CSV
|
||||
if save_csv:
|
||||
csv_filename = f"{file_prefix}.csv"
|
||||
df.to_csv(csv_filename, index=False)
|
||||
print("✅ CSV 文件已生成:", csv_filename)
|
||||
|
||||
# 绘图（使用 matplotlib），注意：不要启用 GUI 后台
|
||||
if save_plot:
|
||||
try:
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
plt.figure(figsize=(8, 5))
|
||||
plt.plot(df["WaveNum"], df["Raw_Spect"], linestyle='-', alpha=0.6, label="原始")
|
||||
plt.plot(df["WaveNum"], df["BaseLineCorrected"], linestyle='--', alpha=0.8, label="基线校正")
|
||||
plt.plot(df["WaveNum"], df["Smooth_Spect"], linewidth=1.2, label="平滑")
|
||||
plt.xlabel("WaveNum (cm^-1)")
|
||||
plt.ylabel("Intensity (a.u.)")
|
||||
plt.title(f"Raman {file_prefix}")
|
||||
plt.grid(True)
|
||||
plt.legend()
|
||||
plt.tight_layout()
|
||||
plot_filename = f"{file_prefix}.png"
|
||||
plt.savefig(plot_filename, dpi=300, bbox_inches="tight")
|
||||
plt.close()
|
||||
# 小短暂等待以确保文件系统刷新
|
||||
time_mod.sleep(0.2)
|
||||
print("✅ 图像已生成:", plot_filename)
|
||||
except Exception as e:
|
||||
print("⚠️ 绘图失败:", e)
|
||||
|
||||
return success, file_prefix, df
|
||||
|
||||
except Exception as e:
|
||||
print("拉曼测试异常:", e)
|
||||
try:
|
||||
if wrapper is not None:
|
||||
try:
|
||||
wrapper.CloseDevice()
|
||||
except Exception:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
return False, None, None
|
||||
Reference in New Issue
Block a user