Index: fact/tools/pyscripts/doc/examples.rst
===================================================================
--- fact/tools/pyscripts/doc/examples.rst	(revision 14470)
+++ fact/tools/pyscripts/doc/examples.rst	(revision 14471)
@@ -115,36 +115,36 @@
 Have a look::
 
-#!/usr/bin/python -tti
-
-import time
-import numpy as np
-from pyfact import SlowData
-
-f = SlowData('20120601.FTM_STATIC_DATA.fits')
-#
-# do here --> f.show() if you are not sure about the column names
-#
-f.register('PatchThresh')
-f.register('Time')
-f.stack()
-
-# now loop over the file and let SlowData do the magic
-for row in f:
-    pass
-    
-# the stacked data can be found in the dict SlowData.stacked_cols
-# put the Time array into a variable and convert into seconds since 01.01.1970
-t = f.stacked_cols['Time'] * 24. * 3600
-
-# put the thresholds into another var, for convenience
-dtr = f.stacked_cols['PatchThresh']
-
-# give the user some diagnostic info
-print 'start time:', time.asctime(time.gmtime(t[0]))
-print 'stop time:', time.asctime(time.gmtime(t[-1]))
-
-print 'mean/median threshold:', dtr.mean(), np.median(dtr)
-print 'min/max threshold:', dtr.min(), dtr.max()
-print 'std deviation:', dtr.std()
+  #!/usr/bin/python -tti
+  
+  import time
+  import numpy as np
+  from pyfact import SlowData
+  
+  f = SlowData('20120601.FTM_STATIC_DATA.fits')
+  #
+  # do here --> f.show() if you are not sure about the column names
+  #
+  f.register('PatchThresh')
+  f.register('Time')
+  f.stack()
+  
+  # now loop over the file and let SlowData do the magic
+  for row in f:
+      pass
+      
+  # the stacked data can be found in the dict SlowData.stacked_cols
+  # put the Time array into a variable and convert into seconds since 01.01.1970
+  t = f.stacked_cols['Time'] * 24. * 3600
+  
+  # put the thresholds into another var, for convenience
+  dtr = f.stacked_cols['PatchThresh']
+  
+  # give the user some diagnostic info
+  print 'start time:', time.asctime(time.gmtime(t[0]))
+  print 'stop time:', time.asctime(time.gmtime(t[-1]))
+  
+  print 'mean/median threshold:', dtr.mean(), np.median(dtr)
+  print 'min/max threshold:', dtr.min(), dtr.max()
+  print 'std deviation:', dtr.std()