Page History

...

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.

• hit 'Scan File(s)' again
• Checkmark the IPM3 checkbox ('XppSb3Ipm=1|Ipimb-0')
• hit the 'Write configuration to file' button,
• hit the 'Run pyana' button
• hit 'OK' and wait till a plot pops up... Close the window and wait again...
• hit the 'Quit pyana' button
• go to the 'General Settings' tab and switch Display mode to 'SlideShow'
• go back to 'General Settings'
  • again and change the number of events to accumulate to 240
  • hit the 'Write configuration to file'
  and '
  • hit the 'Edit configuration file' button. Edit the line with 'quantities = ': remove 'fex:channels' and add 'fex:ch1' and 'fex:ch0' instead
  • hit 'Run pyana'
  buttons
  • button again (as well as 'OK' when that pops up). Stare at the plot for a while...

xtcexplorer /reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s0*

addpkg XtcExplorer
scons
xtcexplorer /reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s0*

The xtcexplorer has several shortcomings. It tries to be very generic, and thus is sometimes slower than it would have to be. It is also currently only capable of plotting from a single device in each plot, so many correlation plots will need to be added by hand. However, it is a simple tool to just look at the data contents, and provides many examples through its pyana modules.

For some more useful analysis examples, in the following we'll stick to writing customized pyana modules and running pyana from the command line.

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:

...

addpkg XtcExplorer
scons
xtcexplorer /reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s0*

The xtcexplorer has several shortcomings. It tries to be very generic, and thus is sometimes slower than it would have to be. It is also currently only capable of plotting from a single device in each plot, so many correlation plots will need to be added by hand. However, it is a simple tool to just look at the data contents, and provides many examples through its pyana modules.

For some more useful analysis examples, in the following we'll stick to writing customized pyana modules and running pyana from the command line.

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

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 Blocknonenone

  
      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
  ctrlipmS_configraw = envevt.getConfigget(TypeId.Type.Id_ControlConfig) IpimbData, "XppSb3Pim-1|Ipimb-0" ) for ic in range (0, ctrl_config.npvControls() ):ipmS_fex = evt.get(TypeId.Type.Id_IpmFex, "XppSb3Pim-1|Ipimb-0" ) cpv ipm_sig = ctrlipmS_fex.channel[1]

  
  Get the phase cavity:
  

  Code Block
  none none
  config.pvControl(ic) pc = if cpv.name()=="fs2:ramp_angsft_target": evt.getPhaseCavity() phasecav1 = pc.fFitTime1 # store thephasecav2 value in a class variable (visible in every class method) = pc.fFitTime2 charge1 = pc.fCharge1 self.current_pv_valuecharge2 = 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:

  pc.fCharge2

  
  Compute delay time and fill histograms
  

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

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

  Code Block
  none	none	= self.current_pv_value + phasecav1*1e3 # The 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	"histograms" are nothing but python lists. Append to them, and turn them into arrays at the end. self.h_ipm_rsig.append( ipm_sig ) ipmS_raw = evt.get(TypeId.Type.Id_IpimbData, "XppSb3Pim-1|Ipimb-0"self.h_ipm_nsig.append( ipm_sig/ipm_norm ) ipmS_fex = evt.get(TypeId.Type.Id_IpmFex, "XppSb3Pim-1|Ipimb-0" ) ipm_sig = ipmS_fex.channel[1]

  Get the phase cavity:
  Code Blocknonenone

  
          pc = evt.getPhaseCavity()
          phasecav1 = pc.fFitTime1
          phasecav2 = pc.fFitTime2
          charge1 = pc.fCharge1
          charge2 = pc.fCharge2
  

  Compute delay time and fill histograms
  Code Blocknonenone

  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/^{Image Added}

[ofte@psana0106 xpptutorial]$ ipython
Python 2.4.3 (#1, Nov  3 2010, 12:52:40)
Type "copyright", "credits" or "license" for more information.

IPython 0.9.1 -- An enhanced Interactive Python.
?        

...

 -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's 

...

own 

...

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...

...

help system.
object?   -> Details about 'object'. ?object also works, ?? prints more.

In [1]: from numpy import *

In [2]: from matplotlib.pyplot import *

In [3]: ipm3 = load('point_scan_delay.npy')

In [4]: ipm3.shape
Out[4]: (200, 3)

In [5]: ion()

In [6]: delay = ipm3[:,0]

In [7]: ipmraw = ipm3[:,1]

In [8]: ipmnorm = ipm3[:,2]

n [9]: plot(delay,ipmnorm,'ro')
Out[9]: [<matplotlib.lines.Line2D object at 0x59c4c10>]

In [10]: draw()

In [11]:

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