Add label finder part

- remove the label part to clean the data

ETA.py

@@ -1,4 +1,5 @@
 import numpy as np
+from scipy.optimize import curve_fit
 import utils
 import sys
 
@@ -40,12 +41,24 @@ small_data = utils.compress( data , 5 )
 
 points = utils.big_to_small( points , 5 )
 
+# size - 1
 points[ 1 ][ 1 ] -= 1
 points[ 3 ][ 0 ] -= 1
 
+# little shift to be inside the light
+points[ 2 ][ 1 ] += 3
+points[ 3 ][ 1 ] += 3
+
+"""
+fill data
+"""
+
 extremum = []
 for point in points:
-    taken_points = utils.small_to_big( utils.fill( small_data , point , 1000 ) , 5 )
+    taken_points = utils.small_to_big(
+        utils.fill( small_data , point , 1000 ),
+        5
+    )
     extremum.append( [
         np.min( taken_points[ : , 1 ] ),
         np.max( taken_points[ : , 1 ] ),
@@ -64,6 +77,80 @@ border = {
     },
 }
 
+"""
+label deletion
+"""
+
+mean_data = np.mean( data[
+    border['y']['min'] : border['y']['max'],
+    border['x']['min'] : border['x']['max']
+] , 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
+        )
+    )
+
+cov = np.array( [
+    np.sum( np.diag( part_result[i][1] ) ) for i in range( 2 )
+] )
+i   = np.argmax( cov ) # part where the label is
+
+derivee = np.convolve(
+        np.gradient( part_data[i] ),
+        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' ]
+
+border['x'][keys[i]] += ( - 1 ) ** i * ( start_label + end_label )
+
 import matplotlib.pyplot as plt
-plt.imshow( data[ border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ] , border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ] ] )
-plt.savefig( 'test.png' )
+plt.imshow( data[
+    border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
+    border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
+] )
+plt.savefig( 'asset/test.png' )