From 68cf04e5c3966c628851531e297717925ae33c7d Mon Sep 17 00:00:00 2001
From: esea_info <info@eseaoptics.com>
Date: Wed, 2 Aug 2023 18:26:19 +0800
Subject: [PATCH] immersion factors

---
 profiler.py | 31 ++++++++++++++++++++++++++++---
 receive.py  |  2 ++
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/profiler.py b/profiler.py
index e27104d..55c5735 100644
--- a/profiler.py
+++ b/profiler.py
@@ -9,7 +9,7 @@ from scipy.optimize import leastsq
 from datetime import datetime, timedelta
 from myRamses import RamsesFactoryHandle,AirWater
 from tools.mylogger import log
-from myconfig import TOKEN,NEWLINE,DATA_DIR,SAVE_EXT_NAME 
+from myconfig import TOKEN,NEWLINE,DATA_DIR,SAVE_EXT_NAME,CAL_DIR
 from myconfig import  RamsesPROFILE
 
 TIME_STR = "2022-06-10 16:16:16"
@@ -288,6 +288,7 @@ class Profiler(object):
     depth : List[float]     =None
     wavelength:List[np.ndarray]         =None
     spectrum:List[np.ndarray]           =None
+    immersion_factors:np.ndarray = None
     beginWavelength: float  = None
     endWavelength: float    = None
     rowFactor:float         = None
@@ -329,6 +330,12 @@ class Profiler(object):
         self.newWavelength      = np.arange(self.beginWavelength,self.endWavelength,self.wvInterval)
         pass
 
+    def set_immersion_factors(self, ifs:np.ndarray):
+        wv = ifs[:, 0]
+        val = ifs[:, 1]
+        self.immersion_factors = np.interp(self.newWavelength,wv,val)
+        pass
+
     def set_ramses_sensor_numbers(self, num:int = 3):
         self.data.data_frame = DataFrame()
         self.data.data_frame.set_ramses_num( num )
@@ -487,7 +494,11 @@ class Profiler(object):
         '''' Get  Lu0    Ku   '''
         log.info( " get_lu0_Ku.........  ", __class__, "get_Lu0_Ku" )
         x = np.array(self.depth)
-        y = np.log(self.Lu)   # ln_Lu
+        
+        ## 浸没因子处理 ##
+        tmp  = self.deal_Lu_by_immersion_factors()
+        y = np.log(tmp)   # ln_Lu
+        
         self.Ku,ln_Lu0 = self.leastsq_linear_fitting( x, y)
         self.Lu0 = np.exp(ln_Lu0)
 
@@ -506,6 +517,12 @@ class Profiler(object):
         # log.warning(t)
         # log.warning(self.Ed0)
 
+    def deal_Lu_by_immersion_factors ( self, ):
+        '''' ?????? 浸没因子处理 Lu  ??? '''
+        ifs = self.immersion_factors
+        lu  = self.Lu
+        return lu
+    
     def leastsq_linear_fitting( self, arr_depth:np.ndarray, arr_data:np.ndarray ):
         '''最小二乘拟合
         # arr_depth np一维数组
@@ -633,7 +650,15 @@ class ProfilerHandle(object):
 
         self.profiler.data.set_data_frame( bytes_list, pth, self.aw.callback )   # 目录也要传过去
         pass
-    
+ 
+    def read_immersion_factors(self,)->np.ndarray: 
+        assert fpath.exists(), f">>>> not find {fpath} "
+        fname   = "immersion_factors_Lu.dat"
+        fpath   = CAL_DIR.joinpath( "profiler" ,fname)
+        ret = np.loadtxt(fpath,skiprows=2)
+        return ret
+        pass
+        
     def read_bin(self,fpath: Path): 
         assert fpath.exists(), f">>>> not find {fpath} "
         ret = b''
diff --git a/receive.py b/receive.py
index 9113e2d..ac7920b 100644
--- a/receive.py
+++ b/receive.py
@@ -219,6 +219,8 @@ class DealData:
         self.profiler_hdl.read_info_path(infopath)
         self.profiler_hdl.profiler.reset_profiler()
         self.profiler_hdl.profiler.data.set_wavelength(  )   # 设置波长
+        # self.profiler_hdl.read_immersion_factors( )  # 读取浸没因子
+        self.profiler_hdl.profiler.set_immersion_factors( self.profiler_hdl.read_immersion_factors( ) ) #设置浸没因子
         self.profiler_hdl.profiler.get_depth_offset()
         self.profiler_hdl.read_sensor_path(sensorpath)