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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:

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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>)'

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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()

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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:
        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 )        

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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

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