From 6a423e528a3f80acad5dce3c10ae1c460f792af4 Mon Sep 17 00:00:00 2001
From: linarphy <linarphy@linarphy.net>
Date: Tue, 16 May 2023 11:13:30 +0200
Subject: [PATCH] Update indicator to find calibrations lines

---
 ETA.py | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/ETA.py b/ETA.py
index 1528701..2489dff 100644
--- a/ETA.py
+++ b/ETA.py
@@ -8,13 +8,15 @@ from scipy.signal import convolve as sp_convolve
 from scipy.signal import find_peaks
 from scipy.ndimage import rotate
 
+cache = True
 if len( sys.argv ) < 2:
     raise Exception( 'this command must have a filename of an ETA fits as an argument' )
+
 data = utils.load( sys.argv[1] )
 
 cache_file = pathlib.Path( 'asset/points_' + sys.argv[1].split( '/' )[-1][:-5] + '.pag' )
 
-if cache_file.is_file():
+if cache_file.is_file() and cache:
     with shelve.open( str( cache_file ) ) as cache:
         data         = cache[ 'rotated_data' ]
         border       = cache[ 'border' ]
@@ -218,6 +220,7 @@ else:
             border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
         ]
     )
+
     def indicator( list_ ):
         if np.mean( list_ ) > 0.75 * tot_avg:
             return 0
@@ -226,7 +229,7 @@ else:
         list_ -= np.min( list_ )
         list_ /= np.max( list_ )
         positions = np.where( list_ > 0.5 )[0]
-        if len( positions ) < 10:
+        if len( positions ) < 100:
             return 2
         if len( positions ) > 400:
             return 3
@@ -238,9 +241,22 @@ else:
     indicators = np.array( [ indicator( data[ i , border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ] ].copy() ) for i in range( border[ 'y' ][ 'min' ] , border[ 'y' ][ 'max' ] , 1 ) ] )
 
     calibration_areas = utils.consecutive( np.where( indicators == 10 )[0] )
-    calibration_sizes = [ len( calibration_area ) for calibration_area in calibration_areas ]
 
-    y_calibrations = [ calibration_areas[ i ] for i in np.argsort( calibration_sizes ) ][ -2 : ]
+    calibration_areas = [
+        [ calibration_area for calibration_area in calibration_areas if calibration_area[-1] < ( border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ] ) / 2 ],
+        [ calibration_area for calibration_area in calibration_areas if calibration_area[ 0] > ( border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ] ) / 2 ],
+    ]
+
+    calibration_sizes = [
+        [ len( calibration_area ) for calibration_area in calibration_areas[0] ],
+        [ len( calibration_area ) for calibration_area in calibration_areas[1] ],
+    ]
+
+    y_calibrations = [
+            calibration_areas[0][ np.argmax( calibration_sizes[0] ) ],
+            calibration_areas[1][ np.argmax( calibration_sizes[1] ) ],
+    ]
+
     calibrations = {
         'top': {
             'x': {
@@ -317,3 +333,7 @@ fall = np.array( [
 temp = np.convolve( results[ : , 10000 ] , np.ones( 50 ) , 'same' )
 for i in range( len( fall ) - 1 ):
     temp[ fall[ i ] : fall[ i + 1 ] ] = np.mean( temp[ fall[ i ] : fall[ i + 1 ] ] )
+
+plt.plot( temp , label = 'flat' )
+plt.legend()
+plt.show()