linarphy
484a75ac0f
- Add imshow to have a better view of what works and what does not - Update plot to get "true" intensity by deleting convolution
357 lines
11 KiB
Python
357 lines
11 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import utils
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import sys
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import pathlib
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import shelve
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from scipy.signal import convolve as sp_convolve
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from scipy.signal import find_peaks
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from scipy.ndimage import rotate
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cache = True
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if len( sys.argv ) < 2:
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raise Exception( 'this command must have a filename of an ETA fits as an argument' )
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data = utils.load( sys.argv[1] )
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cache_file = pathlib.Path( 'asset/points_' + sys.argv[1].split( '/' )[-1][:-5] + '.pag' )
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if cache_file.is_file() and cache:
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with shelve.open( str( cache_file ) ) as cache:
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data = cache[ 'rotated_data' ]
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border = cache[ 'border' ]
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calibrations = cache[ 'calibrations' ]
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else:
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"""
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find fill points
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"""
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points = []
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points += utils.find_point( data[ : , 0 ] , 0 ) # x_min
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points += utils.find_point(
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data[ : , data.shape[1] - 1 ],
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data.shape[1] - 1 ,
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) # x_max
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index_min = 0
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while data.shape[0] - 1 > index_min:
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index_min += 1
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if len( utils.find_point( data[ index_min , : ] , index_min , 'y' ) ) == 3:
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break
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points.append(
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utils.find_point( data[ index_min , : ] , index_min , 'y' )[1]
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) # y_min
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index_max = data.shape[0] - 1
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while index_min < index_max:
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index_max -= 1
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if len( utils.find_point( data[ index_max , : ] , index_max , 'y' ) ) == 3:
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break
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points.append(
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utils.find_point( data[ index_max , : ] , index_max , 'y' )[1]
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) # y_max
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small_data = utils.compress( data , 5 )
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points = utils.big_to_small( points , 5 )
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# size - 1
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points[ 1 ][ 1 ] -= 1
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points[ 3 ][ 0 ] -= 1
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# little shift to be inside the light
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points[ 2 ][ 1 ] += 3
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points[ 3 ][ 1 ] += 3
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"""
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fill data
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"""
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extremum = []
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for point in points:
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if point[0] < points[2][0]:
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point[0] = points[2][0]
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if point[1] < points[0][1]:
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point[1] = points[0][1]
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taken_points = utils.small_to_big(
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np.array( [
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points[2][0],
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points[0][1],
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] ) + utils.fill(
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small_data[
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points[2][0] : points[3][0] + 1,
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points[0][1] : points[1][1] + 1
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],
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[
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point[0] - points[2][0],
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point[1] - points[0][1],
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],
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1000
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),
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5
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)
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extremum.append( [
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np.min( taken_points[ : , 1 ] ),
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np.max( taken_points[ : , 1 ] ),
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np.min( taken_points[ : , 0 ] ),
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np.max( taken_points[ : , 0 ] ),
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] )
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border = {
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'x': {
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'min': points[0][1] + extremum[0][1] + 1,
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'max': points[0][1] + extremum[1][0] ,
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},
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'y': {
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'min': points[2][0] + extremum[2][3] + 1,
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'max': points[2][0] + extremum[3][2] ,
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},
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}
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"""
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label deletion
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"""
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mean_data = np.convolve(
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np.gradient(
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np.mean(
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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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axis = 0
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)
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),
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np.ones(
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int( 0.01 * (
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border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
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) )
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),
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'same'
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)
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mean_data -= np.min( mean_data )
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mean_data /= np.max( mean_data )
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top = utils.consecutive( np.where( mean_data > 0.75 )[0] )
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down = utils.consecutive( np.where( mean_data < 0.25 )[0] )
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size_top = [ len( list_ ) for list_ in top ]
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size_down = [ len( list_ ) for list_ in down ]
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label_x = {
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'min': border[ 'x' ][ 'min' ] + top[ np.argmax( size_top ) ][0] ,
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'max': border[ 'x' ][ 'min' ] + down[ np.argmax( size_down ) ][-1]
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}
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if label_x[ 'min' ] < data.shape[1] // 2:
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if label_x[ 'max' ] < data.shape[1] // 2:
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border[ 'x' ][ 'min' ] = label_x[ 'max' ]
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else:
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raise Exception( 'the label seems to be in the middle of the picture' )
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elif label_x[ 'max' ] > data.shape[1] // 2:
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border[ 'x' ][ 'max' ] = label_x[ 'min' ]
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else:
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raise Exception( 'for an unkown reason, label_x[ \'min\' ] > label_x[ \'max\' ]' )
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"""
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Rotation
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"""
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index = border[ 'x' ][ 'min' ]
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gradient = np.gradient(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
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border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
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) // 2,
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index
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]
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)
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while np.max( gradient ) - np.min( gradient ) > 5500:
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index += 1
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gradient = np.gradient(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
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border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
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) // 2,
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index
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]
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)
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positions = np.argmax(
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sp_convolve(
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np.gradient(
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data[
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border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
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border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
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) // 2 ,
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border[ 'x' ][ 'min' ] : index
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] ,
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axis = 0
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) ,
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np.ones( ( 100 , 1 ) ),
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'valid'
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) ,
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axis = 0
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)
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list_ = np.arange( 0 , index - border[ 'x' ][ 'min' ] , 1 )
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polyval = np.polyfit( list_ , positions , 1 )
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angle = np.arctan( polyval[0] )
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data = rotate( data , angle * ( 180 / np.pi ) ) # utils.rotate does not keep intensity absolute value ? TODO
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diff_y = int( np.tan( angle ) * ( border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ] ) )
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border[ 'y' ][ 'min' ] -= diff_y
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border[ 'y' ][ 'max' ] -= diff_y
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"""
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Calibration
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"""
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tot_avg = np.mean(
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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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def indicator( list_ ):
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if np.mean( list_ ) > 0.75 * tot_avg:
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return 0
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if np.mean( list_ ) < 0.25 * tot_avg:
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return 1
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list_ -= np.min( list_ )
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list_ /= np.max( list_ )
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positions = np.where( list_ > 0.5 )[0]
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if len( positions ) < 100:
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return 2
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if len( positions ) > 400:
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return 3
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distance = np.mean( positions[ 1 : ] - positions[ : -1 ] )
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if distance < 10:
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return 4
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return 10
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indicators = np.array( [ indicator( data[ i , border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ] ].copy() ) for i in range( border[ 'y' ][ 'min' ] , border[ 'y' ][ 'max' ] , 1 ) ] )
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calibration_areas = utils.consecutive( np.where( indicators == 10 )[0] )
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calibration_areas = [
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[ calibration_area for calibration_area in calibration_areas if calibration_area[-1] < ( border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ] ) / 2 ],
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[ calibration_area for calibration_area in calibration_areas if calibration_area[ 0] > ( border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ] ) / 2 ],
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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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y_calibrations = [
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calibration_areas[0][ np.argmax( calibration_sizes[0] ) ],
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calibration_areas[1][ np.argmax( calibration_sizes[1] ) ],
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]
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calibrations = {
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'top': {
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'x': {
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'min': border['x']['min'],
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'max': border['x']['max'],
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},
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'y': {
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'min': border['y']['min'] + y_calibrations[0][ 0],
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'max': border['y']['min'] + y_calibrations[0][-1],
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},
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},
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'down': {
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'x': {
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'min': border['x']['min'],
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'max': border['x']['max'],
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},
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'y': {
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'min': border['y']['min'] + y_calibrations[1][ 0],
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'max': border['y']['min'] + y_calibrations[1][-1],
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},
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},
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}
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with shelve.open( str( cache_file ) ) as cache:
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cache[ 'rotated_data' ] = data
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cache[ 'border' ] = border
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cache[ 'calibrations'] = calibrations
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"""
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stripes curves detection
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"""
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list_ = data[
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calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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].copy()
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list_ -= np.min( list_ , axis = 0 )
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list_ /= np.max( list_ , axis = 0 )
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size = list_.shape[1]
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y_stripe = np.argmax( list_ , axis = 0 )
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good_x = np.where( y_stripe < 2 * np.mean( y_stripe ) )[0]
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x_stripe = np.arange( 0 , size , 1 ).astype( int )[ good_x ]
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y_stripe = y_stripe[ good_x ]
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stripes = [ # list of polyval result for each stripe
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np.polyfit( x_stripe , y_stripe , 3 )
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]
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# First deformation
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y_diff = ( np.polyval( stripes[0] , np.arange( 0 , size , 1 ) ) ).astype( int )
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y_diff[ np.where( y_diff < 0 ) ] = 0
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results = np.zeros( ( list_.shape[0] + np.max( y_diff ) , list_.shape[1] ) )
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for i in range( list_.shape[1] ):
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results[ : , i ] = np.concatenate( ( np.zeros( np.max( y_diff ) - y_diff[ i ] ) , list_[ : , i ] , np.zeros( y_diff[i] ) ) )
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list_results = np.convolve(
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np.gradient(
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np.mean( results , axis = 1 ),
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) ,
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np.ones( 50 ),
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'same' ,
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)
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fall = utils.consecutive( np.where( list_results < - 0.02 )[0] )
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fall = np.array( [
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np.argmax( list_results )
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] + [
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consecutive[0] + np.argmin(
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list_results[ consecutive[0] : consecutive[-1] ]
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) for consecutive in fall
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] ).astype( int )
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mask = np.zeros_like( data )
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mask[
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calibrations[ 'top' ][ 'y' ][ 'min' ] : calibrations[ 'top' ][ 'y' ][ 'max' ],
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calibrations[ 'top' ][ 'x' ][ 'min' ] : calibrations[ 'top' ][ 'x' ][ 'max' ]
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] = -1
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mask[
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calibrations[ 'down' ][ 'y' ][ 'min' ] : calibrations[ 'down' ][ 'y' ][ 'max' ],
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calibrations[ 'down' ][ 'x' ][ 'min' ] : calibrations[ 'down' ][ 'x' ][ 'max' ]
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] = -1
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mask[
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calibrations[ 'top' ][ 'y' ][ 'max' ] : calibrations[ 'down' ][ 'y' ][ 'min' ],
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border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
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] = 1
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plt.imshow( data )
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plt.imshow( mask , alpha = 0.5 )
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plt.show()
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temp = results[ : , 10000 ].copy()
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for i in range( len( fall ) - 1 ):
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temp[ fall[ i ] : fall[ i + 1 ] ] = np.mean( temp[ fall[ i ] : fall[ i + 1 ] ] )
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plt.plot( results[ : , 10000 ] , label = 'raw' )
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plt.plot( temp , label = 'flat' )
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plt.legend()
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plt.show()
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