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Add first strategy to calibrate wavelength

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It's don't seems good enough
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linarphy 2023-05-17 14:31:01 +02:00
parent bc493177bd
commit 0aa8a98cc7
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1 changed files with 27 additions and 10 deletions
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33
ETA.py
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@ -387,21 +387,38 @@ if calibration != None:
calib_peaks = np.loadtxt( calibration )
mean_calib_up = np.mean( data[
calibrations[ 'top' ][ 'y' ][ 'min' ] : calibrations[ 'top' ][ 'y' ][ 'max' ],
border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
calibrations[ 'top' ][ 'x' ][ 'min' ] : calibrations[ 'top' ][ 'x' ][ 'max' ]
] , axis = 0 )
mean_calib_up -= np.min( mean_calib_up )
mean_calib_up /= np.max( mean_calib_up )
peaks_up = find_peaks( mean_calib_up , height = 0.6 )[0] + border[ 'x' ][ 'min' ]
peaks_up = find_peaks( mean_calib_up , height = 0.5 )[0] + calibrations[ 'top' ][ 'x' ][ 'min' ]
diff = np.array( [ np.inf ] )
polyval = np.polyfit( peaks_up[ : len( calib_peaks ) ] , calib_peaks[ : len( peaks_up ) ] , 1 )
if len( peaks_up ) != len( calib_peaks ):
print( calib_peaks , peaks_up )
print( len( calib_peaks ) , len( peaks_up ) )
plt.plot( mean_calib_up )
while np.max( diff ) > 1000: # we do not have the exact same number of peak (and the same peaks) in calibration model
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 )
polyfull_calib = np.polyfit( peaks_up[ : len( calib_peaks_new ) ] , calib_peaks_new[ : len( peaks_up ) ] , 1 , full = True )
if polyfull_up[ 1 ][ 0 ] < polyfull_calib[ 1 ][ 0 ]: # which one is a better fit ?
peaks_up = peaks_up_new
polyval = polyfull_up[ 0 ]
else:
calib_peaks = calib_peaks_new
polyval = polyfull_calib[ 0 ]
x = np.arange( calibrations[ 'top' ][ 'x' ][ 'min' ] , calibrations[ 'top' ][ 'x' ][ 'max' ] , 1 )
x = np.polyval( polyval , x )
plt.margins( x = 0 )
plt.plot( x , mean_calib_up )
plt.show()
exit()
polyval = np.polyfit( peaks_up , calib_peaks , 1 )
print( polyval )
# First deformation