linarphy
a240bf83e8
- Remove old label detection code and dependancy to scipy - Add quick label detection method without dependancies
149 lines
3.6 KiB
Python
149 lines
3.6 KiB
Python
import numpy as np
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import utils
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import sys
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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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"""
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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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import matplotlib.pyplot as plt
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plt.imshow( 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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plt.savefig( 'asset/test.png' )
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