Add auto calibration based on peakless signal
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3d90b03a6d
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bf40d1db67
1 changed files with 109 additions and 71 deletions
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@ -13,20 +13,109 @@ 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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peaks_calib_top = np.array(
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utils.retrieve_peaks( mean_calib_top )
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).astype( int )
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peaks_reference = np.array(
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utils.retrieve_peaks(
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reference[1] ,
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window_size = 1 ,
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max_window_size = 1,
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)
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).astype( int )
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"""
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Signal without peaks
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"""
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peakless_calib_top = mean_calib_top.copy()
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for peak in peaks_calib_top:
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first = peak
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peak , old = first - 2 , first - 1
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while peakless_calib_top[ peak ] <= peakless_calib_top[ old ]:
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old = peak
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peak -= 1
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peakless_calib_top[ peak : first ] = peakless_calib_top[ peak ] * np.ones( first - peak )
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peak , old = first + 2 , first + 1
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while peakless_calib_top[ peak ] <= peakless_calib_top[ old ]:
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old = peak
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peak += 1
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peakless_calib_top[ first : peak ] = peakless_calib_top[ peak ] * np.ones( peak - first )
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peakless_calib_top = sig.medfilt( peakless_calib_top , 111 )
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"""
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It's possible to have an idea of the part the spectrum begins with the
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small large peak at the start of it, the peak at 3810 A should be inside
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it. The same goes for the area at the end.
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ONLY TRUE FOR THE CURRENT CALIBRATION (Hg)
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"""
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argmin = np.argmin(
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peakless_calib_top[ : len( peakless_calib_top ) // 2 ]
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)
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argmax = np.argmax( peakless_calib_top[ : argmin ] )
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peaks_inside = np.where(
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np.logical_and(
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argmax < peaks_calib_top,
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argmin > peaks_calib_top,
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)
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)[0]
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if len( peaks_inside ) < 1:
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raise Error( 'unknown plage, cannot autocalibrate' )
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first_peak_cal = peaks_inside[0]
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first_peak_ref = 3 # hard-coded
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"""
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The next peak after the minimum at the end of the spectrum should be
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5079 A.
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ONLY TRUE FOR THE CURRENT CALIBRATION (Hg)
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"""
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argmin_1 = np.argmin(
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peakless_calib_top[ len( peakless_calib_top ) // 2 : ]
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) + len( peakless_calib_top ) // 2
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peaks_inside = np.where(
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argmin_1 < peaks_calib_top
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)[0]
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if len( peaks_inside ) < 1:
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raise Error( 'unknown plage, cannot autocalibrate' )
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last_peak_cal = peaks_inside[0]
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last_peak_ref = 20 # hard-coded
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print( first_peak_cal , last_peak_cal )
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polyval = np.polyfit(
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peaks_calib_top[ first_peak_cal : last_peak_cal ],
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peaks_reference[ first_peak_ref : last_peak_ref ],
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1 ,
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) # We suppose there is as much calib peak than ref peak for now
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peaks_values = [ reference[ 1 , i ] for i in peaks_reference ]
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sorting = np.argsort( peaks_values )[ :: -1 ]
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wavelength = np.array( [
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reference[ 0 , i ] for i in np.array( [
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peaks_reference[ j ] for j in sorting
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] ).astype( int )
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] )
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polyval_wavelength = np.polyfit(
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peaks_reference,
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np.sort( wavelength ),
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1,
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)
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wavelength = np.polyval(
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polyval_wavelength,
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np.polyval(
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polyval,
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np.arange(
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0 ,
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len( mean_calib_top ),
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1 ,
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)
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)
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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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@ -35,73 +124,22 @@ manager = qt.FigureManager(
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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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manager.canvas.figure.gca().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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wavelength ,
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peakless_calib_top,
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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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wavelength[ argmin ] ,
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wavelength[ argmax ] ,
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wavelength[ argmin_1 ],
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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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np.min( peakless_calib_top ),
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np.max( peakless_calib_top ),
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color = 'red' ,
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)
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manager.show()
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