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)
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 + 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:
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 ###
# arrays of 4 numbers:
ch = [ipm.ipimbData.channel0(),
ipm.ipimbData.channel1(),
ipm.ipimbData.channel2(),
ipm.ipimbData.channel3()]
ch_volt = [ipm.ipimbData.channel0Volts(),
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:
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:
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:
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:
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
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:
ebeam = evt.getEBeam()
if ebeam :
beamChrg = ebeam.fEbeamCharge
beamEnrg = ebeam.fEbeamL3Energy
beamPosX = ebeam.fEbeamLTUPosX
beamPosY = ebeam.fEbeamLTUPosY
beamAngX = ebeam.fEbeamLTUAngX
beamAngY = ebeam.fEbeamLTUAngY
beamPkCr = ebeam.fEbeamPkCurrBC2
print "ebeam: ", beamChrg, beamEnrg, beamPosX, beamPosY, beamAngX, beamAngY, beamPkCr
else :
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:
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]
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:
pc = evt.getPhaseCavity()
if pc :
pcFitTime1 = pc.fFitTime1
pcFitTime2 = pc.fFitTime2
pcCharge1 = pc.fCharge1
pcCharge2 = pc.fCharge2
print "PhaseCavity: ", pcFitTime1, pcFitTime2, pcCharge1, pcCharge2
else :
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:
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:
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:
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().
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()
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:
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
quads = range(4)
print
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()
try:
# older versions may not have all methods
print " roiMask : [%s]" % ', '.join([hex(config.roiMask(q)) for q in quads])
print " numAsicsStored: %s" % str(map(config.numAsicsStored, quads))
except:
pass
print " sections : %s" % str(map(config.sections, quads))
print
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