Update polyfit to interpolation
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1 changed files with 89 additions and 33 deletions
122
ETA.py
122
ETA.py
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@ -8,6 +8,7 @@ import shelve
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from scipy.signal import convolve as sp_convolve
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from scipy.signal import find_peaks
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from scipy.ndimage import rotate
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import scipy.interpolate as interpolate
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cache , filename , output , calibration = None , None , None , None
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verbose , no_cache = False , False
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@ -466,7 +467,7 @@ else:
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if verbose:
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print( 'cache saved' )
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size_x = border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
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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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@ -478,12 +479,16 @@ if calibration != None:
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peaks_calib = np.loadtxt( calibration ) # sorted list
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peaks_calib = np.sort( peaks_calib )
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mean_up = np.mean( data[
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calibrations[ 'top' ][ 'y' ][ 'min' ] : calibrations[ 'top' ][ 'y' ][ 'max' ],
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calibrations[ 'top' ][ 'x' ][ 'min' ] : calibrations[ 'top' ][ 'x' ][ 'max' ]
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calibrations[ 'top' ][ 'y' ][ 'min' ]:
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calibrations[ 'top' ][ 'y' ][ 'max' ],
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calibrations[ 'top' ][ 'x' ][ 'min' ]:
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calibrations[ 'top' ][ 'x' ][ 'max' ]
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] , axis = 0 )
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mean_down = np.mean( data[
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calibrations[ 'down' ][ 'y' ][ 'min' ] : calibrations[ 'down' ][ 'y' ][ 'max' ],
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calibrations[ 'down' ][ 'x' ][ 'min' ] : calibrations[ 'down' ][ 'x' ][ 'max' ]
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calibrations[ 'down' ][ 'y' ][ 'min' ]:
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calibrations[ 'down' ][ 'y' ][ 'max' ],
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calibrations[ 'down' ][ 'x' ][ 'min' ]:
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calibrations[ 'down' ][ 'x' ][ 'max' ]
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] , axis = 0 )
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peaks_up = np.array(
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utils.retrieve_peaks(
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@ -559,7 +564,9 @@ if calibration != None:
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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 ] = data[
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calibrations[ 'top' ][ 'y' ][ 'max' ] + i : calibrations[ 'top' ][ 'y' ][ 'max' ] + i + len( list_same ),
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calibrations[ 'top' ][ 'y' ][ 'max' ] + i :
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calibrations[ 'top' ][ 'y' ][ 'max' ] + i
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+ 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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@ -572,7 +579,7 @@ if calibration != None:
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print( 'y to x pixel calibrated' )
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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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peaks_up , # calibrations[ 'top' ][ 'x' ][ 'min' ]
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peaks_calib,
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1 ,
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)
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@ -600,38 +607,88 @@ if verbose:
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"""
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remove artefact
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"""
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list_ = np.convolve(
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np.gradient(
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np.mean(
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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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axis = 1,
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),
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) ,
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np.ones( 50 ),
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'same' ,
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first_stripe = np.argmax(
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np.convolve(
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np.gradient(
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np.mean(
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data[
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calibrations[ 'top' ][ 'y' ][ 'max' ] :
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calibrations[ 'down' ][ 'y' ][ 'min' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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] ,
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axis = 1,
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),
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) ,
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np.ones( 50 ),
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'same' ,
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),
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)
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first_stripe = np.argmax( list_ )
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range_ = range(
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range_ = np.arange(
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calibrations[ 'top' ][ 'y' ][ 'max' ] + first_stripe,
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calibrations[ 'down' ][ 'y' ][ 'max' ]
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calibrations[ 'down' ][ 'y' ][ 'max' ] ,
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100 ,
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)
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for line in range_:
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abciss = range(
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convolve_point = 500
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abciss = np.arange(
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border[ 'x' ][ 'min' ],
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border[ 'x' ][ 'max' ],
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)
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list_ = data[
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line ,
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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list_ = np.convolve(
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data[
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line ,
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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] ,
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np.ones(
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convolve_point,
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) ,
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'valid', # Need to append value at extremities
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) / convolve_point # keep correct value
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list_ = np.concatenate(
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(
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data[
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line ,
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border[ 'x' ][ 'min' ] :
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border[ 'x' ][ 'min' ] + convolve_point //
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2 - 1
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] ,
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list_,
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data[
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line ,
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border[ 'x' ][ 'max' ]
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- convolve_point // 2 + convolve_point % 2 :
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border[ 'x' ][ 'max' ]
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] ,
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),
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)
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number_point = 10
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points_taken = abciss[
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convolve_point // 2 + convolve_point % 2 :
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- convolve_point // 2 + convolve_point % 2:
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( abciss.shape[0] - convolve_point + 1 ) // ( number_point - 1 )
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]
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polyfit_ = np.polyfit(
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abciss,
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list_ ,
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8 ,
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values_taken = list_[
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convolve_point // 2 + convolve_point % 2 :
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- convolve_point // 2 + convolve_point % 2:
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( abciss.shape[0] - convolve_point + 1 ) // ( number_point - 1 )
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]
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sigma = np.std( list_ )
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interpolation = interpolate.PchipInterpolator( # Seems the best
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# (Have to check to be sure) -> TODO find reference
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# value to determine which interpolation is better
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points_taken,
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values_taken,
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)
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wrong_points = np.where(
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np.abs(
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interpolation(
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abciss,
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) - list_ ,
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) > sigma
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)[0]
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list_[ wrong_points ] = interpolation( # no border point
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abciss[ wrong_points ],
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)
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import matplotlib.pyplot as plt
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@ -641,9 +698,8 @@ for line in range_:
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)
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plt.plot(
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abciss,
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np.polyval(
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polyfit_,
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abciss ,
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interpolation(
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abciss,
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) ,
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)
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plt.show()
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