Page History

}
cloak.toggle.type = text
cloak.toggle.open = + show
cloak.toggle.close = - hide
{composition-setup}

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.

...

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:unmigrated-inline-wiki-markup

{

...

...

# useful imports
import numpy as np
import matplotlib.pyplot as plt
from pypdsdata.xtc import TypeId

class mymodule (object) :
    """Class whose instance will be used as a user analysis module. """

    def __init__ ( self,
                   source = ""
                   threshold = "" ):
        """Class constructor.
        The parameters to the constructor are passed from pyana configuration file.
        If parameters do not have default values  here then the must be defined in
        pyana.cfg. All parameters are passed as strings, convert to correct type before use.

        @param source         name of device, format 'Det-ID|Dev-ID'
        @param threshold      threshold value (remember to convert from string)
        """
        self.source = source
        self.threshold = float(threshold)

    def beginjob( self, evt, env ) :
        """This method is called once at the beginning of the job. It should
        do a one-time initialization possible extracting values from event
        data (which is a Configure object) or environment.

        @param evt    event data object
        @param env    environment object
        """
        pass

    def beginrun( self, evt, env ) :
        """This optional method is called if present at the beginning of the new run.

        @param evt    event data object
        @param env    environment object
        """
        pass

    def begincalibcycle( self, evt, env ) :
        """This optional method is called if present at the beginning
        of the new calibration cycle.

        @param evt    event data object
        @param env    environment object
        """
        pass

    def event( self, evt, env ) :
        """This method is called for every L1Accept transition.

        @param evt    event data object
        @param env    environment object
        """
        pass

    def endcalibcycle( self, env ) :
        """This optional method is called if present at the end of the
        calibration cycle.

        @param env    environment object
        """
        pass

    def endrun( self, env ) :
        """This optional method is called if present at the end of the run.

        @param env    environment object
        """
        pass

    def endjob( self, env ) :
        """This method is called at the end of the job. It should do
        final cleanup, e.g. close all open files.

        @param env    environment object
        """
        pass

...

...

...

addpkg pyana_examples HEAD
scons

Datatypes, and how to find data from your detector/device in the xtc file.

...

[pyana]

modules = pyana_examples.xppt_delayscan

[pyana_examples.xppt_delayscan]
controlpv = fs2:ramp_angsft_target
ipimb_norm = XppSb3Ipm-1|Ipimb-0
ipimb_sig = XppSb3Pim-1|Ipimb-0
threshold = 0.1
outputfile = point_scan_delay.npy

pyana -n 200 /reg/d/psdm/XPP/xppi0310/xtc/e81-r0098-s0*

Wiki Markup{

...

...

• 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

Here are a collection of useful algorithms for image analysis: http://docs.scipy.org/doc/scipy/reference/ndimage.html

...