source: fact/tools/pyscripts/doc/examples.rst@ 18481

========
Examples
========


RawData
========

one may interactively play with the two most important classes *RawData* and
*SlowData* from module pyfact... 

*datafilepath* and *calibfilepath* should be adjusted in case you are not working
on the ISDC cluster. 

explore class RawData::

  from pyfact import RawData
  # or from pyfact import *
  
  datafilepath = '/fact/raw/2012/03/04/20120304_018.fits.gz'
  calibfilepath = '/fact/raw/2012/03/04/20120304_012.drs.fits.gz'
  run = RawData(datafilepath, calibfilepath)
  event = run.next()
  
  type(event)
  
  print 70*'*'
  for key in event:
    print 'key  :', key
    print 'value:', event[key]
    print 70*'*'

for historical reasons, an event contains *data* and *acal_data*, where *acal_data*
would be the DRS amplitude calibrated data, and *data* would be uncalibrated.
But since the calibration was moved into a C++ class, for better performance,
these keys now contains the same data.

loop over RawData::

  from pyfact import RawData
  # or from pyfact import *
  
  datafilepath = '/fact/raw/2012/03/04/20120304_018.fits.gz'
  calibfilepath = '/fact/raw/2012/03/04/20120304_012.drs.fits.gz'
  run = RawData(datafilepath, calibfilepath)

  for event in run:
    print 'event_id:', event['event_id'] 
    # the data can be found in event['data']

SlowData
=========

have a look at some SlowData::

  :~$ cd /fact/aux/2012/05/30
  :/fact/aux/2012/05/30$ python
  
  from pyfact import SlowData
  file = SlowData('20120530.FTM_CONTROL_TRIGGER_RATES.fits')
  file.show()
  

have a look at the *columns*, that are available::

  'columns': {'BoardRate': (40L, 4L, 'E', 'Hz'),
             'ElapsedTime': (1L, 4L, 'E', 'sec'),
             'FTMtimeStamp': (1L, 8L, 'K', 'us'),
             'OnTime': (1L, 4L, 'E', 'sec'),
             'OnTimeCounter': (1L, 8L, 'K', 'us'),
             'PatchRate': (160L, 4L, 'E', 'Hz'),
             'QoS': (1L, 4L, 'J', ''),
             'Time': (1L, 8L, 'D', 'MJD'),
             'TriggerCounter': (1L, 4L, 'J', 'int'),
             'TriggerRate': (1L, 4L, 'E', 'Hz')},

choose the *columns* you would like to retrieve from the file::

  file.register('TriggerRate')
  file.register('Time')
  # or in case you are unsure
  file.register('all')

check, what happened. file has got some new members::

  file.show()

but they are all zero. Now one should call *next()* in order to get 
the file contents row by row::

  file.next()
  file.show()

or loop over the file::

  for row in file:
    print row.Time, row. OnTime

*row* is just be a copy of *file*, but inside the for loop, I think it is 
convenient to access members of *row* rather than members of *file*.
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
========================
Using the os module any command executable on the command line can be called within a script. This is in particular true for your own python scripts::

    import os
    os.system('echo long listing of dir; pwd; ls -l')

or suppose you created a script my_script.py::
        
        from os import system
        system('python my_scrip.py')