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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:

...

    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]

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

BeamLine Data: Phase Cavity

...

     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

def event(self, evt, env):
    evrdata  try:
  = evt.getEvrData("NoDetector-0|Evr-0")
    
    for i encoderin =range evt(evrdata.get(xtc.TypeId.Type.Id_EncoderData, self.enc_source )numFifoEvents()):
        encoder_value = encoder.value()
    except:
        print "No encoder found 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!):

...

    # Encoder Parameters to convert to picoseconds
    delay_a = -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:

...

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

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)

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"
        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!):

    # Encoder Parameters to convert to picoseconds
    delay_a = -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:

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:

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() ]
                

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:

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

    # feature-extracted data
    ipmFex = evt.get(xtc.TypeId.Type.Id_IpmFex, source )
    try:
         # array of 4 numbers
         fex_channel = ipmFex.channel 

         # scalar values
         fex_sum = ipmFex.sum 
         fex_xpos = ipmFex.xpos
         fex_ypos = ipmFex.ypos

     except:
         pass

def event(self, evt, env):
    ipm = evt.getSharedIpimbValue("HFX-DG3-IMB-02")
    # or equivalently:
    # ipm = evt.get(xtc.TypeId.Type.Id_SharedIpimb, "HFX-DG3-IMB-02")
    try: 
        ### Raw data ###
        #ch_volt arrays of 4 numbers:
= [ipmRaw.channel0Volts(),
          ch = [ipm.ipimbData.channel0(),
       ipmRaw.channel1Volts(),
       ipm.ipimbData.channel1(),
              ipm.ipimbData.channel2ipmRaw.channel2Volts(),
                   ipmipmRaw.ipimbData.channel3channel3Volts()]
    except:
    ch_volt = [ipm.ipimbData.channel0Volts(),
  pass

    # feature-extracted data
    ipmFex       ipm.ipimbData.channel1Volts(),
  = evt.get(xtc.TypeId.Type.Id_IpmFex, source )
    try:
         # array of  ipm.ipimbData.channel2Volts(),4 numbers
         fex_channel          ipm.ipimbData.channel3Volts()]= ipmFex.channel 

        ### Feature-extracted# datascalar ###values
        # array of 4 numbers:
        fex_channels = ipm.ipmFexData.channel 
        
        # scalars:
        fex fex_sum = ipmipmFex.ipmFexData.sum 
         fex_xpos = ipmipmFex.ipmFexData.xpos
         fex_ypos = ipm.ipmFexDataipmFex.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:

    def __init__ ( self event(self, evt, env):
    ipm    # initialize data= evt.getSharedIpimbValue("HFX-DG3-IMB-02")
    # or equivalently:
  self.address  # ipm = evt.get(xtc.TypeId.Type.Id_SharedIpimb, "AmoITof-0|Acqiris-0"HFX-DG3-IMB-02")
    try: 
        ### Raw self.data =###
 []
       # self.counterarrays = 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:

of 4 numbers:
    def   beginjob (ch self, evt, env ) :
= [ipm.ipimbData.channel0(),
           cfg = env.getConfig( _pdsdata.xtc.TypeId.Type.Id_AcqConfig, self.address )
 ipm.ipimbData.channel1(),
            self.num = cfgipm.ipimbData.nbrChannels()

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

channel2(),
    def event ( self, evt, env ) :
   ipm.ipimbData.channel3()]
     channel = 0
        acqData = evt.getAcqValue( self.address, channel, env)
ch_volt = [ipm.ipimbData.channel0Volts(),
          if acqData :
       ipm.ipimbData.channel1Volts(),
     self.counter+=1
            wf = acqDataipm.ipimbData.waveformchannel2Volts(),
   # returns a waveform array of numpy.ndarray type.
            self.data.append(wf)

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

ipm.ipimbData.channel3Volts()]

    def   endjob( self, env ) :

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

array of 4 numbers:
        fex_channels = ipm.ipmFexData.channel 
        # take
 the mean of all events for each sampling# timescalars:
        xsfex_sum = np.mean(data, axis=0)

ipm.ipmFexData.sum 
        plt.plot(xs)

fex_xpos = ipm.ipmFexData.xpos
        plt.xlabel('Seconds')fex_ypos = ipm.ipmFexData.ypos

        plt.ylabel('Volts')
except:
         plt.show()

Which gives you a plot like this
Image Removed

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:

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:

    def __init__ ( self ):
        # initialize data
        self.address =  "SxrEndstationAmoITof-0|PrincetonAcqiris-0"
        self.data = None
[]
        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 objectIn each event, we add the image array returned from the getPrincetonValue function:

    def eventbeginjob ( self, evt, env ) :

        framecfg = evtenv.getPrincetonValuegetConfig( _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:

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

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

counter+=1
   def endjob( self, env ) :
    wf = acqData.waveform()  plt.imshow( self.data/self.countpass, origin='lower')
     # returns a waveform array of numpy.ndarray type.
    plt.colorbar()
        pltself.data.showappend(wf)

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

    def endjob( self, env # 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 Removed

PnCCD image

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

      image = frameplt.dataxlabel('Seconds')
    except:    plt.ylabel('Volts')
        pass

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

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 variablesThese all use the generic getFrameValue function:

def event(self,evt,env
   def __init__ ( self ):
    try:
      #  frame = evt.getFrameValue( self.source )initialize data
        imageself.address = frame.data() "SxrEndstation-0|Princeton-0"
    except:
    self.data =   pass

FCCD (Fast CCD) image

None

In each event, we add the image array returned from the getPrincetonValue function:The Fast CCD is read out as two 8-bit images, therefore you need this extra line to convert it to the right format.

  def event ( self, evt, env ) :
    try:
        frame = evt.getFrameValuegetPrincetonValue( self.sourceaddress, env)
       if image = frame.data()frame :
    except:
       # accumulate the passdata

    # convert to 16-bit integer
   if imageself.dtypedata is = np.uint16

CsPad data

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

def event(self,evt,env):
None :
       quads = evt.getCsPadQuads(self.img_source, env)
    if notself.data quads := np.float_(frame.data())
        print '*** cspad information is missing ***'else :
        return
       self.data += frame.data()

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

   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

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:

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.

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:

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:

def beginjob(self,evt,env):
    config = env.getConfig(xtc.TypeId.Type.Id_CspadConfig, self.img_source)
    if not config:
    # 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 "'*** cspad config object lane:is %s" % q.lane()missing ***'
        printreturn "    tid: %s" % q.tid()
    print "Cspad configuration"
    print "  N quadrants  acq_count : %s%d" % qconfig.acq_countnumQuads()
    print "  Quad printmask "    op_code: %s%#x" % qconfig.op_codequadMask()
        print "  payloadSize  seq_count : %s%d" % qconfig.seq_countpayloadSize()
   
     print "  badAsicMask0  ticks: %s%#x" % qconfig.ticksbadAsicMask0()
        print "  badAsicMask1  fiducials: %s%#x" % qconfig.fiducialsbadAsicMask1()
    print "  asicMask print "    frame_type: %s%#x" % qconfig.frame_typeasicMask()
        print "  numAsicsRead  sb_temp: %s%d" % map(q.sb_temp, range(4))config.numAsicsRead()

   # get the indices of sections in use:
   qn = range(0,config.numQuads())       
        #
 image data as 3-dimentional array
  self.sections = map(config.sections, qn )      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 arrayrowcolsec. 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:

...

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:

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 
        alarm_severity = pv.severity 

def begincalibcycle(self,evt,env):
    ctrl_config = env.getConfig(xtc.TypeId.Type.Id_ControlConfig)
    
    nControls = ctrl_config.npvControls()
    for ic in range (0, nControls ):

        cpv = ctrl_config.pvControl(ic)
        name = cpv.name()
        value = cpv.value()