linarphy
3d90b03a6d
Try to start from the same point for each spectrum by comparing 'ground' part without peaks
108 lines
2.9 KiB
Python
108 lines
2.9 KiB
Python
import numpy as np
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import scipy.signal as sig
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import matplotlib.backends.backend_qtagg as qt
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import matplotlib.figure as fig
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import utils
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rect = [0.125,0.11,0.775,0.77]
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calib_top = np.load( 'asset/calib_top.npy' )
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calib_down = np.load( 'asset/calib_down.npy' )
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reference = np.load( 'asset/reference.npy' )
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mean_calib_top = np.mean( calib_top , axis = 0 )
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mean_calib_down = np.mean( calib_down , axis = 0 )
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peaks_calib_top = utils.retrieve_peaks( mean_calib_top )
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peaks_reference = utils.retrieve_peaks( reference[1] , window_size = 1 , max_window_size = 1 )
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polyval = np.polyfit( peaks_calib_top , peaks_reference[ 2 : ] , 1 )
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peaks_values = [ reference[ 1 , i ] for i in np.array( peaks_reference ).astype( int ) ]
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sorting = np.argsort( peaks_values )[ :: -1 ]
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wavelength = [ reference[ 0 , i ] for i in np.array( [ peaks_reference[ j ] for j in sorting ] ).astype( int ) ]
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polyval_wavelength = np.polyfit( peaks_reference , np.sort( wavelength ) , 1 )
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wavelength = np.polyval( polyval_wavelength , np.polyval( polyval , np.arange( 0 , len( mean_calib_top ) , 1 ) ) )
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"""
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manager = qt.FigureManager(
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qt.FigureCanvas(
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fig.Figure(
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figsize = ( 10 , 5 )
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),
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),
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0,
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)
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manager.canvas.figure.add_subplot( 2 , 1 , 1 , xlim = ( 3800 , 4000 ) , xmargin = 0 ).plot(
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wavelength ,
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mean_calib_top,
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)
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manager.canvas.figure.axes[0].set_title( 'raw data' )
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manager.canvas.figure.add_subplot( 2 , 1 , 2 , xlim = ( 3800 , 4000 ) , xmargin = 0 ).plot(
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reference[0],
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reference[1],
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)
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manager.canvas.figure.axes[1].set_title( 'reference' )
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manager.show()
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manager.start_main_loop()
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"""
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for peak in np.array( peaks_calib_top ).astype( int ):
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first = peak
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peak , old = first - 2 , first - 1
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while mean_calib_top[ peak ] <= mean_calib_top[ old ]:
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old = peak
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peak -= 1
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mean_calib_top[ peak : first ] = mean_calib_top[ peak ] * np.ones( first - peak )
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peak , old = first + 2 , first + 1
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while mean_calib_top[ peak ] <= mean_calib_top[ old ]:
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old = peak
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peak += 1
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mean_calib_top[ first : peak ] = mean_calib_top[ peak ] * np.ones( peak - first )
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manager = qt.FigureManager(
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qt.FigureCanvas(
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fig.Figure(
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figsize = ( 10 , 5 )
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),
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),
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0,
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)
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manager.canvas.figure.gca().plot(
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mean_calib_top
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)
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manager.canvas.figure.gca().plot(
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sig.medfilt( mean_calib_top , 71 )
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)
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manager.show()
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manager.start_main_loop()
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manager = qt.FigureManager(
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qt.FigureCanvas(
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fig.Figure(),
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),
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1,
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)
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signal_calib = sig.convolve(
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sig.medfilt( mean_calib_top , 71 ),
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np.ones( 50 ),
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'same'
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)
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manager.canvas.figure.gca().plot(
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signal
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)
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manager.canvas.figure.gca().vlines(
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[
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np.argmin( signal[ 25 : -25 ] )
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] ,
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np.min( signal[ 25 : -25 ] ),
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np.max( signal[ 25 : -25 ] ),
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color = 'red' ,
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)
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manager.show()
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manager.start_main_loop()
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