diff --git a/ETA.py b/ETA.py
index 3274a10..697d47e 100644
--- a/ETA.py
+++ b/ETA.py
@@ -89,10 +89,9 @@ if cache_file.is_file():
         for key in [ 'rotated_data' , 'border' , 'calibrations' ]:
             if key not in cache:
                 raise Exception( 'ETA.py: missing data in cache_file' )
-        data         = cache[ 'rotated_data' ]
+        data         = cache[     'data'     ]
         border       = cache[    'border'    ]
         calibrations = cache[ 'calibrations' ]
-        stripes      = cache[   'stripes'    ]
 else:
     """
     find fill points
@@ -370,20 +369,42 @@ else:
     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 , 3 )
+    stripe = np.polyfit( x_stripe , y_stripe , 3 )
+
+    # First deformation
+
+    list_ = data[
+        calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ],
+        border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
     ]
+    y_diff  = ( np.polyval(
+        stripe,
+        np.arange( 0 , border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ] , 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] ),
+        ) )
+
+    data[
+        calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ] + np.max( y_diff ),
+        border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
+    ] = results
 
     if not cache_file.exists():
         with shelve.open( str( cache_file ) ) as cache:
-            cache[ 'rotated_data' ] = data
-            cache[ 'border' ]       = border
-            cache[ 'calibrations']  = calibrations
-            cache[ 'stripes' ]      = stripes
+            cache[ 'data'   ]      = data
+            cache[ 'border' ]      = border
+            cache[ 'calibrations'] = calibrations
 
-size = border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
-size_x = size
-size_y =  calibrations[ 'down' ][ 'y' ][ 'min' ] - calibrations[ 'top' ][ 'y' ][ 'max' ]
+size_x = border[ 'x' ][ 'max' ]                 - border[ 'x' ][ 'min' ]
+size_y = calibrations[ 'down' ][ 'y' ][ 'min' ] - calibrations[ 'top' ][ 'y' ][ 'max' ]
 
 # Calibration
 
@@ -461,17 +482,21 @@ if calibration != None:
 
         return ( a * list_y + b ).astype( int )
 
-    new_data = np.zeros( ( size_y , size_x - 199 ) )
+    padding = diff_calc( size_x , size_y ) - size_x
+    new_data = np.zeros( ( size_y , size_x - padding ) )
     list_y    = np.arange( 0 , size_y , 1 )
-    for x in range( 100 , size_x - 100 ):
+    for x in range( size_x - padding ):
         diff = diff_calc( x , list_y )
         cons_diff , i = utils.same_value( diff ) , 0
         for list_same in cons_diff:
-            new_data[ i : i + len( list_same ) , x - 100 ] = data[
+            new_data[ i : i + len( list_same ) , x ] = data[
                 calibrations[ 'top' ][ 'y' ][ 'max' ] + i : calibrations[ 'top' ][ 'y' ][ 'max' ] + i + len( list_same ),
                 calibrations[ 'top' ][ 'x' ][ 'min' ] + list_same[0]
             ]
             i += len( list_same )
+    calibrations[  'top' ][ 'x' ][ 'max' ] -= padding
+    calibrations[ 'down' ][ 'x' ][ 'max' ] -= padding
+    border[ 'x' ][ 'max' ]                 -= padding
 
     polyval_wavelength = np.polyfit( # x = 0 begins at the start of
         peaks_up   ,                 # calibrations[ 'top' ][ 'x' ][ 'min' ]
@@ -486,44 +511,37 @@ if calibration != None:
             1             ,
         )
     )
+else:
+    padding    = 0
+    wavelength = np.arange( size_y )
 
-# First deformation
-
-list_ = data[
-    calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ],
-    border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
-]
-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(
+list_ = np.convolve(
     np.gradient(
-        np.mean( results , axis = 1 ),
+        np.mean( data , axis = 1 ),
     )            ,
     np.ones( 50 ),
     'same'       ,
 )
 
-fall = utils.consecutive( np.where( list_results < 0.03 * np.min( list_results ) )[0] )
+fall = utils.consecutive( np.where( list_ < 0.03 * np.min( list_ ) )[0] )
 fall = np.array( [
-        np.argmax( list_results )
+        np.argmax( list_ )
     ] + [
     consecutive[0] + np.argmin(
-        list_results[ consecutive[0] : consecutive[-1] ]
+        list_[ consecutive[0] : consecutive[-1] ]
     ) for consecutive in fall
 ] ).astype( int )
 
-stairs = np.zeros_like( results )
-for x in range( size ):
-    stairs[ : , x ] = results[ : , x ] # can be modified, but no used anymore so it's fine
+stairs = np.zeros( ( size_y , size_x ) )
+for x in range( size_x ):
     stairs[ : fall[0] , x ] = 0 # TODO: put the mean
     for i in range( len( fall ) - 1 ):
-        stairs[ fall[ i ] : fall[ i + 1 ] , x ] = np.mean( stairs[ fall[ i ] : fall[ i + 1 ] ] )
+        stairs[ fall[ i ] : fall[ i + 1 ] , x ] = np.mean( data[ fall[ i ] : fall[ i + 1 ] , x ] )
     stairs[ fall[ -1 ] : , x ] = 0
 
+np.imshow( stairs , aspect = 'auto' )
+np.show()
+
 if output == None:
     print( stairs )
 else: