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Update to use wavelength_calibration

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linarphy 2023-05-24 15:10:07 +02:00
parent f3e62bfca3
commit 4e54a1b433
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1 changed files with 44 additions and 25 deletions
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69
ETA.py
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@ -384,39 +384,58 @@ else:
# Calibration # Calibration
if calibration != None: if calibration != None:
calib_peaks = np.loadtxt( calibration ) import wavelength_calibration as wave_calib
mean_calib_up = np.mean( data[ peaks_calib = np.loadtxt( calibration ) # sorted list
peaks_calib = np.sort( peaks_calib )
mean_up = np.mean( data[
calibrations[ 'top' ][ 'y' ][ 'min' ] : calibrations[ 'top' ][ 'y' ][ 'max' ], calibrations[ 'top' ][ 'y' ][ 'min' ] : calibrations[ 'top' ][ 'y' ][ 'max' ],
calibrations[ 'top' ][ 'x' ][ 'min' ] : calibrations[ 'top' ][ 'x' ][ 'max' ] calibrations[ 'top' ][ 'x' ][ 'min' ] : calibrations[ 'top' ][ 'x' ][ 'max' ]
] , axis = 0 ) ] , axis = 0 )
mean_calib_up -= np.min( mean_calib_up ) peaks_up = np.array(
mean_calib_up /= np.max( mean_calib_up ) utils.retrieve_peaks(
mean_up ,
window_size = 1 ,
max_window_size = 1,
)
).astype( int )
peaks_up = find_peaks( mean_calib_up , height = 0.5 )[0] + calibrations[ 'top' ][ 'x' ][ 'min' ] peakless_up = wave_calib.remove_peaks( mean_up , peaks_up )
diff = np.array( [ np.inf ] ) calib = { # hard-coded for now
polyval = np.polyfit( peaks_up[ : len( calib_peaks ) ] , calib_peaks[ : len( peaks_up ) ] , 1 ) 'first': 3 ,
'last': 20,
}
first , last = wave_calib.get_extremities( peakless_up , peaks_up , mean_up )
up = {
'first': first,
'last' : last ,
}
peaks_up = peaks_up[ up[ 'first' ] : up[ 'last' ] + 1 ]
peaks_calib = peaks_calib[ calib[ 'first' ] : calib[ 'last' ] + 1 ]
while np.max( diff ) > 1000: # we do not have the exact same number of peak (and the same peaks) in calibration model peaks_up = wave_calib.only_keep_calib( peaks_up , peaks_calib )
diff = abs( np.polyval( polyval , peaks_up[ : len( calib_peaks ) ] ) - calib_peaks[ : len( peaks_up ) ] )
point_to_delete = np.argmax( diff ) # get the "worst" point
peaks_up_new = np.delete( peaks_up , [ point_to_delete ] ) # remove from data value ( other peak detected )
calib_peaks_new = np.delete( calib_peaks , [ point_to_delete ] ) # remove from model ( peak not detected )
polyfull_up = np.polyfit( peaks_up_new[ : len( calib_peaks ) ] , calib_peaks[ : len( peaks_up_new ) ] , 1 , full = True ) polyval_wavelength = np.polyfit( # x = 0 begins at the start of
polyfull_calib = np.polyfit( peaks_up[ : len( calib_peaks_new ) ] , calib_peaks_new[ : len( peaks_up ) ] , 1 , full = True ) peaks_up , # calibartions[ 'top' ][ 'x' ][ 'min' ]
peaks_calib,
1 ,
)
if polyfull_up[ 1 ][ 0 ] < polyfull_calib[ 1 ][ 0 ]: # which one is a better fit ? wavelength = np.polyval(
peaks_up = peaks_up_new polyval_wavelength,
polyval = polyfull_up[ 0 ] np.arange(
else: 0 ,
calib_peaks = calib_peaks_new len( mean_up ),
polyval = polyfull_calib[ 0 ] 1 ,
)
)
x = np.arange( calibrations[ 'top' ][ 'x' ][ 'min' ] , calibrations[ 'top' ][ 'x' ][ 'max' ] , 1 ) plt.plot(
x = np.polyval( polyval , x ) peaks_up,
peaks_calib - np.polyval(
plt.margins( x = 0 ) polyval_wavelength,
plt.plot( x , mean_calib_up ) peaks_up ,
) ,
)
plt.show() plt.show()
exit() exit()