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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.

The Basics

Python

http://docs.python.org/tutorial/

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• 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
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  def event( self, evt, env ) :

  
  Use "XppSb3Ipm-1|Ipimb-0" (a.k.a. IPM3) sum of all channels for normalization and filtering
  

  Code Block
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  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
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  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
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  pc = evt.getPhaseCavity() phasecav1 = pc.fFitTime1 phasecav2 = pc.fFitTime2 charge1 = pc.fCharge1 charge2 = pc.fCharge2

  
  Compute delay time and fill histograms
  

  Code Block
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  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

This particular example is done with a CSPad image, but only a single section is available. For more typical CSPad module, see next section.

• For each event, fetch the CsPad information, and get the image array:

  Code Block
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  def event( self, evt, env ) : elements = evt.getCsPadQuads(self.source, env) image = elements[0].data().reshape(185, 388)

  
  
• Select a region of interest. If none is given (optional module parameter), set RoI to be the whole image.

  Code Block
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  # Region of Interest (RoI) if self.roi is None: self.roi = [ 0, image.shape[1], 0, image.shape[0] ] # [x1,x2,y1,y2] print "ROI [x1, x2, y1, y2] = ", self.roi

  
  
• Using only the RoI subset of the image, compute center-of-mass using one of the SciPy.ndimamge algorithms. Add to it the position of the RoI to get the CMS in global pixel coordinates:

  Code Block
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  roi_array = image[self.roi[2]:self.roi[3],self.roi[0]:self.roi[1]] cms = scipy.ndimage.measurements.center_of_mass(roi_array) print "Center-of-mass of the ROI: (x, y) = (%.2f, %.2f)" %(self.roi[0]+cms[1],self.roi[2]+cms[0])

  
  
• Here's an example how you can make an interactive plot and select the Region of Interest with the mouse. Here we plot the image in two axes (subpads on the canvas). The first will always show the full image. In the second axes, you can select a rectangular region in "Zoom" mode (click on the Toolbar's Zoom button). The selected region will be drawn on top of the full image to the left, while the right plot will zoom into the selected region:

  Code Block
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  fig = plt.figure(1,figsize=(16,5)) axes1 = fig.add_subplot(121) axes2 = fig.add_subplot(122) axim1 = axes1.imshow(image) axes1.set_title("Full image") axim2 = axes2.imshow(roi_array, extent=(self.roi[0],self.roi[1],self.roi[3],self.roi[2])) axes2.set_title("Region of Interest") # rectangular ROI selector rect = UpdatingRect([0, 0], 0, 0, facecolor='None', edgecolor='red', picker=10) rect.set_bounds(*axes2.viewLim.bounds) axes1.add_patch(rect) # Connect for changing the view limits axes2.callbacks.connect('xlim_changed', rect) axes2.callbacks.connect('ylim_changed', rect)

  
  
• To compute the center-of-mass of the selected region, revert back to non-zoom mode (hit the 'zoom' button again) and click on the rectangle. The rectangle is connected to the 'onpick' function which updates self.roi and computes the center-of-mass:

  Code Block
  none none
  def onpick(event): xrange = axes2.get_xbound() yrange = axes2.get_ybound() self.roi = [ xrange[0], xrange[1], yrange[0], yrange[1]] roi_array = image[self.roi[2]:self.roi[3],self.roi[0]:self.roi[1]] cms = scipy.ndimage.measurements.center_of_mass(roi_array) print "Center-of-mass of the ROI: (x, y) = (%.2f, %.2f)" % (self.roi[0]+cms[1],self.roi[2]+cms[0]) fig.canvas.mpl_connect('pick_event', onpick) plt.draw()

CSPad images and tile arangements

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