Update label detection method

- Remove old label detection code and dependancy to scipy
- Add quick label detection method without dependancies

ETA.py

@@ -1,8 +1,9 @@
 import numpy as np
-from scipy.optimize import curve_fit
 import utils
 import sys
 
+if len( sys.argv ) < 2:
+    raise Exception( 'this command must have a filename of an ETA fits as an argument' )
 data = utils.load( sys.argv[1] )
 
 """
@@ -55,8 +56,25 @@ fill data
 
 extremum = []
 for point in points:
+    if point[0] < points[2][0]:
+        point[0] = points[2][0]
+    if point[1] < points[0][1]:
+        point[1] = points[0][1]
     taken_points = utils.small_to_big(
-        utils.fill( small_data , point , 1000 ),
+        np.array( [
+            points[2][0],
+            points[0][1],
+        ] ) + utils.fill(
+            small_data[
+                points[2][0] : points[3][0] + 1,
+                points[0][1] : points[1][1] + 1
+            ],
+            [
+                point[0] - points[2][0],
+                point[1] - points[0][1],
+            ],
+            1000
+        ),
         5
     )
     extremum.append( [
@@ -68,12 +86,12 @@ for point in points:
 
 border = {
     'x': {
-        'min': extremum[0][1] + 1,
+        'min': points[0][1] + extremum[0][1] + 1,
-        'max': extremum[1][0]    ,
+        'max': points[0][1] + extremum[1][0]    ,
     },
     'y': {
-        'min': extremum[2][3] + 1,
+        'min': points[2][0] + extremum[2][3] + 1,
-        'max': extremum[3][2]    ,
+        'max': points[2][0] + extremum[3][2]    ,
     },
 }
 
@@ -81,72 +99,47 @@ border = {
 label deletion
 """
 
-mean_data = np.mean( data[
+mean_data = np.convolve(
+    np.gradient(
+        np.mean(
+            data[
                 border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
                 border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
-] , axis = 0 )
+            ],
-
+            axis = 0
-gauss        = lambda x , sigma , mu , a , b : a * (
-    1 / sigma * np.sqrt(
-        2 * np.pi
-    ) * np.exp(
-        - ( x - mu ) ** 2 / ( 2 * sigma ** 2 )
-    )
-) + b
-abciss       = np.arange(
-        border['x']['min'],
-        border['x']['max'],
-        1
-)
-guess_params = [
-    1                                                                    ,
-    border['x']['min'] + ( border['x']['max'] - border['x']['min'] ) // 2,
-    np.max( mean_data )                                                  ,
-    np.min( mean_data )                                                  ,
-]
-
-first_estimate = curve_fit(
-    gauss       ,
-    abciss      ,
-    mean_data   ,
-    guess_params
-)[0]
-
-part_data   = [
-    mean_data[ : mean_data.shape[0] // 2 ],
-    mean_data[ mean_data.shape[0] // 2 : ]
-]
-part_abciss = [
-    abciss[ : abciss.shape[0] // 2 ],
-    abciss[ abciss.shape[0] // 2 : ]
-]
-part_result = []
-for i in range( 2 ):
-    part_result.append(
-        curve_fit(
-            gauss         ,
-            part_abciss[i],
-            part_data[i]  ,
-            first_estimate
         )
+    ),
+    np.ones(
+        int( 0.01 * (
+            border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ]
+        ) )
+    ),
+    'same'
 )
 
-cov = np.array( [
+mean_data -= np.min( mean_data )
-    np.sum( np.diag( part_result[i][1] ) ) for i in range( 2 )
+mean_data /= np.max( mean_data )
-] )
-i   = np.argmax( cov ) # part where the label is
 
-derivee = np.convolve(
+top  = utils.consecutive( np.where( mean_data > 0.75 )[0] )
-        np.gradient( part_data[i] ),
+down = utils.consecutive( np.where( mean_data < 0.25 )[0] )
-        np.ones( part_data[i].shape[0] // 100 ),
-        'same',
-)
-start_label = np.argmax( derivee )
-end_label   = np.argmin( derivee[ start_label :: ( - 1 ) ** i ] )
 
-keys = [ 'min' , 'max' ]
+size_top  = [ len( list_ ) for list_ in top  ]
+size_down = [ len( list_ ) for list_ in down ]
 
-border['x'][keys[i]] += ( - 1 ) ** i * ( start_label + end_label )
+label_x = {
+    'min': border[ 'x' ][ 'min' ] + top[ np.argmax( size_top ) ][0]   ,
+    'max': border[ 'x' ][ 'min' ] + down[ np.argmax( size_down ) ][-1]
+}
+
+if label_x[ 'min' ] < data.shape[1] // 2:
+    if label_x[ 'max' ] < data.shape[1] // 2:
+        border[ 'x' ][ 'min' ] = label_x[ 'max' ]
+    else:
+        raise Exception( 'the label seems to be in the middle of the picture' )
+elif label_x[ 'max' ] > data.shape[1] // 2:
+    border[ 'x' ][ 'max' ] = label_x[ 'min' ]
+else:
+    raise Exception( 'for an unkown reason, label_x[ \'min\' ] > label_x[ \'max\' ]' )
 
 import matplotlib.pyplot as plt
 plt.imshow( data[