From 78c02df9afc8e96338720a97432eed499752e5ad Mon Sep 17 00:00:00 2001
From: linarphy <linarphy@linarphy.net>
Date: Fri, 23 Jun 2023 10:09:51 +0200
Subject: [PATCH] Add bias substraction after wavelength calibration

---
 spectrum.py | 58 +++++++++++++++++++++++++++--------------------------
 1 file changed, 30 insertions(+), 28 deletions(-)

diff --git a/spectrum.py b/spectrum.py
index 5fc2869..ac46b52 100644
--- a/spectrum.py
+++ b/spectrum.py
@@ -531,40 +531,42 @@ if calibration != None:
 
     wavelengths = np.polyval( wavelength_polyval , abciss )
 
-    """
-    mean_up = np.mean( data[
-        calibrations[ 'top' ][ 'y' ][ 'min' ] : calibrations[ 'top' ][ 'y' ][ 'max' ],
-        calibrations[ 'top' ][ 'x' ][ 'min' ] : claibrations[ 'top' ][ 'x' ][ 'max' ]
-    ] , axis = 0 )
-    mean_up = np.mean( data[
-        calibrations[ 'down' ][ 'y' ][ 'min' ] : calibrations[ 'down' ][ 'y' ][ 'max' ],
-        calibrations[ 'down' ][ 'x' ][ 'min' ] : claibrations[ 'down' ][ 'x' ][ 'max' ]
-    ] , axis = 0 )
-    peaks_up = np.array(
-        utils.retrieve_peaks(
-            mean_up            ,
-            window_size = 1    ,
-            max_window_size = 1,
-        )          ,
-        dtype = int,
-    )
-    peaks_down = np.array(
-        utils.retrieve_peaks(
-            mean_down          ,
-            window_size = 1    ,
-            max_window_size = 1,
-        )          ,
-        dtype = int,
-    )
-    """
-
     if verbose:
         print( 'wavelength calibration finished' )
 
+if verbose:
+    print( 'starting bias substraction' )
+
+bias = {
+    'top':  np.mean(
+        data[
+            calibrations[ 'top' ][ 'y' ][ 'min' ] - 100 :
+            calibrations[ 'top' ][ 'y' ][ 'min' ]       ,
+            calibrations[ 'top' ][ 'x' ][ 'min' ]       :
+            calibrations[ 'top' ][ 'x' ][ 'max' ]
+        ]       ,
+        axis = 0,
+    ),
+    'down': np.mean(
+        data[
+            calibrations[ 'down' ][ 'y' ][ 'max' ]      :
+            calibrations[ 'down' ][ 'y' ][ 'max' ] + 100,
+            calibrations[ 'down' ][ 'x' ][ 'min' ]      :
+            calibrations[ 'down' ][ 'x' ][ 'max' ]
+        ]       ,
+        axis = 0,
+    ),
+}
+
+mean_bias = np.mean( [ bias[ 'top' ] , bias[ 'down' ] ] , axis = 0 )
+
+if verbose:
+    print( 'bias substraction finished' )
+
 mean_data = np.mean( data[
     spectrum[ 'y' ][ 'min' ] : spectrum[ 'y' ][ 'max' ],
     spectrum[ 'x' ][ 'min' ] : spectrum[ 'x' ][ 'max' ]
-] , axis = 0 )
+] , axis = 0 ) - mean_bias
 
 if intensity != None:
     if verbose: