Add intensity step correction

- Finished curve correction
- Add intensity correction for each x

ETA.py

@@ -1,6 +1,9 @@
 import numpy as np
+import matplotlib.pyplot as plt
 import utils
 import sys
+import pathlib
+import shelve
 from scipy.signal import convolve as sp_convolve
 from scipy.signal import find_peaks
 from scipy.ndimage import rotate
@@ -9,6 +12,14 @@ 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():
+    with shelve.open( str( cache_file ) ) as cache:
+        data         = cache[ 'rotated_data' ]
+        border       = cache[ 'border' ]
+        calibrations = cache[ 'calibrations' ]
+else:
     """
     find fill points
     """
@@ -224,7 +235,7 @@ def indicator( list_ ):
             return 4
         return 10
 
-indicators = np.array( [ indicator( data[ i , border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ] ] ) for i in range( border[ 'y' ][ 'min' ] , border[ 'y' ][ 'max' ] , 1 ) ] )
+    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 ]
@@ -253,36 +264,73 @@ calibrations = {
         },
     }
 
+    with shelve.open( str( cache_file ) ) as cache:
+        cache[ 'rotated_data' ] = data
+        cache[ 'border' ]       = border
+        cache[ 'calibrations']  = calibrations
+
 """
 stripes curves detection
 """
-
 list_ = data[
     calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ],
     border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
 ].copy()
 list_ -= np.min( list_ , axis = 0 )
 list_ /= np.max( list_ , axis = 0 )
+size     = list_.shape[1]
 
-size  = border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
-x_stripe = np.arange( border[ 'x' ][ 'min' ] + 1 * size // 4 , border[ 'x' ][ 'min' ] + 3 * size // 4 , 1 ).astype( int )
-y_stripe = np.array( [
-    np.where(
-        list_[ : , x ] > 0.8
-    )[0][0] for x in x_stripe
-] )
+y_stripe = np.argmax( list_ , axis = 0 )
+good_x   = np.where( y_stripe < 2 * np.mean( y_stripe ) )[0]
+x_stripe = np.arange( 0 , size , 1 ).astype( int )[ good_x ]
+y_stripe = y_stripe[ good_x ]
 
 stripes = [ # list of polyval result for each stripe
-    np.polyfit( x_stripe , y_stripe , 2 )
+    np.polyfit( x_stripe , y_stripe , 3 )
 ]
 
 # First deformation
-y_diff  = np.polyval( stripes[0] , np.arange( 0 , size , 1 ) ).astype( int )
-results = np.zeros( ( list_.shape[1] , list_.shape[0] - np.max( y_diff ) ) )
-for i in range( list_.shape[1] ):
-    results[i] = list_[ y_diff[ i ] : list_.shape[0] + y_diff[ i ] - np.max( y_diff ) , i ]
-results = results.transpose()
 
-import matplotlib.pyplot as plt
-plt.imshow( results )
-plt.savefig( 'asset/deformation.png' )
+y_diff  = ( np.polyval( stripes[0] , np.arange( 0 , size , 1 ) ) ).astype( int )
+y_diff[ np.where( y_diff < 0 ) ] = 0
+results = np.zeros( ( list_.shape[0] + np.max( y_diff ) , list_.shape[1] ) )
+for i in range( list_.shape[1] ):
+    results[ : , i ] = np.concatenate( ( np.zeros( np.max( y_diff ) - y_diff[ i ] ) , list_[ : , i ] , np.zeros( y_diff[i] ) ) )
+
+list_results = np.convolve(
+    np.gradient(
+        np.mean( results , axis = 1 ),
+    )            ,
+    np.ones( 50 ),
+    'same'       ,
+)
+
+fall = utils.consecutive( np.where( list_results < - 0.02 )[0] )
+fall = np.array( [
+        np.argmax( list_results )
+    ] + [
+    consecutive[0] + np.argmin(
+        list_results[ consecutive[0] : consecutive[-1] ]
+    ) for consecutive in fall
+] ).astype( int )
+
+"""
+plt.imshow( results , aspect = 'auto' )
+plt.hlines( fall , 0 , size )
+plt.show()
+"""
+
+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 )
+plt.plot(
+    np.convolve(
+        results[ : , 10000 ],
+        np.ones( 50 )       ,
+        'same'              ,
+    ),
+)
+plt.vlines( fall , 0 , 50 , colors = 'red' )
+plt.show()