Fix and add function to utils.py
- Add point function - Add find_point function - Fix type check in some function
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1 changed files with 65 additions and 32 deletions
97
utils.py
97
utils.py
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@ -1,6 +1,5 @@
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from astropy.io.fits import open
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from numpy import compress as numpy_compress
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from numpy import ndarray
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import numpy as np
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def load( filename ):
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"""
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@ -8,7 +7,6 @@ def load( filename ):
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"""
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if not isinstance( filename , str ):
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raise ValueError( 'the filename must be a string, ' + type( filename ) + ' given' )
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hdul = open( filename )
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data = hdul[0].data
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hdul.close()
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@ -19,14 +17,14 @@ def compress( data , factor ):
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"""
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divide the size of the data by 2**factor
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"""
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if not isinstance( data , ndarray ) and not isinstance( data , list ):
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if not isinstance( data , np.ndarray ) and not isinstance( data , list ):
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raise ValueError( 'data must be a list, ' + type( data ) + ' given' )
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if not isinstance( factor , int ):
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raise ValueError( 'factor must be an integer, ' + type( factor ) + ' given' )
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for _ in range( factor ):
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data = numpy_compress( [ True , False ] * ( data.shape[1] // 2 ) , data , axis = 1 )
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data = numpy_compress( [ True , False ] * ( data.shape[0] // 2 ) , data , axis = 0 )
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data = np.compress( [ True , False ] * ( data.shape[1] // 2 ) , data , axis = 1 )
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data = np.compress( [ True , False ] * ( data.shape[0] // 2 ) , data , axis = 0 )
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return data
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@ -35,18 +33,16 @@ def big_to_small( indexes , factor ):
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convert a coordinate of a point in the non compressed data to the same coordinate
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in the compressed one (there is a loss of information here !)
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"""
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if not isinstance( indexes , int ) and not isinstance( indexes , list ) and not isinstance( indexes , tuple ):
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raise ValueError( 'indexes must be an integer or a tuple, ' + type( indexes ) + ' given' )
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if not isinstance( indexes , int ) and not isinstance( indexes , list ) and not isinstance( indexes , np.ndarray ):
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raise ValueError( 'indexes must be an integer or a list, ' + type( indexes ) + ' given' )
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if not isinstance( factor , int ):
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raise ValueError( 'factor mus be an integer' )
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if isinstance( indexes , int ):
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return big_to_small( [ indexes ] , factor )[0]
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if isinstance( indexes , tuple ):
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return tuple( big_to_small( list( indexes ) , factor ) )
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if isinstance( indexes , int ) or isinstance( indexes , np.ndarray ):
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return indexes // ( 2 ** factor )
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for i in range( len( indexes ) ):
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indexes[i] = indexes[i] // ( 2 ** factor )
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indexes[i] = big_to_small( indexes[i] , factor )
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return indexes
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@ -55,18 +51,18 @@ def small_to_big( indexes , factor ):
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convert a coordinate of a point n the compressed data to the same coordinate
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in the compressed one
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"""
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if not isinstance( indexes , int ) and not isinstance( indexes , list ) and not isinstance( indexes , tuple ):
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raise ValueError( 'indexes must be an integer or a tuple, ' + type( indexes ) + ' given' )
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if not isinstance( indexes , int ) and not isinstance( indexes , list ) and not isinstance( indexes , np.ndarray ):
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raise ValueError( 'indexes must be an integer or a list, ' + type( indexes ) + ' given' )
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if not isinstance( factor , int ):
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raise ValueError( 'factor mus be an integer' )
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if isinstance( indexes , int ):
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return small_to_big( [ indexes ] , factor )[0]
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if isinstance( indexes , tuple ):
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return tuple( small_to_big( list( indexes ) , factor ) )
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return int( indexes * 2 ** factor )
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if isinstance( indexes , np.ndarray ):
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return ( indexes * 2 ** factor ).astype( int )
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for i in range( len( indexes ) ):
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indexes[i] = int( indexes[i] * 2 ** factor )
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indexes[i] = small_to_big( indexes[i] , factor )
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return indexes
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@ -75,31 +71,31 @@ def check_side( data , point , tolerance ):
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give coordinates of all side point of the given point which have an
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intensity difference inferior than tolerance
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"""
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if not isinstance( data , ndarray ) and not isinstance( data , list ):
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if not isinstance( data , np.ndarray ) and not isinstance( data , list ):
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raise ValueError( 'data must be a list, ' + type( data ) + ' given' )
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if not isinstance( point , ndarray ) and not isinstance( point , tuple ) and not isinstance( point , list ):
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if not isinstance( point , np.ndarray ) and not isinstance( point , tuple ) and not isinstance( point , list ):
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raise ValueError( 'point must be a tuple, ' + type( point ) + ' given' )
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if not isinstance( tolerance , int ) and not isinstance( tolerance , float ):
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raise ValueError( 'tolerance must be a number, ' + type( tolerance ) + ' given' )
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positions , intensity = [] , data[ point ]
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if 0 <= position[0] < data.shape[0] - 1 and intensity - tolerance <= data[ position[0] + 1 , position[1] ] <= intensity + tolerance:
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positions.append( ( position[0] + 1 , position[1] ) )
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if 0 < position[0] < data.shape[0] and intensity - tolerance <= data[ position[0] - 1 , position[1] ] <= intensity + tolerance:
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positions.append( ( position[0] - 1 , position[1] ) )
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if 0 <= position[1] < data.shape[1] - 1 and intensity - tolerance <= data[ position[0] , position[1] + 1 ] <= intensity + tolerance:
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positions.append( ( position[0] , position[1] + 1 ) )
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if 0 < position[1] < data.shape[1] and intensity - tolerance <= data[ position[0] , position[1] - 1 ] <= intensity + tolerance:
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positions.append( ( position[0] , position[1] - 1 ) )
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positions , intensity = [] , data[ *point ]
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if 0 <= point[0] < data.shape[0] - 1 and intensity - tolerance <= data[ point[0] + 1 , point[1] ] <= intensity + tolerance:
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positions.append( [ point[0] + 1 , point[1] ] )
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if 0 < point[0] < data.shape[0] and intensity - tolerance <= data[ point[0] - 1 , point[1] ] <= intensity + tolerance:
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positions.append( [ point[0] - 1 , point[1] ] )
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if 0 <= point[1] < data.shape[1] - 1 and intensity - tolerance <= data[ point[0] , point[1] + 1 ] <= intensity + tolerance:
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positions.append( [ point[0] , point[1] + 1 ] )
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if 0 < point[1] < data.shape[1] and intensity - tolerance <= data[ point[0] , point[1] - 1 ] <= intensity + tolerance:
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positions.append( [ point[0] , point[1] - 1 ] )
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return positions
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def fill( data , point , tolerance , limit = 100000 ):
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"""
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give the coordinate of all points that fill the area with the given tolerance
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"""
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if not isinstance( data , ndarray ) and not isinstance( data , list ):
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if not isinstance( data , np.ndarray ) and not isinstance( data , list ):
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raise ValueError( 'data must be a list, ' + type( data ) + ' given' )
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if not isinstance( point , ndarray ) and not isinstance( point , tuple ) and not isinstance( point , list ):
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if not isinstance( point , np.ndarray ) and not isinstance( point , tuple ) and not isinstance( point , list ):
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raise ValueError( 'point must be a tuple, ' + type( point ) + ' given' )
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if not isinstance( tolerance , int ) and not isinstance( tolerance , float ):
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raise ValueError( 'tolerance must be a number, ' + type( tolerance ) + ' given' )
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@ -117,3 +113,40 @@ def fill( data , point , tolerance , limit = 100000 ):
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new_points.append( position )
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i += 1
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return np.array( taken_point )
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def point( index_1 , index_2 , axis = 'x' ):
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"""
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reorder coordinate
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"""
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if axis == 'x':
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return [ index_2 , index_1 ]
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return [ index_1 , index_2 ]
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def find_point( list_ , index , axis = 'x' , threshold = 0.5 ):
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"""
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find the index where to fill in a side
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"""
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mean = np.mean( list_ )
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ampl = np.max( list_ ) - np.min( list_ )
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if ampl < mean / 2:
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return [ point( index , 0 , axis ) ]
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else:
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points = []
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list_ = np.convolve( list_ , np.ones( 100 ) , 'same' )
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list_ -= np.min( list_ )
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list_ /= np.max( list_ )
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i , inside , size = 0 , False , 0
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while i < len( list_ ):
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if list_[ i ] > threshold and not inside:
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points.append( point( index , i , axis ) )
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inside = True
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size = 0
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elif list_[ i ] < threshold and inside:
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size += 1
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if size > 0.01 * len( list_ ): # low sensibility
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inside = False
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i += 1
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return points
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