Wiki Markup |
{ Composition Setup |
---|
}
cloak.toggle.type = text
cloak.toggle.open = + show
cloak.toggle.close = - hide
{composition-setup} |
A quick walk-through of the tools that exist for analysis of xtc files with python.
The main focus is on pyana, and the examples are from and for XPP primarily,
but may be useful examples to other experiments too.
...
Like the other frameworks, pyana is an executable that loops through the XTC file and calls all
requested user modules at certain transitions. All the analysts need to do is to fill in the
relevant functions in their user analysis module:unmigrated-inline-wiki-markup
{
:=}...
...
Code Block |
---|
# useful imports
import numpy as np
import matplotlib.pyplot as plt
from pypdsdata.xtc import TypeId
class mymodule (object) :
"""Class whose instance will be used as a user analysis module. """
def __init__ ( self,
source = ""
threshold = "" ):
"""Class constructor.
The parameters to the constructor are passed from pyana configuration file.
If parameters do not have default values here then the must be defined in
pyana.cfg. All parameters are passed as strings, convert to correct type before use.
@param source name of device, format 'Det-ID|Dev-ID'
@param threshold threshold value (remember to convert from string)
"""
self.source = source
self.threshold = float(threshold)
def beginjob( self, evt, env ) :
"""This method is called once at the beginning of the job. It should
do a one-time initialization possible extracting values from event
data (which is a Configure object) or environment.
@param evt event data object
@param env environment object
"""
pass
def beginrun( self, evt, env ) :
"""This optional method is called if present at the beginning of the new run.
@param evt event data object
@param env environment object
"""
pass
def begincalibcycle( self, evt, env ) :
"""This optional method is called if present at the beginning
of the new calibration cycle.
@param evt event data object
@param env environment object
"""
pass
def event( self, evt, env ) :
"""This method is called for every L1Accept transition.
@param evt event data object
@param env environment object
"""
pass
def endcalibcycle( self, env ) :
"""This optional method is called if present at the end of the
calibration cycle.
@param env environment object
"""
pass
def endrun( self, env ) :
"""This optional method is called if present at the end of the run.
@param env environment object
"""
pass
def endjob( self, env ) :
"""This method is called at the end of the job. It should do
final cleanup, e.g. close all open files.
@param env environment object
"""
pass
|
...
...
...
Panel |
---|
For the following two examples, check out the latest version of the pyana_examples package: Code Block |
---|
|
addpkg pyana_examples HEAD
scons
|
(Note, if you don't already have a Kerberos ticket, you need to issue a 'kinit ' command before 'addpkg '. You will be prompted for your unix password.) |
Datatypes, and how to find data from your detector/device in the xtc file.
...
Panel |
---|
Open an editor and save the following in a file named pyana.cfg: Code Block |
---|
|
[pyana]
modules = pyana_examples.xppt_delayscan
[pyana_examples.xppt_delayscan]
controlpv = fs2:ramp_angsft_target
ipimb_norm = XppSb3Ipm-1|Ipimb-0
ipimb_sig = XppSb3Pim-1|Ipimb-0
threshold = 0.1
outputfile = point_scan_delay.npy
|
If you look at the code (pyana_examples/src/xppt_delayscan.py) you'll notice there are no detector names in there. The names of the detectors in the XTC file are passed as parameters from the configuration file above. The ipimb_norm parameter represents the IPIMB diode used for normalization, and the configuration files set its value to "XppSb3Ipm-1|Ipimb-0" (a.k.a. IPM3). Similarly, the IPIMB diode used for signal is represented by the pipmb_sig and its value set to "XppSb3Pim-1|Ipimb-0" (a.k.a. PIM3). By changing these parameter values, the pyana_examples/src/xppt_delayscan.py module can easily be used for other experiments or instruments. Run pyana (start with 200 events): Code Block |
---|
|
pyana -n 200 /reg/d/psdm/XPP/xppi0310/xtc/e81-r0098-s0*
|
|
Wiki Markup |
{
:=}...
...
- Fetching the ControlPV information:
ControlPV is available from the env
object, and since it only changes at the beginning
of each calibration cycle, the begincalibcycle
function is the appropriate place to get it:
Code Block |
---|
|
def begincalibcycle( self, evt, env ) :
|
The ControlConfig object may contain several pvControl and pvMonitor objects. In this case
there's only one, but make sure the name matches anyway: Code Block |
---|
|
ctrl_config = env.getConfig(TypeId.Type.Id_ControlConfig)
for ic in range (0, ctrl_config.npvControls() ):
cpv = ctrl_config.pvControl(ic)
if cpv.name()=="fs2:ramp_angsft_target":
# store the value in a class variable (visible in every class method)
self.current_pv_value = cpv.value() )
|
- Fetching the IPIMB and PhaseCavity information:
All the other information that we need, is available through the evt
object, and
event
member function is the place to get it:
Code Block |
---|
|
def event( self, evt, env ) :
|
Use "XppSb3Ipm-1|Ipimb-0" (a.k.a. IPM3) sum of all channels for normalization and filtering
Code Block |
---|
|
ipmN_raw = evt.get(TypeId.Type.Id_IpimbData, "XppSb3Ipm-1|Ipimb-0")
ipmN_fex = evt.get(TypeId.Type.Id_IpmFex, "XppSb3Ipm-1|Ipimb-0")
ipmN_norm = ipmN_fex.sum
|
Use "XppSb3Pim-1|Ipimb-0" (a.k.a. PIM3) channel 1 as signal
Code Block |
---|
|
ipmS_raw = evt.get(TypeId.Type.Id_IpimbData, "XppSb3Pim-1|Ipimb-0" )
ipmS_fex = evt.get(TypeId.Type.Id_IpmFex, "XppSb3Pim-1|Ipimb-0" )
ipm_sig = ipmS_fex.channel[1]
|
Get the phase cavity:
Code Block |
---|
|
pc = evt.getPhaseCavity()
phasecav1 = pc.fFitTime1
phasecav2 = pc.fFitTime2
charge1 = pc.fCharge1
charge2 = pc.fCharge2
|
Compute delay time and fill histograms
Code Block |
---|
|
delaytime = self.current_pv_value + phasecav1*1e3
# The "histograms" are nothing but python lists. Append to them, and turn them into arrays at the end.
self.h_ipm_rsig.append( ipm_sig )
self.h_ipm_nsig.append( ipm_sig/ipm_norm )
self.h_delaytime.append( delaytime )
|
Image peak finding
Here are a collection of useful algorithms for image analysis: http://docs.scipy.org/doc/scipy/reference/ndimage.html
...