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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.

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() )

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

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
	none
title	outline of a pyana module

def event(self, evt, env):
    # raw data
    ipmRaw = evt.get(xtc.TypeId.Type.Id_IpimbData, source )
    tryimport numpy as np
import matplotlib.pyplot as plt
from pypdsdata import xtc

class mypyana(object):
    def __init__(self,source=""):
        chself.source = [ipmRaw.channel0(),source
        self.counter = None
    ipmRaw.channel1(),
    self.array = []   # really just   ipmRaw.channel2(),a list

    def beginjob(self,evt,env):
         ipmRaw.channel3() ]self.counter = 0

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

        ch_volt# = [ipmRaw.channel0Volts(),
  snippet code goes here
        thedata         ipmRaw.channel1Volts(),
  = evt.get(xtc.TypeId.Type.Id_SomeType, self.source )
           self.array.append( thedata.somevalue )

    def  ipmRaw.channel2Volts(),endjob(self,evt,env):
       print "Job done! Processed %d events. " %     ipmRaw.channel3Volts() ]self.counter

    except:
   # place for plotting  passetc

    # feature-extracted data
 # convert from ipmFexpython = evt.get(xtc.TypeId.Type.Id_IpmFex, source )
    try:list to a numpy array
       self.array = # array of 4 numbersnp.array( self.array )

       #  fex_channel = ipmFex.channel 
plot graph
         # scalar values
         fex_sum = ipmFex.sum 
         fex_xpos = ipmFex.xpos
         fex_ypos = ipmFex.ypos

     except:
         pass

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

	none
	none
title	getEBeam

def event(self, evt, env):

    ipmebeam = evt.getSharedIpimbValue("HFX-DG3-IMB-02"getEBeam()
    # ortry equivalently:
       # ipmbeamChrg = evt.get(xtc.TypeId.Type.Id_SharedIpimb, "HFX-DG3-IMB-02")ebeam.fEbeamCharge
    try: 
   beamEnrg = ebeam.fEbeamL3Energy
   ### Raw data ###
  beamPosX = ebeam.fEbeamLTUPosX
    #   arrays ofbeamPosY 4 numbers:= ebeam.fEbeamLTUPosY
        chbeamAngX = [ipm.ipimbData.channel0(),ebeam.fEbeamLTUAngX
        beamAngY =     ipm.ipimbData.channel1(),ebeam.fEbeamLTUAngY
        beamPkCr =     ipm.ipimbData.channel2(),ebeam.fEbeamPkCurrBC2
        print "ebeam: ", beamChrg, beamEnrg,  ipm.ipimbData.channel3()]
   beamPosX, beamPosY, beamAngX, beamAngY, beamPkCr
     ch_volt = [ipm.ipimbData.channel0Volts(),except:
        print "No EBeam         ipm.ipimbData.channel1Volts(),
                   ipm.ipimbData.channel2Volts(),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

	none
	none
title	getFeeGasDet


    fee_energy_array = evt.getFeeGasDet()
    gdENRC11 = fee_energy_array[0]
    gdENRC12 = fee_energy_array[1]
    gdENRC21   ipm.ipimbData.channel3Volts()]

= fee_energy_array[2]
    gdENRC22 = fee_energy_array[3]

  ### Feature-extracted dataenergy ###
= (gdENRC21 + gdENRC22)     # array of 4 numbers:
        fex_channels = ipm.ipmFexData.channel / 2.0
    # or use the 
first two that has a different   # scalarsgain:
    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

	none
	none
title	getPhaseCavity

fex_sum = ipm.ipmFexData.sum 
     pc =  fex_xpos = ipm.ipmFexData.xposevt.getPhaseCavity()
     try:
        fex_ypos pcFitTime1 = ipmpc.ipmFexData.ypos

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


fFitTime1
    def __init__ ( self ):
 pcFitTime2 = pc.fFitTime2
     # initialize data
    pcCharge1 = pc.fCharge1
         self.address =  "AmoITof-0|Acqiris-0"
pcCharge2 = pc.fCharge2
         print "PhaseCavity: ", pcFitTime1,  self.data = []pcFitTime2, pcCharge1, pcCharge2
      except :
 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:

Code Block
def beginjob (print "No Phase Cavity object found"

Event code

Code Block

	none
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title	EnvData


def event(self, evt, env):
    evrdata = evt.getEvrData("NoDetector-0|Evr-0")
    self, evt, env ) :
        cfg = env.getConfig( _pdsdata.xtc.TypeId.Type.Id_AcqConfig, self.address )
    for i in range self.num = cfg.nbrChannels()

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

Code Block
(evrdata.numFifoEvents()): def event ( self, evt,print env"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

	none
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title	EncoderData


def event(self,evt,env):
    try:
       channel = 0
        acqData = evt.getAcqValue( self.address, channel, env)
        if acqData :
            self.counter+=1
            wfencoder = acqDataevt.waveform()get(xtc.TypeId.Type.Id_EncoderData, self.enc_source )
   #  returns a waveform arrayencoder_value of= numpy.ndarray type.
encoder.value()
    except:
        print "No encoder found  self.data.append(wf)

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

in this event"
        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

Code Block

	none
	none

    def endjob( self, env ) :

# Encoder Parameters to convert to picoseconds
    delay_a = -80.0e-6;
    datadelay_b = np.array(self.data)  # this is an array of shape (Nevents, nSamples)0.52168;
    delay_c = 299792458;
    delay_0 = 0;

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

        plt.plot(xs)

        plt.xlabel('Seconds')
        plt.ylabel('Volts')
        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:

+ 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

	none
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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

	none
	none
title	DetInfo


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

Code Block


       ebeam = evt.getEBeam()
        if ebeam :
            beamChrg = ebeam.fEbeamCharge
            beamEnrgch = ebeam.fEbeamL3Energy[ipmRaw.channel0(),
            beamPosX = ebeam.fEbeamLTUPosXipmRaw.channel1(),
            beamPosY = ebeam.fEbeamLTUPosYipmRaw.channel2(),
            beamAngX = ebeam.fEbeamLTUAngXipmRaw.channel3() ]
            beamAngY = ebeam.fEbeamLTUAngY
  
        ch_volt  beamPkCr = ebeam.fEbeamPkCurrBC2
[ipmRaw.channel0Volts(),
                print  "ebeam: ", beamChrg, beamEnrg, beamPosX, beamPosY, beamAngX, beamAngY, beamPkCr
 ipmRaw.channel1Volts(),
                  else :
 ipmRaw.channel2Volts(),
               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

 ipmRaw.channel3Volts()]
    except:
       fee_energy_array = evt.getFeeGasDet()
     pass

    # feature-extracted data
    gdENRC11ipmFex = fee_energy_array[0]evt.get(xtc.TypeId.Type.Id_IpmFex, source )
    try:
        gdENRC12 =# fee_energy_array[1]array of 4 numbers
        gdENRC21 fex_channel = fee_energy_array[2]
ipmFex.channel 

         gdENRC22# = fee_energy_array[3]scalar values
        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
fex_sum = ipmFex.sum pcfex_xpos = evtipmFex.getPhaseCavity()xpos if pc : pcFitTime1 = pc.fFitTime1 fex_ypos = ipmFex.ypos except: pcFitTime2 = pc.fFitTime2 pass

Code Block

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title	BldInfo


def event(self, evt, env):
    ipm = evt.getSharedIpimbValue("HFX-DG3-IMB-02")
    pcCharge1# = pc.fCharge1or equivalently:
    # ipm = evt.get(xtc.TypeId.Type.Id_SharedIpimb, "HFX-DG3-IMB-02")
    pcCharge2 = pc.fCharge2try: 
        ### Raw data ###
 print "PhaseCavity: ", pcFitTime1,  pcFitTime2, pcCharge1, pcCharge2       # arrays of 4 numbers:
        ch = [ipm.ipimbData.channel0(),
        else :
     ipm.ipimbData.channel1(),
       print "No Phase Cavity object 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
def __init__ ( self ): ipm.ipimbData.channel2(), ipm.ipimbData.channel3()] #ch_volt initialize data= [ipm.ipimbData.channel0Volts(), self.address = "SxrEndstation-0\|Princeton-0" self.data = None

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

ipm.ipimbData.channel1Volts(),
                   ipm.ipimbData.channel2Volts(),
                   ipm.ipimbData.channel3Volts()]

        ### Feature-extracted data ###
        # array of 4 numbers:
        fex_channels = ipm.ipmFexData.channel 
        
        # scalars:
        fex_sum = ipm.ipmFexData.sum 
        fex_xpos = ipm.ipmFexData.xpos
        fex_ypos = 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

	none
	none


    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:

Code Block

	none
	none


    def beginjob ( self, evt, env ) :
        cfg = env.getConfig( _pdsdata.xtc.TypeId.Type.Id_AcqConfig, self.address )
        self.num = cfg.nbrChannels()

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

Code Block

	none
	none


    def event ( self, evt, env ) :
        channel = 0
        acqData = evt.getAcqValue( self.address, channel, env)
        if acqData :
            self.counter+=1
            wf = 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

	none
	none


    def endjob( self, env ) :

        data = np.array(self.data)  # this 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)

        plt.xlabel('Seconds')
        plt.ylabel('Volts')
        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

	none
	none


   def __init__ ( self ):
        # initialize data
        self.address =  "SxrEndstation-0|Princeton-0"
        self.data = None

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

Code Block

	none
	none
title	getPrincetonValue


  def event ( self, evt, env ) :

       frame = evt.getPrincetonValue( self.address, env)
       if frame :
           # accumulate the data
           if self.data is None :
               self.data = np.float_(frame.data())
           else :
               self.data += frame.data()

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

Code Block


   def endjob( self, env ) :
        plt.imshow( self.data/self.countpass, origin='lower')
        plt.colorbar()
        plt.show()

        # save the full image to a png file
        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 Added

PnCCD image

Code Block

	none
	none
title	getPnCcdValue


def event(self,evt,env):
    try:
        frame = evt.getPnCcdValue( self.source, env )
        image = frame.data()
    except:
        pass

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

These all use the generic getFrameValue function:

Code Block

	none
	none
title	getFrameValue


def event(self,evt,env):
    try:
        frame = evt.getFrameValue( self.source )
        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

	none
	none
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

	none
	none
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

	none
	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

	none
	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:
        logging.warning('EPICS PV %s does not exist', pv_name)
    else:
        value = pv.value

Code Block


  def event ( self, evt, env ) :

       frame = evt.getPrincetonValue( self.address, env)
       if frame :
           # accumulate the data
           if self.data is None :
               self.data = np.float_(frame.data())
           else :
        status = pv.status 
      self.data +  alarm_severity = frame.data()

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

pv.severity

Code Block

	none
	none
title	ControlConfig


def begincalibcycle(self,evt,env)

Code Block


   def endjob( self, env ) :
    ctrl_config =   pltenv.imshow( self.data/self.countpass, origin='lower')
  getConfig(xtc.TypeId.Type.Id_ControlConfig)
    
    nControls = pltctrl_config.colorbarnpvControls()
    for ic in range plt.show()(0, nControls ):

        #cpv save the full image to a png file= ctrl_config.pvControl(ic)
        plt.imsave(fname="pyana_princ_image.png",arr=self.data, origin='lower')

...

name = cpv.name()
        value = cpv.value()

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Versions Compared

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Key

Time data

IPIMB diode data

BeamLine Data: EBeam

BeamLine Data: FEE Gas Detector

BeamLine Data: Phase Cavity

Acqiris waveform data

Event code

Encoder data (delay scanner)

Beamline data (Bld)

Time data

IPIMB diode data

Display images from princeton camera

Acqiris waveform 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