Fix & Clean up

ETA.py

@@ -211,12 +211,12 @@ else:
 
     border = {
         'x': {
-            'min': points[0][1] + extremum[0][1] + 1,
+            'min': points[0][1] + extremum[0][1] + 25,
-            'max': points[0][1] + extremum[1][0]    ,
+            'max': points[0][1] + extremum[1][0] - 25,
         },
         'y': {
-            'min': points[2][0] + extremum[2][3] + 1,
+            'min': points[2][0] + extremum[2][3] + 25,
-            'max': points[2][0] + extremum[3][2]    ,
+            'max': points[2][0] + extremum[3][2] - 25,
         },
     }
 
@@ -264,11 +264,11 @@ else:
         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' )
+            raise Exception( 'ETA.py: 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\' ]' )
+        raise Exception( 'ETA.py: for an unkown reason, label_x[ \'min\' ] > label_x[ \'max\' ]' )
     
     if verbose:
         print( 'label section deleted' )
@@ -490,20 +490,44 @@ if calibration != None:
         calibrations[ 'down' ][ 'x' ][ 'min' ]:
         calibrations[ 'down' ][ 'x' ][ 'max' ]
     ] , axis = 0 )
-    peaks_up   = np.array(
+
-        utils.retrieve_peaks(
+    percent = 0.4
-            mean_up            ,
+
-            window_size = 1    ,
+    peaks_up = np.array( [
-            max_window_size = 1,
+        np.mean( consecutive )
+        for consecutive in
+        utils.consecutive(
+            np.where(
+                mean_up > np.min(
+                    mean_up
+                ) + (
+                    np.max(
+                        mean_up
+                    ) - np.min(
+                        mean_up
                     )
-    ).astype( int )
+                ) * percent
-    peaks_down = np.array(
+            )[0]
-        utils.retrieve_peaks(
-            mean_down          ,
-            window_size = 1    ,
-            max_window_size = 1,
         )
-    ).astype( int )
+    ] , dtype = int )
+
+    peaks_down = np.array( [
+        np.mean( consecutive )
+        for consecutive in
+        utils.consecutive(
+            np.where(
+                mean_down > np.min(
+                    mean_down
+                ) + (
+                    np.max(
+                        mean_down
+                    ) - np.min(
+                        mean_down
+                    )
+                ) * percent
+            )[0]
+        )
+    ] , dtype = int )
 
     peakless_up   = wave_calib.remove_peaks( mean_up   , peaks_up   )
     peakless_down = wave_calib.remove_peaks( mean_down , peaks_down )
@@ -521,6 +545,7 @@ if calibration != None:
         'first': first,
         'last' : last ,
     }
+
     peaks_up    = peaks_up[    up[ 'first' ]    : up[ 'last' ]    + 1 ]
     peaks_down  = peaks_down[  down[ 'first' ]  : down[ 'last' ]  + 1 ]
     peaks_calib = peaks_calib[ calib[ 'first' ] : calib[ 'last' ] + 1 ]
@@ -691,18 +716,10 @@ for line in range_:
         abciss[ wrong_points ],
     )
 
-    import matplotlib.pyplot as plt
+    data[
-    plt.plot(
+        line                                           ,
-        abciss,
+        border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
-        list_ ,
+    ] = list_
-    )
-    plt.plot(
-        abciss,
-        interpolation(
-            abciss,
-        )     ,
-    )
-    plt.show()
 
 if verbose:
     print( 'artefact removed from intensity curve' )
@@ -741,7 +758,7 @@ mean_bias = sp_convolve(
         50,
     )     ,
     'same',
-) / 50
+) / 50 * 0
 
 data[
                            :                       ,
@@ -804,7 +821,6 @@ if verbose:
 if output == None:
     print( stairs , wavelength )
 else:
-
     output_file = pathlib.Path( output )
     with shelve.open( str( output_file ) ) as output:
         output[ 'data'   ]     = stairs