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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.

Encoder data (delay scanner)

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

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="")

Code Block

none


def event(self,evt,env):
    try:
        encoder = evt.get(xtc.TypeId.Type.Id_EncoderData, self.enc_source )
        encoder_value = encoder.value()
    except:
        print "No encoder found in this event"
self.source = source
        self.counter =   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!):

Code Block

none


None
        #self.array Encoder= Parameters[] to convert to# picoseconds
really just a list

  delay_a = -80.0e-6; def beginjob(self,evt,env):
     delay_b   self.counter = 0.52168;

     delay_c = 299792458;
def event(self,evt,env):
        delay_0 self.counter += 0;1

    delay_time = (delay_a * encoder_value# + delay_b)*1.e-3 / delay_c) 
    delay_time = 2 * delay_time / 1.0e-12 + delay_0 + phase_cavity1

Time data

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

...


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

IPIMB diode data

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

none


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

       # convert from python list to a numpy array
       self.array = np.array( self.array )

       # plot graph
        ch = [ipmRaw.channel0(),
              ipmRaw.channel1(),
              ipmRaw.channel2(),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()
    try :
        beamChrg ipmRaw.channel3() ]= ebeam.fEbeamCharge
        beamEnrg        = ebeam.fEbeamL3Energy
        ch_voltbeamPosX = [ipmRaw.channel0Volts(),ebeam.fEbeamLTUPosX
        beamPosY = ebeam.fEbeamLTUPosY
        beamAngX = ipmRaw.channel1Volts(),
ebeam.fEbeamLTUAngX
        beamAngY = ebeam.fEbeamLTUAngY
        beamPkCr = ipmRaw.channel2Volts(),ebeam.fEbeamPkCurrBC2
        print "ebeam: ", beamChrg, beamEnrg, beamPosX, beamPosY, beamAngX,    ipmRaw.channel3Volts() ]beamAngY, beamPkCr
    except:
        pass

print "No EBeam  # feature-extracted data
    ipmFex = evt.get(xtc.TypeId.Type.Id_IpmFex, source )
    try: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]
   # arraygdENRC12 of 4 numbers= fee_energy_array[1]
    gdENRC21     fex_channel = ipmFex.channel 

         # scalar values
         fex_sum = ipmFex.sum = fee_energy_array[2]
    gdENRC22 = fee_energy_array[3]

    energy = (gdENRC21 + gdENRC22) / 2.0
    # or use the first two that has a different gain:
    energy = (gdENRC11 + gdENRC12) / 2.0

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

fex_xpos = ipmFex.xpos
         fex_ypospc = ipmFex.ypos
evt.getPhaseCavity()
     excepttry:
         pass

Code Block

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def event(self, evt, env):
pcFitTime1 = pc.fFitTime1
         ipmpcFitTime2 = evt.getSharedIpimbValue("HFX-DG3-IMB-02")
pc.fFitTime2
         #pcCharge1 or equivalently:
= pc.fCharge1
     #    ipmpcCharge2 = evt.get(xtc.TypeId.Type.Id_SharedIpimb, "HFX-DG3-IMB-02")
pc.fCharge2
        try: 
print "PhaseCavity: ", pcFitTime1,  pcFitTime2,   ### Raw data ###pcCharge1, pcCharge2
        # arrays of 4 numbersexcept :
        ch = [ipm.ipimbData.channel0(),
       print "No Phase Cavity object found"

Event code

Code Block

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title	EnvData


def event(self, evt, env):
    evrdata =  ipm.ipimbData.channel1(),evt.getEvrData("NoDetector-0|Evr-0")
    
    for i in range   ipm.ipimbData.channel2(),(evrdata.numFifoEvents()):
        print "Event 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):
    try:
  ipm.ipimbData.channel3()]
        ch_volt = [ipm.ipimbData.channel0Volts(),
                   ipm.ipimbData.channel1Volts(),
                   ipm.ipimbData.channel2Volts(),
            encoder = evt.get(xtc.TypeId.Type.Id_EncoderData, self.enc_source )
   ipm.ipimbData.channel3Volts()]

     encoder_value = encoder.value()
 ### Feature-extracted data ###except:
        #print array"No ofencoder 4found numbers:
in this event"
      fex_channels = ipm.ipmFexData.channel  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!):

Code Block

	none
	none

    # Encoder Parameters to 
convert to picoseconds
    delay_a  # scalars:= -80.0e-6;
    delay_b = 0.52168;
   fex delay_sumc = ipm.ipmFexData.sum 299792458;
        fex_xposdelay_0 = ipm.ipmFexData.xpos0;

    delay_time = (delay_a * fexencoder_yposvalue = ipm.ipmFexData.ypos

     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 ): # initialize data self.address = "AmoITof-0\|Acqiris-0" self.data = [] self.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:

+ 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(

Code Block


    def beginjob ( self, evt, env ) :
    # raw data
  cfg  ipmRaw = envevt.getConfigget( _pdsdata.xtc.TypeId.Type.Id_AcqConfigIpimbData, self.addresssource )
    try:
        self.numch = cfg[ipmRaw.nbrChannelschannel0()

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

Code Block


,
            def event ipmRaw.channel1( self),
 evt, env ) :
        channel = 0 ipmRaw.channel2(),
        acqData  = evt.getAcqValue( self.address, channel, env) ipmRaw.channel3() ]
        if acqData :
      
      self.counter+=1
            wf = acqData.waveform()   # returns a waveform array of numpy.ndarray type.
ch_volt = [ipmRaw.channel0Volts(),
                   selfipmRaw.data.appendchannel1Volts(wf)

...

Code Block

    def endjob( self, env ) :

        data = npipmRaw.arraychannel2Volts(self.data),
  # this is an array of shape (Nevents, nSamples)

        # take the mean of all events for each sampling time
    ipmRaw.channel3Volts()]
    except:
        pass

     xs = np.mean(data, axis=0)

# feature-extracted data
    ipmFex =   pltevt.plot(xs)
get(xtc.TypeId.Type.Id_IpmFex, source )
    try:
         # array of  plt.xlabel('Seconds')4 numbers
        plt.ylabel('Volts')  fex_channel = ipmFex.channel 

        plt.show()

Which gives you a plot like this
Image Removed

Beamline data (Bld)

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
# scalar values fex_sum ebeam = evt.getEBeam()ipmFex.sum if ebeam :fex_xpos = ipmFex.xpos beamChrg fex_ypos = ebeamipmFex.fEbeamChargeypos except: beamEnrg = ebeam pass

Code Block

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title	BldInfo


def event(self, evt, env):
.fEbeamL3Energy
            beamPosXipm = ebeam.fEbeamLTUPosXevt.getSharedIpimbValue("HFX-DG3-IMB-02")
    # or equivalently:
    #  beamPosYipm = ebeam.fEbeamLTUPosYevt.get(xtc.TypeId.Type.Id_SharedIpimb, "HFX-DG3-IMB-02")
    try: 
       beamAngX = ebeam.fEbeamLTUAngX
  ### Raw data ###
        # arrays beamAngYof = ebeam.fEbeamLTUAngY4 numbers:
        ch = [ipm.ipimbData.channel0(),
   beamPkCr = ebeam.fEbeamPkCurrBC2
         ipm.ipimbData.channel1(),
   print "ebeam: ", beamChrg, beamEnrg, beamPosX, beamPosY, beamAngX, beamAngY, beamPkCr           ipm.ipimbData.channel2(),
        else :
     ipm.ipimbData.channel3()]
       print "No EBeam object found"

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

Code Block


ch_volt = [ipm.ipimbData.channel0Volts(),
                 fee_energy_array = evtipm.ipimbData.getFeeGasDetchannel1Volts(),
        gdENRC11 = fee_energy_array[0]
        gdENRC12 = fee_energy_array[1] ipm.ipimbData.channel2Volts(),
        gdENRC21 = fee_energy_array[2]
        gdENRC22 = fee_energy_array[3]
 ipm.ipimbData.channel3Volts()]

        ###  print "GasDet energy ", gdENRC11, gdENRC12, gdENRC21, gdENRC22

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

Code Block

Feature-extracted data ###
        # array of 4 numbers:
        pcfex_channels = evt.getPhaseCavity()
ipm.ipmFexData.channel 
        
  if  pc :
   # scalars:
        pcFitTime1fex_sum = pc.fFitTime1ipm.ipmFexData.sum 
        fex_xpos    pcFitTime2 = pc.fFitTime2= ipm.ipmFexData.xpos
            pcCharge1 fex_ypos = pcipm.ipmFexData.fCharge1
            pcCharge2 = pc.fCharge2ypos

            print "PhaseCavity: ", pcFitTime1,  pcFitTime2, pcCharge1, pcCharge2except:
        else : 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 ):
     print "No Phase Cavity# objectinitialize found"

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
data def __init__ ( self.address ): # initialize data= "AmoITof-0\|Acqiris-0" self.addressdata = "SxrEndstation-0\|Princeton-0"[] self.datacounter = None0

In each event, we add the image array returned from the getPrincetonValue functionIf 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 eventbeginjob ( self, evt, env ) :

        framecfg = evtenv.getPrincetonValue(getConfig( _pdsdata.xtc.TypeId.Type.Id_AcqConfig, self.address, env)
        self.num = cfg.nbrChannels()

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

Code Block

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if frame :
           # accumulate the data
    def event ( self, evt, env ) if:
 self.data is None :
    channel = 0
         self.dataacqData = npevt.float_getAcqValue(frame.data()) self.address, channel, env)
        if acqData  else :
               self.data += frame.data()

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

Code Block


self.counter+=1
   def endjob( self, env ) :
    wf =   pltacqData.imshowwaveform( self.data/self.countpass, origin='lower')
        plt.colorbar()
)   # returns a waveform array of numpy.ndarray type.
         plt.show()
   self.data.append(wf)

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

Code Block

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    def endjob( self, env #) save:

 the full image to a png file
 data = np.array(self.data)  # this  plt.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 Removed

CsPad data

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

Code Block

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def event(self,evt,env):
    quads = evt.getCsPadQuads(self.img_source, env)
    if not quads :is an array of shape (Nevents, nSamples)

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

        plt.plot(xs)

        print '*** cspad information is missing ***'plt.xlabel('Seconds')
        returnplt.ylabel('Volts')
        
    # dump information about quadrants
    print "Number of quadrants: %d" % len(quads)plt.show()

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 ):
    
    for# q in quads:initialize data
        printself.address "=  Quadrant %d" % q.quad()"SxrEndstation-0|Princeton-0"
        printself.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, evt, env ) :

       frame = evt.getPrincetonValue( self.address, env)
"    virtual_channel: %s" % q.virtual_channel()
        print "    lane: %s" % q.lane()
        print "if frame   tid: %s" % q.tid()
        print "  # accumulate acq_count: %s" % q.acq_count()
the data
          print "if self.data is None op_code:
 %s" % q.op_code()
        print "    seq_count: %s" % q.seq_count()self.data = np.float_(frame.data())
        print "  else  ticks:
 %s" % q.ticks()
        print "    fiducials: %s" % q.fiducials()self.data += frame.data()

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

Code Block

   def endjob( self, env ) print:
 "    frame_type: %s" % qplt.frame_type(imshow( self.data/self.countpass, origin='lower')
        print "plt.colorbar()
     sb_temp: %s" % map(q.sb_temp, range(4))plt.show()

        # save the full 
image to a      # image data as 3-dimentional arraypng file
        plt.imsave(fname="pyana_princ_image.png",arr=self.data, = q.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 beginjobevent(self,evt,env):
    try:
    config    frame = envevt.getConfig(xtc.TypeId.Type.Id_CspadConfig,getPnCcdValue( self.img_source)
,    if not config:env )
        print '*** cspad config object is missing ***'image = frame.data()
        returnexcept:
        
    quads = range(4)
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 try:
    print "Cspad configuration"
  frame = print "  N quadrantsevt.getFrameValue( self.source )
     : %d" % config.numQuads()   image = frame.data()
    except:
        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:
        frame = evt.getFrameValue( self.source )
        image = frame.data()
    except:
        pass

    # convert to 16-bit integer
    image.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):
    quads = evt.getCsPadQuads(self.img_source, env)
    if not quads :
        print '*** cspad information is missing ***'
        return
        
    # dump information about quadrants
    print "Number of quadrants: %d" % len(quads)
    
    for q in quads:
        print "  Quadrant %d" % q.quad()
        print "    virtual_channel: %s" % q.virtual_channel()
        print "    lane: %s" % q.lane()
        print "    tid: %s" % q.tid()
        print "    acq_count: %s" % q.acq_count()
        print "    op_code: %s" % q.op_code()
        print "    seq_count: %s" % q.seq_count()
        print "    ticks: %s" % q.ticks()
        print "    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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	none


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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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:
        logging.warning('EPICS PV %s does not exist', pv_name)
    else:
        value = pv.value 
        status = pv.status 
    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()
    try:
        # older versions may not have all methods
        printalarm_severity "=  roiMask   pv.severity

Code Block

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title	ControlConfig


def begincalibcycle(self,evt,env):
    : [%s]" % ', '.join([hex(config.roiMask(q)) for q in quads])ctrl_config = env.getConfig(xtc.TypeId.Type.Id_ControlConfig)
    
    nControls    print "  numAsicsStored: %s" % str(map(config.numAsicsStored, quads))= ctrl_config.npvControls()
    except:
for ic in range (0, nControls ):

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

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Versions Compared

Old Version 12

New Version Current

Key

Encoder data (delay scanner)

Time data

IPIMB diode data

BeamLine Data: EBeam

BeamLine Data: FEE Gas Detector

BeamLine Data: Phase Cavity

Event code

Encoder data (delay scanner)

Acqiris waveform data

Time data

IPIMB diode data

Beamline data (Bld)

Acqiris waveform data

Display images from princeton camera

CsPad data

Princeton camera image

PnCCD image

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

FCCD (Fast CCD) image

CsPad data

CSPad pixel coordinates.

Epics Process Variables and ControlConfig