Page History

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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
  none none
  def event( self, evt, env ) :

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

  Code Block
  none none
  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
  none none
  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
  none none
  pc = evt.getPhaseCavity() phasecav1 = pc.fFitTime1 phasecav2 = pc.fFitTime2 charge1 = pc.fCharge1 charge2 = pc.fCharge2

  
  Compute delay time and fill histograms
  

  Code Block
  none none
  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

http://docs.scipy.org/doc/scipy/reference/ndimage.html^{Image Added}

CSPad images and tile arangements

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[ofte@psana0106 xpptutorial]$ ipython
Python 2.4.3 (#1, Nov  3 2010, 12:52:40)
Type "copyright", "credits" or "license" for more information.

IPython 0.9.1 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object'. ?object also works, ?? prints more.

In [1]: from numpy import *

In [2]: from matplotlib.pyplot import *

In [3]: ipm3 = load('point_scan_delay.npy')

In [4]: ipm3.shape
Out[4]: (200, 3)

In [5]: ion()

In [6]: delay = ipm3[:,0]

In [7]: ipmraw = ipm3[:,1]

In [8]: ipmnorm = ipm3[:,2]

n [9]: plot(delay,ipmnorm,'ro')
Out[9]: [<matplotlib.lines.Line2D object at 0x59c4c10>]

In [10]: draw()

In [11]:

Plotting with MatPlotLib

Matplotlib:

• http://matplotlib.sourceforge.net/users/pyplot_tutorial.html^{Image Added}

• The plotting can be done directly in the pyana module, but be aware that you need to disable plotting for the
  module to run successfully in a batch job.

  Code Block
  import matplotlib.pyplot as plt plt.plot(array) plt.show()

• Or you can load arrays from a file and interactively plot them in iPython. The same ('recommended') syntax as above can be used, or if you use 'import *' you don't need to prepend the commands with the package name, which is handy when plotting interactively:

  Code Block
  from matplotlib.pyplot import * ion() plot(array) draw()

Related useful tools and links

• http://www.scipy.org/NumPy_for_Matlab_Users^{Image Added}
• http://www.scipy.org/^{Image Added}
• http://www.sagemath.org/^{Image Added}
• http://code.google.com/p/spyderlib/^{Image Added}

Non-interactive batch analysis

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