Index: fact/tools/pyscripts/doc/examples.rst
===================================================================
--- fact/tools/pyscripts/doc/examples.rst	(revision 14469)
+++ fact/tools/pyscripts/doc/examples.rst	(revision 14470)
@@ -101,4 +101,54 @@
 I is just easier to understand for the reader, I think.
 
+
+Stack some slow Data into numpy arrays
+======================================
+
+This example shows how to stack some columns from slow data into numpy arrays.
+One first open the file as usual, and *registers* the columns one is interested in.
+Then one informs the SlowData object about ones intention to have the data stacked.
+Now the SlowData object will create 2D-arrays, which contain the registered data
+while one loops over the slowdata file.
+
+The looping is important!
+
+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()
+
+
+
+
 calling a system command
 ========================