Add rotation step for spectrum.py
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ba920b7e79
commit
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1 changed files with 100 additions and 5 deletions
105
spectrum.py
105
spectrum.py
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@ -1,10 +1,12 @@
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import numpy as np
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import scipy.signal as signal
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from scipy.signal import medfilt , find_peaks
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from scipy.optimize import curve_fit
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import utils
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import utils
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import sys
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import sys
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import shelve
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import shelve
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import pathlib
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import pathlib
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from scipy.ndimage import rotate
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cache , filename , output , calibration , verbose , no_cache = '' , None , None , None , False, False
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cache , filename , output , calibration , verbose , no_cache = '' , None , None , None , False, False
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if len( sys.argv ) < 2:
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if len( sys.argv ) < 2:
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@ -218,9 +220,76 @@ else:
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}
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}
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if verbose:
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if verbose:
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print( 'first zoom finished' )
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print( 'first zoom finished' )
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print( 'starting spectrum isolation' )
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print( 'starting rotation' )
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print( 'starting y border detection' )
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print( 'retrieving current angle' )
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"""
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Rotation
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"""
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gauss = lambda x , sigma , mu , a , b : a * (
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1 / ( sigma * np.sqrt( 2 * np.pi ) )
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) * np.exp(
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- ( x - mu ) ** 2 / ( 2 * sigma ** 2 )
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) + b
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guess_params = [
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1 ,
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( border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ] ) / 2,
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np.mean( data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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] ) ,
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np.mean( data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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] ) ,
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]
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number = 1000
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position_peaks = np.zeros( number )
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indexes = np.linspace( border[ 'x' ][ 'min' ] , border[ 'x' ][ 'max' ] , number , dtype = int )
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for i in range( number ):
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x = np.arange(
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border[ 'y' ][ 'min' ],
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border[ 'y' ][ 'max' ],
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1 ,
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)
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y = data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
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indexes[ i ]
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]
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try:
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position_peaks[ i ] = curve_fit(
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gauss ,
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x ,
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y ,
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guess_params,
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)[0][1]
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except:
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position_peaks[ i ] = 0
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position_peaks = medfilt(
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position_peaks[ np.where( position_peaks != 0 ) ],
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11 ,
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)
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abciss = np.arange(
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len( position_peaks )
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)[ np.where( position_peaks ) ]
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polyval = np.polyfit( abciss , position_peaks , 1 )
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angle = np.arctan( polyval[0] )
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if verbose:
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print( 'current angle retrieved: ' + str( angle ) )
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print( 'starting image rotation' )
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data = rotate( data , angle * ( 180 / np.pi ) ) # utils.rotate does not keep intenisty absolute value TODO
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if verbose:
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print( 'image rotation finished' )
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print( 'rotation finished' )
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print( 'starting spectrum isolation' )
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print( 'starting y border detection' )
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"""
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"""
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Spectrum y
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Spectrum y
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@ -310,7 +379,7 @@ else:
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list_ /= np.max( list_ ) # max 1
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list_ /= np.max( list_ ) # max 1
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amplitude = np.mean( list_ ) # the lower the better
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amplitude = np.mean( list_ ) # the lower the better
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peaks = signal.find_peaks(
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peaks = find_peaks(
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list_ ,
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list_ ,
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height = np.mean( list_ ) + ( 1 - np.mean( list_ ) ) / 2,
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height = np.mean( list_ ) + ( 1 - np.mean( list_ ) ) / 2,
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)[0]
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)[0]
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@ -405,6 +474,32 @@ Calibration
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if calibration != None:
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if calibration != None:
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if verbose:
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if verbose:
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print( 'starting calibration' )
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print( 'starting calibration' )
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mean_up = np.mean( data[
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calibrations[ 'top' ][ 'y' ][ 'min' ] : calibrations[ 'top' ][ 'y' ][ 'max' ],
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calibrations[ 'top' ][ 'x' ][ 'min' ] : claibrations[ 'top' ][ 'x' ][ 'max' ]
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] , axis = 0 )
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mean_up = np.mean( data[
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calibrations[ 'down' ][ 'y' ][ 'min' ] : calibrations[ 'down' ][ 'y' ][ 'max' ],
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calibrations[ 'down' ][ 'x' ][ 'min' ] : claibrations[ 'down' ][ 'x' ][ 'max' ]
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] , axis = 0 )
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peaks_up = np.array(
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utils.retrieve_peaks(
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mean_up ,
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window_size = 1 ,
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max_window_size = 1,
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) ,
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dtype = int,
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)
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peaks_down = np.array(
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utils.retrieve_peaks(
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mean_down ,
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window_size = 1 ,
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max_window_size = 1,
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) ,
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dtype = int,
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)
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if verbose:
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if verbose:
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print( 'calibration finished' )
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print( 'calibration finished' )
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