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This page holds a few example code-snippets for use in pyana analysis. The analysis is written in python and uses MatPlotLib.PyPlot for plotting of data. Compare with myana user examples to see how (some of) the same things can be done using the myana analysis framework. The most reliable place for up-to-date information about all the event getters in pyana, see: https://confluence.slac.stanford.edu/display/PCDS/Pyana+Reference+Manual#PyanaReferenceManual-Classpyana.event.Event^{Image Removed}

For all the examples, you may assume that the pyana module contains a class with at least 'beginjob', 'event' and 'endjob' functions that starts something like this:

Code Block

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title	outline of a pyana module

import numpy as np
import matplotlib.pyplot as plt
from pypdsdata import xtc

class mypyana(object):
    def __init__(self,source=""):
        self.source = source
        self.counter = None
        self.array = []   # really just a list

    def beginjob(self,evt,env):
        self.counter = 0

    def event(self,evt,env):
        self.counter += 1

        # snippet code goes here
        thedata = evt.get(xtc.TypeId.Type.Id_SomeType, self.source )
        self.array.append( thedata.somevalue )

    def endjob(self,evt,env):
       print "Job done! Processed %d events. " % self.counter

       # place for plotting etc

BeamLine Data: EBeam

To read out energy, charge and position of the beam from the beamline data, use getEBeam(). It returns a class/structure that has the following members/fields:

Code Block

none

title

getEBeam


def event(self,evt,env):

    ebeam = evt.getEBeam()
       # convert from python list to a numpy array
    try :
  self.array = np.array(    beamChrg = ebeam.fEbeamCharge
        beamEnrg = ebeam.fEbeamL3Energyself.array )

       # beamPosX = ebeam.fEbeamLTUPosXplot graph
       plt.plot(self.array)

BeamLine Data: EBeam

To read out energy, charge and position of the beam from the beamline data, use getEBeam(). It returns a class/structure that has the following members/fields:

Code Block

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title	getEBeam


def event(self,evt,env):

    ebeam = evt.getEBeam() beamPosY = ebeam.fEbeamLTUPosY
        beamAngX = ebeam.fEbeamLTUAngX
        beamAngY = ebeam.fEbeamLTUAngY
    try :
   beamPkCr     beamChrg = ebeam.fEbeamPkCurrBC2fEbeamCharge
        printbeamEnrg = "ebeam: ", beamChrg, beamEnrg, beamPosX, beamPosY, beamAngX, beamAngY, beamPkCrebeam.fEbeamL3Energy
        beamPosX = ebeam.fEbeamLTUPosX
        beamPosY = ebeam.fEbeamLTUPosY
    except:
    beamAngX = ebeam.fEbeamLTUAngX
  print "No EBeam object found"

BeamLine Data: FEE Gas Detector

To read out the energy from the front end enclosure (FEE) gas detector, use getFeeGasDet(). This returns and array of 4 numbers:

Code Block

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title

getFeeGasDet

   beamAngY = ebeam.fEbeamLTUAngY
    fee_energy_array    beamPkCr = evtebeam.getFeeGasDet()fEbeamPkCurrBC2
    gdENRC11  =  print "ebeam: ", beamChrg, beamEnrg, beamPosX, beamPosY, beamAngX, beamAngY, beamPkCr
    except:
        print "No EBeam object found"

BeamLine Data: FEE Gas Detector

To read out the energy from the front end enclosure (FEE) gas detector, use getFeeGasDet(). This returns and array of 4 numbers:

Code Block

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title	getFeeGasDet


    fee_energy_array = evt.getFeeGasDet()
    gdENRC11 = fee_energy_array[0]
    gdENRC12 = fee_energy_array[1]
    gdENRC21 = fee_energy_array[2]
    gdENRC22 = fee_energy_array[3]
fee_energy_array[0]
    gdENRC12 = fee_energy_array[1]
    gdENRC21 = fee_energy_array[2]
    gdENRC22 = fee_energy_array[3]

    energy = (gdENRC21 - gdENRC22) / 2
    # or use the first two that has a different gain:
    energy = (gdENRC11gdENRC21 -+ gdENRC12gdENRC22) / 2
.0
    # or use the first two that has a different gain:
    energy = (gdENRC11 + gdENRC12) / 2.0

BeamLine BeamLine Data: Phase Cavity

To read out fit time and charge of the phase cavity, use getPhaseCavity() which returns a structure with the following fields:

Code Block

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title	getPhaseCavity

     pc = evt.getPhaseCavity()
     try:
         pcFitTime1 = pc.fFitTime1
         pcFitTime2 = pc.fFitTime2
         pcCharge1 = pc.fCharge1
         pcCharge2 = pc.fCharge2
         print "PhaseCavity: ", pcFitTime1,  pcFitTime2, pcCharge1, pcCharge2
      except :
         print "No Phase Cavity object found"

...

Event code

Code Block

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title	EncoderDataEnvData

def event(self, evt, env):
    try:
        encoder evrdata = evt.get(xtc.TypeId.Type.Id_EncoderData, self.enc_source )
  getEvrData("NoDetector-0|Evr-0")
    
    for i encoder_valuein =range encoder(evrdata.valuenumFifoEvents()
    except):
        print "NoEvent encoder found in this event"
        return

...

code: ", evrdata.fifoEvent(i).EventCode

In the example above, the address of the EvrData object is given as "NoDetector-0|Evr-0". The address may be different in other cases, so make sure you have the correct address. If you don't know what it is, you can use 'pyxtcreader -vv <xtcfile> | less' to browse your xtcfile and look for it. Look for a lines with 'contains=EvrConfig_V' or 'contains=EvrData_V'. The address will be found on the same line in 'src=DetInfo(<address>)'

Encoder data (delay scanner)

Code Block

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title	EncoderData

def event(self,evt,env):
   # Encodertry:
 Parameters to convert to picoseconds
   encoder delay_a = -80.0e-6;
    delay_b = 0.52168;
evt.get(xtc.TypeId.Type.Id_EncoderData, self.enc_source )
        delayencoder_cvalue = 299792458;encoder.value()
    delay_0 = 0;

except:
      delay_time = (delay_a *print "No encoder_value + delay_b)*1.e-3 / delay_c) found in this event"
    delay_time = 2 * delay_time / 1.0e-12 + delay_0 + pcFitTime1

Time data

 return

You could combine it with phase cavity time, and compute a time delay from it, for example (I don't know the origin of these parameters!)The time of the event can be obtained within the event function:

Code Block

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title	getTime


def event ( self, evt, env ) :
    # Encoder Parameters to convert to picoseconds
    eventdelay_timea = evt.getTime().seconds() + 1.0e-9*evt.getTime().nanoseconds() )

IPIMB diode data

This is data from sets of 4 diodes around the beam line (Intensity Position, Intensity Monitoring Boards)
that measures the beam intensity in four spots, from which we can also deduce the position of the beam.

Currently there are two data structures that holds data from the same type of devices. Depending on DAQ
configuration, they are either DetInfo type or BldInfo type. Here are examples for extracting both types
in the user module event function:

-80.0e-6;
    delay_b = 0.52168;
    delay_c = 299792458;
    delay_0 = 0;

    delay_time = (delay_a * encoder_value + delay_b)*1.e-3 / delay_c) 
    delay_time = 2 * delay_time / 1.0e-12 + delay_0 + pcFitTime1

Time data

The time of the event can be obtained within the event function:

Code Block

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title	getTime


def event ( self, evt, env ) :
    event_time = evt.getTime().seconds() + 1.0e-9*evt.getTime().nanoseconds() )

IPIMB diode data

This is data from sets of 4 diodes around the beam line (Intensity Position, Intensity Monitoring Boards)
that measures the beam intensity in four spots, from which we can also deduce the position of the beam.

Currently there are two data structures that holds data from the same type of devices. Depending on DAQ
configuration, they are either DetInfo type or BldInfo type. Here are examples for extracting both types
in the user module event function:

Code Block

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title	DetInfo


def event(self, evt, env):
    # raw data
    ipmRaw = evt.get(xtc.TypeId.Type.Id_IpimbData, source )
    try

Code Block

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title

DetInfo


def event(self, evt, env):
    # raw data
    ipmRaw = evt.get(xtc.TypeId.Type.Id_IpimbData, source )
    try:
        ch = [ipmRaw.channel0(),
              ipmRaw.channel1(),
              ipmRaw.channel2(),
              ipmRaw.channel3() ]
                
        ch_volt = [ipmRaw.channel0Volts(),
                   ipmRaw.channel1Volts(),
                   ipmRaw.channel2Volts(),
                   ipmRaw.channel3Volts() ]
    except:
        pass

ch = [ipmRaw.channel0(),
      # feature-extracted data
    ipmFex = evtipmRaw.get(xtc.TypeId.Type.Id_IpmFex, source )
channel1(),
      try:
        ipmRaw.channel2(),
 # array of 4 numbers
         fex_channel = ipmFex.channel 

ipmRaw.channel3() ]
              # scalar values
         fexch_sumvolt = ipmFex.sum [ipmRaw.channel0Volts(),
         fex_xpos = ipmFex.xpos
         fex_ypos = ipmFex.ypos

ipmRaw.channel1Volts(),
          except:
         pass

Code Block

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title

BldInfo


def event(self, evt, env):
ipmRaw.channel2Volts(),
                 ipm = evtipmRaw.getSharedIpimbValue("HFX-DG3-IMB-02")channel3Volts()]
    # or equivalently:except:
        pass

    # ipm = feature-extracted data
    ipmFex = evt.get(xtc.TypeId.Type.Id_SharedIpimbIpmFex, "HFX-DG3-IMB-02"source )
    try: 
        ### Raw data ###
        # arraysarray of 4 numbers:
        ch fex_channel = [ipm.ipimbData.channel0(),ipmFex.channel 

         # scalar values
   ipm.ipimbData.channel1(),
      fex_sum        ipm.ipimbData.channel2(),= ipmFex.sum 
         fex_xpos =    ipm.ipimbData.channel3()]ipmFex.xpos
        ch fex_voltypos = [ipm.ipimbData.channel0Volts(),ipmFex.ypos

     except:
         pass

Code Block

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title	BldInfo


def event(self, evt, env):
    ipm.ipimbData.channel1Volts(), = evt.getSharedIpimbValue("HFX-DG3-IMB-02")
    # or equivalently:
    # ipm = evt.get(xtc.TypeId.Type.Id_SharedIpimb, "HFX-DG3-IMB-02")
     ipm.ipimbData.channel2Volts(),try: 
        ### Raw data ###
         ipm.ipimbData.channel3Volts()]

   # arrays of 4 numbers:
     ### Feature-extracted data ###
ch   = [ipm.ipimbData.channel0(),
     # array of 4 numbers:
     ipm.ipimbData.channel1(),
   fex_channels = ipm.ipmFexData.channel 
        ipm.ipimbData.channel2(),
        # scalars:
        fex_sumipm.ipimbData.channel3()]
        ch_volt = [ipm.ipmFexData.sum 
ipimbData.channel0Volts(),
                 fex_xpos = ipm.ipmFexData.xposipimbData.channel1Volts(),
        fex_ypos =           ipm.ipmFexData.ypos

ipimbData.channel2Volts(),
      except:
         pass

Acqiris waveform data

This method can be used for any detector/device that has Acqiris waveform data. Edit the self.address field to get the detector of your choice.

Initialize class members:

Code Block


    def __init__ ( self ):ipm.ipimbData.channel3Volts()]

        #### initializeFeature-extracted data ###
        self.address# array of 4 numbers:
        fex_channels =  "AmoITof-0|Acqiris-0"
  ipm.ipmFexData.channel 
        
      self.data  =# []scalars:
        self.counterfex_sum = ipm.ipmFexData.sum 0

If you're curious to see any of the Acqiris configuration, e.g. how many channels do we have, you can inspect the AcqConfig object:

Code Block
fex_xpos = ipm.ipmFexData.xpos def beginjob ( self, evt, env ) fex_ypos = ipm.ipmFexData.ypos except: cfg = env.getConfig( _pdsdata.xtc.TypeId.Type.Id_AcqConfig, self.address pass

Acqiris waveform data

This method can be used for any detector/device that has Acqiris waveform data. Edit the self.address field to get the detector of your choice.

Initialize class members:

Code Block

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    def __init__ ( self ):)
        self.num# = cfg.nbrChannels()

The read the event waveform data (an array) and append it to the self.data list:

Code Block
initialize data def event ( self,.address evt,= env ) : "AmoITof-0\|Acqiris-0" channelself.data = 0[] acqDataself.counter = 0

If you're curious to see any of the Acqiris configuration, e.g. how many channels do we have, you can inspect the AcqConfig object:

Code Block

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    def beginjob ( self, evt, env ) evt.getAcqValue( self.address, channel, env)
        if acqData :
        cfg =   self.counter+=1
  env.getConfig( _pdsdata.xtc.TypeId.Type.Id_AcqConfig, self.address )
         self.num wf = cfg.nbrChannels()

The read the event waveform data (an array) and append it to the self.data list:

Code Block

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acqData.waveform()   # def returnsevent a( waveformself, arrayevt, ofenv numpy.ndarray type.) :
        channel = 0
    self.data.append(wf)

At the end of the job, take the average and plot it:

Code Block


    acqData def= endjobevt.getAcqValue( self.address, channel, env ) :

        data = np.array(self.data)  # this is an array of shape (Nevents, nSamples)

if acqData :
            self.counter+=1
        # take the mean ofwf all events for each sampling time
        xs = np.mean(data, axis=0)

= acqData.waveform()   # returns a waveform array of numpy.ndarray type.
            self.data.append(wf)

At the end of the job, take the average and plot it:

Code Block

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 plt.plot(xs)

       def plt.xlabel('Seconds')
endjob( self, env ) :

        data plt= np.ylabel('Volts')
array(self.data)  # this is an array of plt.show()

Which gives you a plot like this
Image Removed

Display images from princeton camera

When plotting with MatPlotLib, we don't need to set the limits of the histogram manually, thus we don't need to read the Princeton configuration for this. If we want to sum the image from several events, we must first define and initialize some variables:

Code Block


   def __init__ ( self ):shape (Nevents, nSamples)

        # take the mean of all events for each sampling time
        #xs initialize= np.mean(data, axis=0)

        self.address =  "SxrEndstation-0|Princeton-0"
plt.plot(xs)

        plt.xlabel('Seconds')
    self.data = None

In each event, we add the image array returned from the getPrincetonValue function:

Code Block
def event ( self, evt, env ) : plt.ylabel('Volts') frame = evtplt.getPrincetonValueshow()

Which gives you a plot like this
Image Added

Princeton camera image

When plotting with MatPlotLib, we don't need to set the limits of the histogram manually, thus we don't need to read the Princeton configuration for this. If we want to sum the image from several events, we must first define and initialize some variables:

Code Block

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   def __init__ ( self ):
        # initialize data
( self.address, env)
       if frame :
           # accumulate the data
           if self.dataaddress is= None : "SxrEndstation-0|Princeton-0"
        self.data = None

In each event, we add the image array returned from the getPrincetonValue function:

Code Block

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title	getPrincetonValue


  def event ( self.data = np.float_(frame.data())
  , evt, env ) :

       frame = evt.getPrincetonValue( self.address, env)
       if frame :
         else  :
# accumulate the data
           if self.data += frame.data()

At the end of the job, display/save the array:

Code Block
is None : def endjob( self, env ) : self.data = plt.imshow( self.data/self.countpass, origin='lower'np.float_(frame.data()) plt.colorbar() else : plt.show() # save the self.data += frame.data()

At the end of the job, display/save the array:

Code Block
def endjob( self, env ) :full image to a png file plt.imsave(fname="pyana_princ_image.png",arr=self.dataimshow( self.data/self.countpass, origin='lower')

Note that imsave saves the image only, pixel by pixel. If you want a view of the figure itself, lower resolution, you can save it from the interactive window you get from plt.show().
Image Removed

CsPad data

Here's an example of getting CsPad data from an event:

Code Block

none


def event(self,evt,env):
    quads = evt.getCsPadQuads(self.img_source, env)
    if not quads :        plt.colorbar()
        plt.show()

        # save the full image to a png file
        print '*** cspad information is missing ***'
        return
        
    # dump information about quadrantsplt.imsave(fname="pyana_princ_image.png",arr=self.data, origin='lower')

Note that imsave saves the image only, pixel by pixel. If you want a view of the figure itself, lower resolution, you can save it from the interactive window you get from plt.show().
Image Added

PnCCD image

Code Block

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title	getPnCcdValue


def event(self,evt,env):
    try:
    print "Number of quadrants: %d"frame %= len(quads)
    evt.getPnCcdValue( self.source, env )
    for q in quads:
 image       print "  Quadrant %d" % q.quad= frame.data()
        print "    virtual_channel: %s" % q.virtual_channel()except:
        pass

Other image (FCCD*,Opal,PIM (TM6740), ... )

These all use the generic getFrameValue function:

Code Block

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title	getFrameValue


def event(self,evt,env):
print "    lanetry:
 %s" % q.lane()
     frame =  print "evt.getFrameValue( self.source )
    tid: %s" % q.tid()
 image       print "    acq_count: %s" % q.acq_count= frame.data()
    except:
    print "   pass

FCCD (Fast CCD) image

The Fast CCD is read out as two 8-bit images, therefore you need this extra line to convert it to the right format.

Code Block

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title	getFrameValue


def event(self,evt,env):
    try: op_code: %s" % q.op_code()
        print "    seq_count: %s" % q.seq_count()
        printframe "= evt.getFrameValue(   ticks: %s" % q.ticks(self.source )
        printimage "    fiducials: %s" % q.fiducials= frame.data()
    except:
    print "    frame_type: %s" % q.frame_type()
    pass

    # convert to 16-bit integer
    printimage.dtype = np.uint16

CsPad data

Here's an example of getting CsPad data from an event:

Code Block

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title	getCsPadQuads


def event(self,evt,env):
"    sb_temp: %s" % map(q.sb_temp, range(4)) quads = evt.getCsPadQuads(self.img_source, env)
    if not quads :
        print '*** cspad information is missing ***'
        return
        
    # imagedump datainformation as 3-dimentional arrayabout quadrants
    print "Number of quadrants: data%d" =% q.datalen()

data2 will give you the third section stored, but be aware that sections sometimes are missing,
and in this case you'll need to check with the configuration information that you can obtain in beginjob:

Code Block

none


def beginjob(self,evt,env):
quads)
    
    for q in quads:
       config = env.getConfig(xtc.TypeId.Type.Id_CspadConfig, self.img_source)
    if not config:print "  Quadrant %d" % q.quad()
        print '*** cspad config object is missing ***'"    virtual_channel: %s" % q.virtual_channel()
        return
print "    lane: %s"  
    quads = range(4)

% q.lane()
    print 
    print "Cspad configuration"
    print "  N quadrants   tid: %d%s" % configq.numQuadstid()
    print "  Quad maskprint "    acq_count: %#x%s" % configq.quadMaskacq_count()
        print "  payloadSize   op_code: %d%s" % configq.payloadSizeop_code()
    print "  badAsicMask0  : %#x   print "    seq_count: %s" % q.seq_count()
        print "    ticks: %s" % configq.badAsicMask0ticks()
        print "  badAsicMask1  : %#x" % config.badAsicMask1()
    print "  asicMask      : %#x" % config.asicMask()
    print "  numAsicsRead  : %d" % config.numAsicsRead()
    try  fiducials: %s" % q.fiducials()
        print "    frame_type: %s" % q.frame_type()
        print "    sb_temp: %s" % map(q.sb_temp, range(4))
            
        # image data as 3-dimentional array
        data = q.data()

So far so good. 'quads' is a list of CsPad Element objects, and not necessarily ordered in the expected way. So you'll need to use q.quad() to obtain the quad number.
q.data() gives you a 3D numpy array [row][col][sec]. Here sections will be ordered as expected, but be aware in case of missing sections, that you may need to check the
configuration object. You can get that from the env object, typically something you do in beginjob:

Code Block

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def beginjob(self,evt,env):
    config = env.getConfig(xtc.TypeId.Type.Id_CspadConfig, self.img_source)
    if not config:
        print '*** cspad config object is missing ***'
        return        
    print "Cspad configuration"
    print "  N quadrants   : %d" % config.numQuads()
    print "  Quad mask     : %#x" % config.quadMask()
    print "  payloadSize   : %d" % config.payloadSize()
    print "  badAsicMask0  : %#x" % config.badAsicMask0()
    print "  badAsicMask1  : %#x" % config.badAsicMask1()
    print "  asicMask      : %#x" % config.asicMask()
    print "  numAsicsRead  : %d" % config.numAsicsRead()

   # get the indices of sections in use:
   qn = range(0,config.numQuads())               
   self.sections = map(config.sections, qn )

If you want to draw the whole CsPad image, there's currently no pyana function that does this. Pyana only supplies the pixels in a numpy array, and the
exact location of each pixel depends on the conditions at the time of data collection. A simplified way of making the image can be seen in cspad_simple.py(newer version (cspad.py) available if you check out the XtcExplorer package).

CSPad pixel coordinates.

The CSPad detector image can be drawn by positioning the sections from the data array into a large image array. This is done in cspad_simple.py above. The positions are extracted from optical meterology measurements and additional calibrations. Alternatively one can find the coordinate of each individual pixel from a pixel map, based on the same optical metrology measurements. This is described in details here

Epics Process Variables and ControlConfig

EPICS data is different from DAQ event data. It stores the conditions and settings of the instruments, but values typically change more slowly than your
average shot-by-shot data, and EPICS data is typically updated only when it changes, or every second, or similar. It is not stored in the 'evt' (event) object,
but in the 'env' (environment) object. You typically would read it only at the beginning of each job or if your doing a scan, you'd read it in every calibration cycle:

Code Block

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	none
title	env.epicsStore()


def begincalibcycle(self,evt,env):

    ## The returned value should be of the type epics.EpicsPvTime.
    pv = env.epicsStore().value( pv_name )
    if not pv:
        # older versions maylogging.warning('EPICS PV %s does not have all methodsexist', pv_name)
    else:
    print "  roiMask value = pv.value 
   : [%s]" % ', '.join([hex(config.roiMask(q)) for q in quads]) status = pv.status 
        alarm_severity = pv.severity

Code Block

	none
	none
title	ControlConfig


def begincalibcycle(self,evt,env):
    ctrl_config = env.getConfig(xtc.TypeId.Type.Id_ControlConfigprint "  numAsicsStored: %s" % str(map(config.numAsicsStored, quads))
    except:
    nControls = ctrl_config.npvControls()
  pass
  for  print "  sectionsic in range (0, nControls ):

       : %s"cpv % str(map(config.sections, quads))= ctrl_config.pvControl(ic)
    print
    name = cpv.name()
        value = cpv.value()

Page tree

Versions Compared

Old Version 19

New Version Current

Key

BeamLine Data: EBeam

BeamLine Data: EBeam

BeamLine Data: FEE Gas Detector

BeamLine Data: FEE Gas Detector

BeamLine BeamLine Data: Phase Cavity

Event code

Encoder data (delay scanner)

Time data

IPIMB diode data

Time data

IPIMB diode data

Acqiris waveform data

Acqiris waveform data

Display images from princeton camera

Princeton camera image

CsPad data

PnCCD image

Other image (FCCD*,Opal,PIM (TM6740), ... )

FCCD (Fast CCD) image

CsPad data

CSPad pixel coordinates.

Epics Process Variables and ControlConfig