Add steps

- Rotation detection
- WIP calibration detection

ETA.py

@@ -1,6 +1,9 @@
 import numpy as np
 import utils
 import sys
+from scipy.signal import convolve as sp_convolve
+from scipy.signal import find_peaks
+from scipy.ndimage import rotate
 
 if len( sys.argv ) < 2:
     raise Exception( 'this command must have a filename of an ETA fits as an argument' )
@@ -141,9 +144,97 @@ elif label_x[ 'max' ] > data.shape[1] // 2:
 else:
     raise Exception( 'for an unkown reason, label_x[ \'min\' ] > label_x[ \'max\' ]' )
 
+"""
+Rotation
+"""
+
+index    = border[ 'x' ][ 'min' ]
+gradient = np.gradient(
+    data[
+        border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
+            border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
+        ) // 2,
+        index
+    ]
+)
+while np.max( gradient ) - np.min( gradient ) > 5500:
+    index   += 1
+    gradient = np.gradient(
+        data[
+            border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
+                border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
+            ) // 2,
+            index
+        ]
+    )
+
+positions = np.argmax(
+    sp_convolve(
+        np.gradient(
+            data[
+                border[ 'y' ][ 'min' ] : border[ 'y' ][ 'min' ] + (
+                    border[ 'y' ][ 'max' ] - border[ 'y' ][ 'min' ]
+                ) // 2                        ,
+                border[ 'x' ][ 'min' ] : index
+            ]       ,
+            axis = 0
+        )                     ,
+        np.ones( ( 100 , 1 ) ),
+        'valid'
+    )       ,
+    axis = 0
+)
+
+list_   = np.arange(  0     , index - border[ 'x' ][ 'min' ] , 1 )
+polyval = np.polyfit( list_ , positions                      , 1 )
+
+angle = np.arctan( polyval[0] )
+
+data = rotate( data , angle * ( 180 / np.pi ) ) # utils.rotate does not keep intensity absolute value ? TODO
+
+diff_y = int( np.tan( angle ) * ( border[ 'x' ][ 'max' ] - border[ 'x' ][ 'min' ] ) )
+
+border[ 'y' ][ 'min' ] -= diff_y
+border[ 'y' ][ 'max' ] -= diff_y
+
+"""
+Calibration
+"""
+
+tot_avg = np.mean(
+    data[
+        border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
+        border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
+    ]
+)
+def indicator( list_ ):
+    if np.mean( list_ ) > 0.75 * tot_avg:
+        return 0
+    if np.mean( list_ ) < 0.25 * tot_avg:
+        return 1
+    list_ -= np.min( list_ )
+    list_ /= np.max( list_ )
+    positions = np.where( list_ > 0.5 )[0]
+    if len( positions ) < 10:
+        return 2
+    if len( positions ) > 400:
+        return 3
+    distance = np.mean( positions[ 1 : ] - positions[ : -1 ] )
+    if distance < 10:
+        return 4
+    return 10
+
+indicators = np.array( [ indicator( data[ i , border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ] ] ) for i in range( border[ 'y' ][ 'min' ] , border[ 'y' ][ 'max' ] , 1 ) ] )
+
+calibration_areas = utils.consecutive( np.where( indicators == 10 )[0] )
+
 import matplotlib.pyplot as plt
+plt.plot( indicators )
+plt.savefig( 'asset/indicator.png' )
+"""
 plt.imshow( data[
     border[ 'y' ][ 'min' ] : border[ 'y' ][ 'max' ],
     border[ 'x' ][ 'min' ] : border[ 'x' ][ 'max' ]
 ] )
-plt.savefig( 'asset/test.png' )
+plt.savefig( 'asset/test_rotated.png' )
+"""