Update processing order & fix
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3c0ece6688
commit
d1b02301aa
1 changed files with 53 additions and 35 deletions
88
ETA.py
88
ETA.py
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@ -89,10 +89,9 @@ if cache_file.is_file():
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for key in [ 'rotated_data' , 'border' , 'calibrations' ]:
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if key not in cache:
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raise Exception( 'ETA.py: missing data in cache_file' )
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data = cache[ 'rotated_data' ]
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data = cache[ 'data' ]
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border = cache[ 'border' ]
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calibrations = cache[ 'calibrations' ]
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stripes = cache[ 'stripes' ]
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else:
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"""
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find fill points
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@ -370,20 +369,42 @@ else:
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x_stripe = np.arange( 0 , size , 1 ).astype( int )[ good_x ]
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y_stripe = y_stripe[ good_x ]
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stripes = [ # list of polyval result for each stripe
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np.polyfit( x_stripe , y_stripe , 3 )
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stripe = np.polyfit( x_stripe , y_stripe , 3 )
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# First deformation
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list_ = data[
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calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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]
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y_diff = ( np.polyval(
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stripe,
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np.arange( 0 , border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ] , 1 )
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) ).astype( int )
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y_diff[ np.where( y_diff < 0 ) ] = 0
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results = np.zeros( ( list_.shape[0] + np.max( y_diff ) , list_.shape[1] ) )
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for i in range( list_.shape[1] ):
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results[ : , i ] = np.concatenate( (
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np.zeros(
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np.max( y_diff ) - y_diff[ i ]
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) ,
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list_[ : , i ] ,
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np.zeros( y_diff[i] ),
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) )
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data[
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calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ] + np.max( y_diff ),
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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] = results
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if not cache_file.exists():
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with shelve.open( str( cache_file ) ) as cache:
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cache[ 'rotated_data' ] = data
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cache[ 'border' ] = border
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cache[ 'calibrations'] = calibrations
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cache[ 'stripes' ] = stripes
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cache[ 'data' ] = data
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cache[ 'border' ] = border
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cache[ 'calibrations'] = calibrations
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size = border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
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size_x = size
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size_y = calibrations[ 'down' ][ 'y' ][ 'min' ] - calibrations[ 'top' ][ 'y' ][ 'max' ]
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size_x = border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
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size_y = calibrations[ 'down' ][ 'y' ][ 'min' ] - calibrations[ 'top' ][ 'y' ][ 'max' ]
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# Calibration
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@ -461,17 +482,21 @@ if calibration != None:
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return ( a * list_y + b ).astype( int )
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new_data = np.zeros( ( size_y , size_x - 199 ) )
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padding = diff_calc( size_x , size_y ) - size_x
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new_data = np.zeros( ( size_y , size_x - padding ) )
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list_y = np.arange( 0 , size_y , 1 )
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for x in range( 100 , size_x - 100 ):
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for x in range( size_x - padding ):
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diff = diff_calc( x , list_y )
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cons_diff , i = utils.same_value( diff ) , 0
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for list_same in cons_diff:
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new_data[ i : i + len( list_same ) , x - 100 ] = data[
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new_data[ i : i + len( list_same ) , x ] = data[
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calibrations[ 'top' ][ 'y' ][ 'max' ] + i : calibrations[ 'top' ][ 'y' ][ 'max' ] + i + len( list_same ),
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calibrations[ 'top' ][ 'x' ][ 'min' ] + list_same[0]
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]
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i += len( list_same )
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calibrations[ 'top' ][ 'x' ][ 'max' ] -= padding
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calibrations[ 'down' ][ 'x' ][ 'max' ] -= padding
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border[ 'x' ][ 'max' ] -= padding
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polyval_wavelength = np.polyfit( # x = 0 begins at the start of
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peaks_up , # calibrations[ 'top' ][ 'x' ][ 'min' ]
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@ -486,44 +511,37 @@ if calibration != None:
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1 ,
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)
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)
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else:
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padding = 0
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wavelength = np.arange( size_y )
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# First deformation
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list_ = data[
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calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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]
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y_diff = ( np.polyval( stripes[0] , np.arange( 0 , size , 1 ) ) ).astype( int )
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y_diff[ np.where( y_diff < 0 ) ] = 0
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results = np.zeros( ( list_.shape[0] + np.max( y_diff ) , list_.shape[1] ) )
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for i in range( list_.shape[1] ):
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results[ : , i ] = np.concatenate( ( np.zeros( np.max( y_diff ) - y_diff[ i ] ) , list_[ : , i ] , np.zeros( y_diff[i] ) ) )
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list_results = np.convolve(
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list_ = np.convolve(
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np.gradient(
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np.mean( results , axis = 1 ),
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np.mean( data , axis = 1 ),
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) ,
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np.ones( 50 ),
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'same' ,
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)
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fall = utils.consecutive( np.where( list_results < 0.03 * np.min( list_results ) )[0] )
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fall = utils.consecutive( np.where( list_ < 0.03 * np.min( list_ ) )[0] )
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fall = np.array( [
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np.argmax( list_results )
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np.argmax( list_ )
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] + [
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consecutive[0] + np.argmin(
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list_results[ consecutive[0] : consecutive[-1] ]
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list_[ consecutive[0] : consecutive[-1] ]
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) for consecutive in fall
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] ).astype( int )
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stairs = np.zeros_like( results )
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for x in range( size ):
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stairs[ : , x ] = results[ : , x ] # can be modified, but no used anymore so it's fine
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stairs = np.zeros( ( size_y , size_x ) )
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for x in range( size_x ):
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stairs[ : fall[0] , x ] = 0 # TODO: put the mean
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for i in range( len( fall ) - 1 ):
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stairs[ fall[ i ] : fall[ i + 1 ] , x ] = np.mean( stairs[ fall[ i ] : fall[ i + 1 ] ] )
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stairs[ fall[ i ] : fall[ i + 1 ] , x ] = np.mean( data[ fall[ i ] : fall[ i + 1 ] , x ] )
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stairs[ fall[ -1 ] : , x ] = 0
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np.imshow( stairs , aspect = 'auto' )
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np.show()
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if output == None:
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print( stairs )
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else:
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