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A quick walk-through of the tools that exist for analysis of xtc files with python.
The main focus is on pyana, and the examples are from and for XPP primarily,
but may be useful examples to other experiments too.

The Basics

Python

http://docs.python.org/tutorial/

Pyana

Analysis Workbook. Python-based Analysis

Setting up a work directory (a.k.a. offline release directory)

Prior to this, you may need to set up your account for offline analysis:
Analysis Workbook. Account Setup

...

newrel ana-current xpptutorial
cd xpptutorial
ls -l
less .sit_release
sit_setup

Exploring an xtc file

pyxtcreader

 pyxtcreader -h
usage: pyxtcreader [options] xtc-files ...

options:
  -h, --help            show this help message and exit
  -v, --verbose
  -l L1_OFFSET, --l1-offset=L1_OFFSET

...

pyxtcreader /reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s0* | less

xtcscanner

xtcscanner -h
usage: xtcscanner [options] xtc-files ...

options:
  -h, --help            show this help message and exit
  -n NDATAGRAMS, --ndatagrams=NDATAGRAMS
  -v, --verbose
  -l L1_OFFSET, --l1-offset=L1_OFFSET
  -e, --epics

...

Scanning....
Start parsing files:
['/reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s00-c00.xtc', '/reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s01-c00.xtc']
  14826 datagrams read in 4.120000 s .   .   .   .   .   .   .
-------------------------------------------------------------
XtcScanner information:
  - 61 calibration cycles.
  - Events per calib cycle:
   [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]

Information from  1  control channels found:
fs2:ramp_angsft_target
Information from  11  devices found
                      BldInfo:EBeam:             EBeamBld_V1 (14641)
            BldInfo:FEEGasDetEnergy:             FEEGasDetEnergy (14563)   Any (78)
             BldInfo:NH2-SB1-IPM-01:             SharedIpimb (14641)
                BldInfo:PhaseCavity:             PhaseCavity (14641)
     DetInfo:EpicsArch-0|NoDevice-0:             Epics_V1 (107580)
         DetInfo:NoDetector-0|Evr-0:             EvrConfig_V4 (62)   EvrData_V3 (14640)
        DetInfo:XppSb2Ipm-1|Ipimb-0:             IpimbConfig_V1 (1)   IpmFexConfig_V1 (1)   IpimbData_V1 (14640)   IpmFex_V1 (14640)
        DetInfo:XppSb3Ipm-1|Ipimb-0:             IpimbConfig_V1 (1)   IpmFexConfig_V1 (1)   IpimbData_V1 (14640)   IpmFex_V1 (14640)
        DetInfo:XppSb3Pim-1|Ipimb-0:             IpimbConfig_V1 (1)   IpmFexConfig_V1 (1)   IpimbData_V1 (14640)   IpmFex_V1 (14640)
        DetInfo:XppSb4Pim-1|Ipimb-0:             IpimbConfig_V1 (1)   IpmFexConfig_V1 (1)   IpimbData_V1 (14640)   IpmFex_V1 (14640)
                          ProcInfo::             RunControlConfig_V1 (62)
XtcScanner is done!
-------------------------------------------------------------

xtcexplorer

XTC Explorer - GUI interface that builds pyana modules for you.

...

Now you have a local version of the XtcExplorer package in your directory. That allows you to edit the
source code and customize the analysis modules in the XtcExplorer/src directory.

Extracting the data with pyana, some examples

Outline of a pyana module

Like the other frameworks, pyana is an executable that loops through the XTC file and calls all
requested user modules at certain transitions. All the analysts need to do is to fill in the
relevant functions in their user analysis module:

Point detector delay scan

• Fetching the ControlPV information:
  ControlPV is available from the env object, and since it only changes at the beginning
  of each calibration cycle, the begincalibcycle function is the appropriate place to get it:
  

  Code Block
  none none
  def begincalibcycle( self, evt, env ) :

  
  The ControlConfig object may contain several pvControl and pvMonitor objects. In this case
  there's only one, but make sure the name matches anyway:

  Code Block
  none none
  ctrl_config = env.getConfig(TypeId.Type.Id_ControlConfig) for ic in range (0, ctrl_config.npvControls() ): cpv = ctrl_config.pvControl(ic) if cpv.name()=="fs2:ramp_angsft_target": # store the value in a class variable (visible in every class method) self.current_pv_value = cpv.value() )

• Fetching the IPIMB and PhaseCavity information:
  All the other information that we need, is available through the evt object, and
  event member function is the place to get it:
  

  Code Block
  none none
  def event( self, evt, env ) :

  
  Use "XppSb3Ipm-1|Ipimb-0" (a.k.a. IPM3) sum of all channels for normalization and filtering
  

  Code Block
  none none
  ipmN_raw = evt.get(TypeId.Type.Id_IpimbData, "XppSb3Ipm-1|Ipimb-0") ipmN_fex = evt.get(TypeId.Type.Id_IpmFex, "XppSb3Ipm-1|Ipimb-0") ipmN_norm = ipmN_fex.sum

  
  Use "XppSb3Pim-1|Ipimb-0" (a.k.a. PIM3) channel 1 as signal
  

  Code Block
  none none
  ipmS_raw = evt.get(TypeId.Type.Id_IpimbData, "XppSb3Pim-1|Ipimb-0" ) ipmS_fex = evt.get(TypeId.Type.Id_IpmFex, "XppSb3Pim-1|Ipimb-0" ) ipm_sig = ipmS_fex.channel[1]

  
  Get the phase cavity:
  

  Code Block
  none none
  pc = evt.getPhaseCavity() phasecav1 = pc.fFitTime1 phasecav2 = pc.fFitTime2 charge1 = pc.fCharge1 charge2 = pc.fCharge2

  
  Compute delay time and fill histograms
  

  Code Block
  none none
  delaytime = self.current_pv_value + phasecav1*1e3 # The "histograms" are nothing but python lists. Append to them, and turn them into arrays at the end. self.h_ipm_rsig.append( ipm_sig ) self.h_ipm_nsig.append( ipm_sig/ipm_norm ) self.h_delaytime.append( delaytime )

Image peak finding

CSPad images and tile arangements

Saving data arrays

Interactive analysis with IPython

The version available in our offline release system is
IPython 0.9.1 – An enhanced Interactive Python.
so this is the one I've been using in these examples.
Not a whole lot more than a python shell.

However, the latest IPython has loads of new and interesting features...

http://ipython.org/

Plotting with MatPlotLib

• The plotting can be done directly in the pyana module, but be aware that you need to disable plotting for the
  module to run successfully in a batch job.

  Code Block
  import matplotlib.pyplot as plt plt.plot(array) plt.show()

...

import matplotlib.pyplot as plt

plt.ion()
plt.plot(array)
plt.draw()

Non-interactive batch analysis