Update intensity calibration
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parent
0f7e074770
commit
7579a3dde1
1 changed files with 182 additions and 122 deletions
278
spectrum.py
278
spectrum.py
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@ -3,6 +3,7 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.signal import medfilt , find_peaks
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from scipy.signal import convolve as sp_convolve
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from scipy.optimize import curve_fit
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import utils
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import sys
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@ -11,7 +12,8 @@ import pathlib
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from scipy.ndimage import rotate
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from astropy.io import fits
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cache , filename , output , calibration , intensity_calibration , verbose , no_cache = None , None , None , None , None , False, False
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cache , filename , output , calibration , intensity_calibration
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, verbose , no_cache = None , None , None , None , None , False, False
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if len( sys.argv ) < 2:
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raise Exception( 'spectrum.py: type \'spectrum.py -h\' for more information' )
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@ -143,7 +145,9 @@ if 'cache' in files and files[ 'cache' ].is_file() and not no_cache:
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with shelve.open( str( files[ 'cache' ] ) ) as cache:
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for key in [ 'data' , 'border' , 'calibrations' ]:
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if key not in cache:
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raise Exception( 'spectrum.py: missing data in cache file' )
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raise Exception(
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'spectrum.py: missing data in cache file'
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)
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data = cache[ 'data' ]
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border = cache[ 'border']
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spectrum = cache[ 'spectrum' ]
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@ -282,7 +286,11 @@ else:
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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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indexes = np.linspace(
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border[ 'x' ][ 'min' ],
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border[ 'x' ][ 'max' ],
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number , dtype = int ,
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)
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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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@ -382,12 +390,28 @@ else:
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factor += 1
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indexes = utils.near_value(
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list_ ,
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np.min( list_ ) + ( np.mean( list_ ) - np.min( list_ ) ) / factor,
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np.min(
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list_,
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) + (
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np.mean(
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list_,
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) - np.min(
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list_,
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)
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) / factor,
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)
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factor -= 1
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indexes = utils.near_value(
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list_ ,
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np.min( list_ ) + ( np.mean( list_ ) - np.min( list_ ) ) / factor,
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np.min(
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list_,
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) + (
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np.mean(
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list_,
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) - np.min(
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list_,
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)
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) / factor,
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) + border[ 'x' ][ 'min' ] + 100 # valid convolution only
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spectrum[ 'x' ] = {
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@ -418,7 +442,8 @@ else:
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list_ ,
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height = np.mean( list_ ) + ( 1 - np.mean( list_ ) ) / 2,
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)[0]
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number = 0.01 + abs( len( peaks ) - 90 ) # the lower the better
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number = 0.01 + abs( len( peaks ) - 90 ) # the lower the
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# better
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intensity = np.sum( list_[ peaks ] )
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return intensity / ( amplitude ** 2 * number )
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@ -439,22 +464,38 @@ else:
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'valid' ,
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)
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indicators /= np.max( indicators )
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calibration_areas = utils.consecutive( np.where( indicators > 1 / 1000 )[0] )
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calibration_areas = utils.consecutive(
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np.where(
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indicators > 1 / 1000,
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)[0],
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)
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calibration_areas = [
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[ calibration_area for calibration_area in calibration_areas if (
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[
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calibration_area for calibration_area in calibration_areas if (
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calibration_area[0] < (
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border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
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) / 2
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) ],
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[ calibration_area for calibration_area in calibration_areas if (
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)
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],
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[
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calibration_area for calibration_area in calibration_areas if (
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calibration_area[0] > (
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border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
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) / 2
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) ],
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)
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],
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]
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calibration_sizes = [
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[ len( calibration_area ) for calibration_area in calibration_areas[0] ],
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[ len( calibration_area ) for calibration_area in calibration_areas[1] ],
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[
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len(
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calibration_area
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) for calibration_area in calibration_areas[0]
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],
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[
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len(
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calibration_area
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) for calibration_area in calibration_areas[1]
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],
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]
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calibrations_y = [
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calibration_areas[0][
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@ -506,7 +547,9 @@ else:
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Calibration
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"""
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wavelengths = np.arange( spectrum[ 'x' ][ 'max' ] - spectrum[ 'x' ][ 'min' ] )
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wavelengths = np.arange(
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spectrum[ 'x' ][ 'max' ] - spectrum[ 'x' ][ 'min' ]
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)
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if calibration != None:
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if verbose:
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@ -530,19 +573,48 @@ if calibration != None:
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] ) * 1e-10
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start , end = 5000 , 18440
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polyval_before = np.polyfit( abciss[ : start ] , mean_data[ : start ] , 2 )
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polyval_middle = np.polyfit( abciss[ start : end ] , mean_data[ start : end ] , 2 )
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polyval_end = np.polyfit( abciss[ end : ] , mean_data[ end : ] , 2 )
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polyval_before = np.polyfit(
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abciss[ : start ] ,
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mean_data[ : start ],
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2 ,
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)
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polyval_middle = np.polyfit(
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abciss[ start : end ] ,
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mean_data[ start : end ],
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2 ,
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)
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polyval_end = np.polyfit(
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abciss[ end : ] ,
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mean_data[ end : ],
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2 ,
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)
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mean_data[ : start ] = mean_data[ : start ] - np.polyval( polyval_before , abciss[ : start ] )
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mean_data[ start : end ] = mean_data[ start : end ] - np.polyval( polyval_middle , abciss[ start : end ] )
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mean_data[ end : ] = mean_data[ end : ] - np.polyval( polyval_end , abciss[ end : ] )
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mean_data[ : start ] = mean_data[ : start ] - np.polyval(
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polyval_before ,
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abciss[ : start ],
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)
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mean_data[ start : end ] = mean_data[ start : end ] - np.polyval(
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polyval_middle ,
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abciss[ start : end ],
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)
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mean_data[ end : ] = mean_data[ end : ] - np.polyval(
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polyval_end ,
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abciss[ end : ],
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)
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mean_data_normalized = mean_data.copy() # normalization
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mean_data_normalized -= np.min( mean_data_normalized )
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mean_data_normalized /= np.max( mean_data_normalized )
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lines = [ np.mean( cons ) for cons in utils.consecutive( np.where( mean_data_normalized < 0.3 )[0] ) ]
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lines = [
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np.mean(
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cons,
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) for cons in utils.consecutive(
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np.where(
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mean_data_normalized < 0.3,
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)[0],
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)
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]
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lines = np.array( lines[ : len( ref ) - 1 ] ) # Balmer discontinuity
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ref = ref[ 1 : ] # start with H-beta
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@ -578,17 +650,50 @@ bias = {
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),
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}
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print( bias[ 'top' ] , bias[ 'down' ] )
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mean_bias = np.mean( [ bias[ 'top' ] , bias[ 'down' ] ] , axis = 0 ) / 2
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print( 'mean_bias spectrum: ' + str( mean_bias ) )
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mean_bias = sp_convolve(
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np.mean(
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[
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bias[ 'top' ],
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bias[ 'down' ],
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] ,
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axis = 0,
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) ,
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np.ones(
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(
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50,
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),
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) ,
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'same',
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) / 50
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plt.plot( bias[ 'top' ] , label = 'top' )
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plt.plot( bias[ 'down' ] , label = 'down' )
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plt.plot( mean_bias , label = 'mean' )
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plt.legend()
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plt.show()
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data[
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: ,
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spectrum[ 'x' ][ 'min' ] : spectrum[ 'x' ][ 'max' ]
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] = data[
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:,
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spectrum[ 'x' ][ 'min' ] : spectrum[ 'x' ][ 'max' ]
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] - mean_bias
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if verbose:
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print( 'bias substraction finished' )
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ETA_bias = np.load( 'ETA_bias.npy' )
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plt.plot( mean_bias , label = 'spectrum' )
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plt.plot( ETA_bias , label = 'ETA' )
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plt.legend()
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plt.show()
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mean_data = np.mean( data[
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spectrum[ 'y' ][ 'min' ] : spectrum[ 'y' ][ 'max' ],
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spectrum[ 'x' ][ 'min' ] : spectrum[ 'x' ][ 'max' ]
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] , axis = 0 ) - mean_bias
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] , axis = 0 )
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if intensity != None:
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if verbose:
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@ -597,120 +702,75 @@ if intensity != None:
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intensity_file = pathlib.Path( intensity )
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with shelve.open( str( intensity_file ) ) as storage:
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intensity_stairs = storage[ 'data' ]
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intensity_wavelengths = storage[ 'wavelength' ] * 1e-10
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ETA_stairs = storage[ 'data' ]
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ETA_wavelengths = storage[ 'wavelength' ] * 1e-10
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wavelengths = wavelengths[ # remove wavelengths outside range
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np.where(
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np.logical_and(
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wavelengths > np.min( intensity_wavelengths ),
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wavelengths < np.max( intensity_wavelengths ),
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wavelengths > np.min( ETA_wavelengths ),
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wavelengths < np.max( ETA_wavelengths ),
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),
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)
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]
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intensity_wavelengths = intensity_wavelengths[ # remove intensity_wavelengths outside range
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ETA_wavelengths = ETA_wavelengths[ # remove intensity_wavelengths
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# outside range
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np.where(
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np.logical_and(
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intensity_wavelengths > np.min( wavelengths ),
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intensity_wavelengths < np.max( wavelengths ),
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ETA_wavelengths > np.min( wavelengths ),
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ETA_wavelengths < np.max( wavelengths ),
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),
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)
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]
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if len( wavelengths ) == 0:
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raise Exception( 'spectrum.py: spectrum and ETA does not share any common wavelengths' )
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raise Exception(
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'spectrum.py: spectrum and ETA does not share any common' +
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'wavelengths'
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)
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step = 0.2 # depends of ETA source #TODO
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final_intensity = np.zeros( len( wavelengths ) )
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for index in range( len( wavelengths ) ):
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intensity_value = mean_data[ index ]
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intensity_wavelength = wavelengths[ index ]
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intensity_index = utils.near_value( # list of index corresponding to index for intensity_stairs
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intensity_wavelengths,
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intensity_wavelength ,
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wavelength = wavelengths[ index ]
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current_ETA_index = np.argmin(
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ETA_wavelengths - wavelength
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)
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ETA_wavelength = ETA_wavelengths[ current_ETA_index ]
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if len( intensity_index ) != 1: # too much or no intensity found near value
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final_intensity[ index ] = - 1
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continue
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intensity_index = intensity_index[0]
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indexes_stair_lower = np.where( # stairs lower than intensity value
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intensity_stairs[
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current_stair = ETA_stairs[
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: ,
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intensity_index,
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current_ETA_index,
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0
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] < intensity_value
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)[0]
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if len( indexes_stair_lower ) == 0: # intensity value outside ETA (below)
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final_intensity[ index ] = 0
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continue
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if len( indexes_stair_lower ) != intensity_stairs.shape[0] - indexes_stair_lower[0]: # stairs intensity does not decrease with index as it should
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# could indicate an artefact in ETA
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indexes_stair_lower = [
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int( np.mean(
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[
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intensity_stairs.shape[0] -
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indexes_stair_lower[0] ,
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len( indexes_stair_lower )
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]
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) )
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]
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indexes_stair_higher = np.where( # stairs higher than intensity value
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intensity_stairs[
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: ,
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intensity_index,
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0
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] > intensity_value
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abciss_intensity_curve = np.arange(
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(
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ETA_stairs.shape[0] - 1 # we want 11 value, no 12
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) * step ,
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- step / 2, # a little more than - step to remove negative
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- step ,
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)
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function_intensity_curve = lambda x , a , b : a * np.log( # log
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# because the curve is inverted (exp on the
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# other side )
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x
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) + b
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results = curve_fit(
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function_intensity_curve,
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current_stair ,
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abciss_intensity_curve ,
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)[0]
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if len( indexes_stair_higher ) == 0: # intensity value outside ETA (upper)
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final_intensity[ index ] = np.exp(
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step * intensity_stairs.shape[0]
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function_intensity_curve(
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mean_data[ index ],
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*results ,
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),
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)
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continue
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if len( indexes_stair_higher ) - 1 != indexes_stair_higher[-1]: # stairs intensity does not decrease with index as it should
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indexes_stair_higher = [
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int( np.mean(
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[
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indexes_stair_higher[ - 1 ] ,
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len( indexes_stair_higher ) - 1,
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],
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) ),
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]
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indexes_stair_higher = [
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indexes_stair_lower[ 0 ] - 1,
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]
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index_stair = {
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'higher': indexes_stair_higher[-1],
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'lower' : indexes_stair_lower[0] ,
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}
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if index_stair[ 'lower' ] - index_stair[ 'higher' ] != 1: # ETA curve should decrease
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raise Exception( 'spectrum.py: given intensity stairs (from ETA) are missformed' )
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stair_intensity = {
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'higher': intensity_stairs[ index_stair[ 'higher' ] , intensity_index , 0 ],
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'lower' : intensity_stairs[ index_stair[ 'lower' ] , intensity_index , 0 ],
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}
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index_polyval = np.polyfit( # fraction stair index from intensity value
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[ stair_intensity[ 'higher' ] , stair_intensity[ 'lower' ] ],
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[ index_stair[ 'higher' ] , index_stair[ 'lower' ] ],
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1 ,
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)
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true_intensity_value = ( intensity_stairs.shape[0] - np.polyval( index_polyval , intensity_value ) ) * step
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final_intensity[index] = np.exp( true_intensity_value )
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if final_intensity[ index ] > np.exp( 2.2 ):
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final_intensity[ index ] = np.exp( 2.2 )
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if verbose:
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print( 'intensity calibration finished' )
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