Update how to find start and stop position of a peak

classes/science/plate.py

@@ -4,7 +4,7 @@ from scipy.signal import convolve, find_peaks
 from scipy.ndimage import rotate
 from classes.science.border import Border
 from classes.science.calibration_spectrum import CalibrationSpectrum
-from function.utils import find_point, fill, cut_biggest
+from function.utils import find_point, fill, find_peak_low_high
 from function.fit import linear
 
 from logging import getLogger
@@ -272,7 +272,7 @@ class Plate:
         """
         self.spectrum = Border()
 
-        indexes = cut_biggest(
+        indexes = find_peak_low_high(
             convolve(
                 mean(
                     self.data[ self.border.slice() ],
@@ -281,12 +281,15 @@ class Plate:
                 ones( 200 ),
                 'valid'    ,
             ),
-        )
+        )[0]
 
         self.spectrum.y.min = indexes[0] + self.border.y.min + 100
         self.spectrum.y.max = indexes[1] + self.border.y.min + 100
 
-        indexes = cut_biggest(
+        import matplotlib.pyplot as plt
+        plt.imshow( self.data[ self.border.slice() ] , aspect = 'auto' )
+        plt.show()
+        indexes = find_peak_low_high(
             convolve(
                 mean(
                     self.data[ self.border.slice() ],
@@ -295,7 +298,7 @@ class Plate:
                 ones( 200 ),
                 'valid'    ,
             ),
-        )
+        )[0]
 
         self.spectrum.x.min = indexes[0] + self.border.x.min + 100
         self.spectrum.x.max = indexes[1] + self.border.x.min + 100

function/utils.py

@@ -208,36 +208,34 @@ def near_value( list_ , value ):
     index = np.append( index , np.where( list_ == value ) )
     return np.round( np.sort( index ) ).astype( int ) # triage
 
-def cut_biggest( list_ ):
+def find_peak_low_high( list_ ):
     """
     Return index of start and end of the biggest peak in a list
     """
-    factor = 1
-    indexes = near_value(
-        list_          ,
-        np.max( list_ ),
-    )
-    if len( indexes ) > 2:
-        import matplotlib.pyplot as plt
-        plt.plot( list_ )
-        plt.show()
-        raise Exception( 'too much peak' )
-    while len( indexes ) < 2:
-        factor += 1
-        indexes = near_value(
-            list_     ,
-            np.min( list_ ) + (
-                np.max( list_ ) - np.mean( list_ )
-            ) / factor,
-        )
-    factor -= 1
-    indexes = near_value(
-        list_     ,
-        np.min( list_ ) + (
-            np.max( list_ ) - np.mean( list_ )
-        ) / factor,
-    )
-    if len( indexes ) == 2:
-        raise Exception( 'less than two pixel peak' )
+    min_diff = ( max( list_ ) - min( list_ ) ) * 0.001
+    max_high_value = max( list_ )
+    min_high_value = min( list_ )
+    max_low_value  = max( list_ )
+    min_low_value  = min( list_ )
+    while (
+        max_high_value - min_high_value > min_diff or
+        max_low_value  - min_low_value  > min_diff
+    ):
+        current_high = min_high_value + (
+            max_high_value - min_high_value
+        ) / 2
+        current_low  = min_low_value  + (
+            max_low_value  - min_low_value
+        ) / 2
+        indexes_high = near_value( list_ , current_high )
+        indexes_low  = near_value( list_ , current_low  )
+        if len( indexes_low ) != 2:
+            min_low_value  = current_low
+        else:
+            max_low_value  = current_low
+        if len( indexes_high ) != 2:
+            max_high_value = current_high
+        else:
+            min_high_value = current_high
 
-    return indexes
+    return indexes_low, indexes_high