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Fix and add function to utils.py

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- Add point function
- Add find_point function
- Fix type check in some function
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linarphy 2023-05-05 17:01:07 +02:00
parent dd526c77ef
commit c26b4782ed
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1 changed files with 65 additions and 32 deletions
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97
utils.py
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@ -1,6 +1,5 @@
from astropy.io.fits import open from astropy.io.fits import open
from numpy import compress as numpy_compress import numpy as np
from numpy import ndarray
def load( filename ): def load( filename ):
""" """
@ -8,7 +7,6 @@ def load( filename ):
""" """
if not isinstance( filename , str ): if not isinstance( filename , str ):
raise ValueError( 'the filename must be a string, ' + type( filename ) + ' given' ) raise ValueError( 'the filename must be a string, ' + type( filename ) + ' given' )
hdul = open( filename ) hdul = open( filename )
data = hdul[0].data data = hdul[0].data
hdul.close() hdul.close()
@ -19,14 +17,14 @@ def compress( data , factor ):
""" """
divide the size of the data by 2**factor divide the size of the data by 2**factor
""" """
if not isinstance( data , ndarray ) and not isinstance( data , list ): if not isinstance( data , np.ndarray ) and not isinstance( data , list ):
raise ValueError( 'data must be a list, ' + type( data ) + ' given' ) raise ValueError( 'data must be a list, ' + type( data ) + ' given' )
if not isinstance( factor , int ): if not isinstance( factor , int ):
raise ValueError( 'factor must be an integer, ' + type( factor ) + ' given' ) raise ValueError( 'factor must be an integer, ' + type( factor ) + ' given' )
for _ in range( factor ): for _ in range( factor ):
data = numpy_compress( [ True , False ] * ( data.shape[1] // 2 ) , data , axis = 1 ) data = np.compress( [ True , False ] * ( data.shape[1] // 2 ) , data , axis = 1 )
data = numpy_compress( [ True , False ] * ( data.shape[0] // 2 ) , data , axis = 0 ) data = np.compress( [ True , False ] * ( data.shape[0] // 2 ) , data , axis = 0 )
return data return data
@ -35,18 +33,16 @@ def big_to_small( indexes , factor ):
convert a coordinate of a point in the non compressed data to the same coordinate convert a coordinate of a point in the non compressed data to the same coordinate
in the compressed one (there is a loss of information here !) in the compressed one (there is a loss of information here !)
""" """
if not isinstance( indexes , int ) and not isinstance( indexes , list ) and not isinstance( indexes , tuple ): if not isinstance( indexes , int ) and not isinstance( indexes , list ) and not isinstance( indexes , np.ndarray ):
raise ValueError( 'indexes must be an integer or a tuple, ' + type( indexes ) + ' given' ) raise ValueError( 'indexes must be an integer or a list, ' + type( indexes ) + ' given' )
if not isinstance( factor , int ): if not isinstance( factor , int ):
raise ValueError( 'factor mus be an integer' ) raise ValueError( 'factor mus be an integer' )
if isinstance( indexes , int ): if isinstance( indexes , int ) or isinstance( indexes , np.ndarray ):
return big_to_small( [ indexes ] , factor )[0] return indexes // ( 2 ** factor )
if isinstance( indexes , tuple ):
return tuple( big_to_small( list( indexes ) , factor ) )
for i in range( len( indexes ) ): for i in range( len( indexes ) ):
indexes[i] = indexes[i] // ( 2 ** factor ) indexes[i] = big_to_small( indexes[i] , factor )
return indexes return indexes
@ -55,18 +51,18 @@ def small_to_big( indexes , factor ):
convert a coordinate of a point n the compressed data to the same coordinate convert a coordinate of a point n the compressed data to the same coordinate
in the compressed one in the compressed one
""" """
if not isinstance( indexes , int ) and not isinstance( indexes , list ) and not isinstance( indexes , tuple ): if not isinstance( indexes , int ) and not isinstance( indexes , list ) and not isinstance( indexes , np.ndarray ):
raise ValueError( 'indexes must be an integer or a tuple, ' + type( indexes ) + ' given' ) raise ValueError( 'indexes must be an integer or a list, ' + type( indexes ) + ' given' )
if not isinstance( factor , int ): if not isinstance( factor , int ):
raise ValueError( 'factor mus be an integer' ) raise ValueError( 'factor mus be an integer' )
if isinstance( indexes , int ): if isinstance( indexes , int ):
return small_to_big( [ indexes ] , factor )[0] return int( indexes * 2 ** factor )
if isinstance( indexes , tuple ): if isinstance( indexes , np.ndarray ):
return tuple( small_to_big( list( indexes ) , factor ) ) return ( indexes * 2 ** factor ).astype( int )
for i in range( len( indexes ) ): for i in range( len( indexes ) ):
indexes[i] = int( indexes[i] * 2 ** factor ) indexes[i] = small_to_big( indexes[i] , factor )
return indexes return indexes
@ -75,31 +71,31 @@ def check_side( data , point , tolerance ):
give coordinates of all side point of the given point which have an give coordinates of all side point of the given point which have an
intensity difference inferior than tolerance intensity difference inferior than tolerance
""" """
if not isinstance( data , ndarray ) and not isinstance( data , list ): if not isinstance( data , np.ndarray ) and not isinstance( data , list ):
raise ValueError( 'data must be a list, ' + type( data ) + ' given' ) raise ValueError( 'data must be a list, ' + type( data ) + ' given' )
if not isinstance( point , ndarray ) and not isinstance( point , tuple ) and not isinstance( point , list ): if not isinstance( point , np.ndarray ) and not isinstance( point , tuple ) and not isinstance( point , list ):
raise ValueError( 'point must be a tuple, ' + type( point ) + ' given' ) raise ValueError( 'point must be a tuple, ' + type( point ) + ' given' )
if not isinstance( tolerance , int ) and not isinstance( tolerance , float ): if not isinstance( tolerance , int ) and not isinstance( tolerance , float ):
raise ValueError( 'tolerance must be a number, ' + type( tolerance ) + ' given' ) raise ValueError( 'tolerance must be a number, ' + type( tolerance ) + ' given' )
positions , intensity = [] , data[ point ] positions , intensity = [] , data[ *point ]
if 0 <= position[0] < data.shape[0] - 1 and intensity - tolerance <= data[ position[0] + 1 , position[1] ] <= intensity + tolerance: if 0 <= point[0] < data.shape[0] - 1 and intensity - tolerance <= data[ point[0] + 1 , point[1] ] <= intensity + tolerance:
positions.append( ( position[0] + 1 , position[1] ) ) positions.append( [ point[0] + 1 , point[1] ] )
if 0 < position[0] < data.shape[0] and intensity - tolerance <= data[ position[0] - 1 , position[1] ] <= intensity + tolerance: if 0 < point[0] < data.shape[0] and intensity - tolerance <= data[ point[0] - 1 , point[1] ] <= intensity + tolerance:
positions.append( ( position[0] - 1 , position[1] ) ) positions.append( [ point[0] - 1 , point[1] ] )
if 0 <= position[1] < data.shape[1] - 1 and intensity - tolerance <= data[ position[0] , position[1] + 1 ] <= intensity + tolerance: if 0 <= point[1] < data.shape[1] - 1 and intensity - tolerance <= data[ point[0] , point[1] + 1 ] <= intensity + tolerance:
positions.append( ( position[0] , position[1] + 1 ) ) positions.append( [ point[0] , point[1] + 1 ] )
if 0 < position[1] < data.shape[1] and intensity - tolerance <= data[ position[0] , position[1] - 1 ] <= intensity + tolerance: if 0 < point[1] < data.shape[1] and intensity - tolerance <= data[ point[0] , point[1] - 1 ] <= intensity + tolerance:
positions.append( ( position[0] , position[1] - 1 ) ) positions.append( [ point[0] , point[1] - 1 ] )
return positions return positions
def fill( data , point , tolerance , limit = 100000 ): def fill( data , point , tolerance , limit = 100000 ):
""" """
give the coordinate of all points that fill the area with the given tolerance give the coordinate of all points that fill the area with the given tolerance
""" """
if not isinstance( data , ndarray ) and not isinstance( data , list ): if not isinstance( data , np.ndarray ) and not isinstance( data , list ):
raise ValueError( 'data must be a list, ' + type( data ) + ' given' ) raise ValueError( 'data must be a list, ' + type( data ) + ' given' )
if not isinstance( point , ndarray ) and not isinstance( point , tuple ) and not isinstance( point , list ): if not isinstance( point , np.ndarray ) and not isinstance( point , tuple ) and not isinstance( point , list ):
raise ValueError( 'point must be a tuple, ' + type( point ) + ' given' ) raise ValueError( 'point must be a tuple, ' + type( point ) + ' given' )
if not isinstance( tolerance , int ) and not isinstance( tolerance , float ): if not isinstance( tolerance , int ) and not isinstance( tolerance , float ):
raise ValueError( 'tolerance must be a number, ' + type( tolerance ) + ' given' ) raise ValueError( 'tolerance must be a number, ' + type( tolerance ) + ' given' )
@ -117,3 +113,40 @@ def fill( data , point , tolerance , limit = 100000 ):
new_points.append( position ) new_points.append( position )
i += 1 i += 1
return np.array( taken_point ) return np.array( taken_point )
def point( index_1 , index_2 , axis = 'x' ):
"""
reorder coordinate
"""
if axis == 'x':
return [ index_2 , index_1 ]
return [ index_1 , index_2 ]
def find_point( list_ , index , axis = 'x' , threshold = 0.5 ):
"""
find the index where to fill in a side
"""
mean = np.mean( list_ )
ampl = np.max( list_ ) - np.min( list_ )
if ampl < mean / 2:
return [ point( index , 0 , axis ) ]
else:
points = []
list_ = np.convolve( list_ , np.ones( 100 ) , 'same' )
list_ -= np.min( list_ )
list_ /= np.max( list_ )
i , inside , size = 0 , False , 0
while i < len( list_ ):
if list_[ i ] > threshold and not inside:
points.append( point( index , i , axis ) )
inside = True
size = 0
elif list_[ i ] < threshold and inside:
size += 1
if size > 0.01 * len( list_ ): # low sensibility
inside = False
i += 1
return points