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Add rotation step for spectrum.py

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linarphy 2023-06-16 15:05:41 +02:00
parent ba920b7e79
commit a6781588bb
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1 changed files with 100 additions and 5 deletions
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99
spectrum.py
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@ -1,10 +1,12 @@
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as signal
from scipy.signal import medfilt , find_peaks
from scipy.optimize import curve_fit
import utils
import sys
import shelve
import pathlib
from scipy.ndimage import rotate
cache , filename , output , calibration , verbose , no_cache = '' , None , None , None , False, False
if len( sys.argv ) < 2:
@ -219,6 +221,73 @@ else:
if verbose:
print( 'first zoom finished' )
print( 'starting rotation' )
print( 'retrieving current angle' )
"""
Rotation
"""
gauss = lambda x , sigma , mu , a , b : a * (
1 / ( sigma * np.sqrt( 2 * np.pi ) )
) * np.exp(
- ( x - mu ) ** 2 / ( 2 * sigma ** 2 )
) + b
guess_params = [
1 ,
( border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ] ) / 2,
np.mean( data[
border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
] ) ,
np.mean( data[
border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
] ) ,
]
number = 1000
position_peaks = np.zeros( number )
indexes = np.linspace( border[ 'x' ][ 'min' ] , border[ 'x' ][ 'max' ] , number , dtype = int )
for i in range( number ):
x = np.arange(
border[ 'y' ][ 'min' ],
border[ 'y' ][ 'max' ],
1 ,
)
y = data[
border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
indexes[ i ]
]
try:
position_peaks[ i ] = curve_fit(
gauss ,
x ,
y ,
guess_params,
)[0][1]
except:
position_peaks[ i ] = 0
position_peaks = medfilt(
position_peaks[ np.where( position_peaks != 0 ) ],
11 ,
)
abciss = np.arange(
len( position_peaks )
)[ np.where( position_peaks ) ]
polyval = np.polyfit( abciss , position_peaks , 1 )
angle = np.arctan( polyval[0] )
if verbose:
print( 'current angle retrieved: ' + str( angle ) )
print( 'starting image rotation' )
data = rotate( data , angle * ( 180 / np.pi ) ) # utils.rotate does not keep intenisty absolute value TODO
if verbose:
print( 'image rotation finished' )
print( 'rotation finished' )
print( 'starting spectrum isolation' )
print( 'starting y border detection' )
@ -310,7 +379,7 @@ else:
list_ /= np.max( list_ ) # max 1
amplitude = np.mean( list_ ) # the lower the better
peaks = signal.find_peaks(
peaks = find_peaks(
list_ ,
height = np.mean( list_ ) + ( 1 - np.mean( list_ ) ) / 2,
)[0]
@ -405,6 +474,32 @@ Calibration
if calibration != None:
if verbose:
print( 'starting calibration' )
mean_up = np.mean( data[
calibrations[ 'top' ][ 'y' ][ 'min' ] : calibrations[ 'top' ][ 'y' ][ 'max' ],
calibrations[ 'top' ][ 'x' ][ 'min' ] : claibrations[ 'top' ][ 'x' ][ 'max' ]
] , axis = 0 )
mean_up = np.mean( data[
calibrations[ 'down' ][ 'y' ][ 'min' ] : calibrations[ 'down' ][ 'y' ][ 'max' ],
calibrations[ 'down' ][ 'x' ][ 'min' ] : claibrations[ 'down' ][ 'x' ][ 'max' ]
] , axis = 0 )
peaks_up = np.array(
utils.retrieve_peaks(
mean_up ,
window_size = 1 ,
max_window_size = 1,
) ,
dtype = int,
)
peaks_down = np.array(
utils.retrieve_peaks(
mean_down ,
window_size = 1 ,
max_window_size = 1,
) ,
dtype = int,
)
if verbose:
print( 'calibration finished' )