linarphy/naroo_reader
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Update how to find start and stop position of a peak

Browse source
This commit is contained in:
linarphy 2023-08-30 01:49:51 +02:00
parent 08e3b850e5
commit b85c2d69d6
No known key found for this signature in database
GPG key ID: B914FF3FE69AA363
2 changed files with 50 additions and 17 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

36
classes/science/plate.py
View file

@ -271,16 +271,18 @@ class Plate:
Set spectrum area
"""
self.spectrum = Border()
list_ = convolve(
mean(
self.data[ self.border.slice() ],
axis = 1 ,
) ,
ones( 200 ),
'valid' ,
)
indexes = find_peak_low_high(
convolve(
mean(
self.data[ self.border.slice() ],
axis = 1 ,
) ,
ones( 200 ),
'valid' ,
),
list_ ,
( max( list_ ) + mean( list_ ) ) / 2,
)[0]
self.spectrum.y.min = indexes[0] + self.border.y.min + 100
@ -289,15 +291,17 @@ class Plate:
import matplotlib.pyplot as plt
plt.imshow( self.data[ self.border.slice() ] , aspect = 'auto' )
plt.show()
list_ = convolve(
mean(
self.data[ self.border.slice() ],
axis = 0 ,
) ,
ones( 200 ),
'valid' ,
)
indexes = find_peak_low_high(
convolve(
mean(
self.data[ self.border.slice() ],
axis = 0 ,
) ,
ones( 200 ),
'valid' ,
),
list_ ,
mean( list_ ) + max( list_ ) / 2,
)[0]
self.spectrum.x.min = indexes[0] + self.border.x.min + 100

31
function/utils.py
View file

@ -208,10 +208,39 @@ def near_value( list_ , value ):
index = np.append( index , np.where( list_ == value ) )
return np.round( np.sort( index ) ).astype( int ) # triage
def find_peak_low_high( list_ ):
def find_peak_low_high( list_ , value ):
"""
Return index of start and end of the biggest peak in a list
"""
indexes = near_value(
list_,
value,
)
list_ = np.gradient( list_ )
if list_[ indexes[0] ] < 0:
indexes.insert( 0 , 0 )
if list_[ indexes[0] ] < 0:
indexes.insert( 0 , 0 ) # start with a descent
if list_[ indexes[-1] ] > 0:
indexes.append( len( list_ ) - 1 )
if list_[ indexes[-1] ] > 0:
indexes.append( len( list_ ) - 1 ) # end with a rise
if len( indexes ) % 2 == 1:
raise Exception(
'number of peaks doesn\'t match what it should be'
)
rises = [
indexes[ i ] for i in range( 0 , len( indexes ) , 2 )
]
descents = [
indexes[ i ] for i in range( 1 , len( indexes ) , 2 )
]
np.where( list_[ : rises[0] ] < 0 )[-1]
np.where( list_[ descents[ i - 1 ] : rises[ i ] ] < 0 )[-1]
np.where( list_[ rises[ i - 1 ] : descents[ i ] ] < 0 )[-1]
first_rise , last_rise = indexes[0] , indexes[0]
first_descent , last_descent = indexes[1] , indexes[1]
min_diff = ( max( list_ ) - min( list_ ) ) * 0.001
max_high_value = max( list_ )
min_high_value = min( list_ )