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The Basics

Python

http://docs.python.org/tutorial/^{Image Removed}

Pyana

Analysis Workbook. Python-based Analysis

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Scanning....
Start parsing files:
['/reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s00-c00.xtc', '/reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s01-c00.xtc']
  14826 datagrams read in 4.120000 s .   .   .   .   .   .   .
-------------------------------------------------------------
XtcScanner information:
  - 61 calibration cycles.
  - Events per calib cycle:
   [240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240, 240]

Information from  1  control channels found:
fs2:ramp_angsft_target
Information from  11  devices found
                      BldInfo:EBeam:             EBeamBld_V1 (14641)
            BldInfo:FEEGasDetEnergy:             FEEGasDetEnergy (14563)   Any (78)
             BldInfo:NH2-SB1-IPM-01:             SharedIpimb (14641)
                BldInfo:PhaseCavity:             PhaseCavity (14641)
     DetInfo:EpicsArch-0|NoDevice-0:             Epics_V1 (107580)
         DetInfo:NoDetector-0|Evr-0:             EvrConfig_V4 (62)   EvrData_V3 (14640)
        DetInfo:XppSb2Ipm-1|Ipimb-0:             IpimbConfig_V1 (1)   IpmFexConfig_V1 (1)   IpimbData_V1 (14640)   IpmFex_V1 (14640)
        DetInfo:XppSb3Ipm-1|Ipimb-0:             IpimbConfig_V1 (1)   IpmFexConfig_V1 (1)   IpimbData_V1 (14640)   IpmFex_V1 (14640)
        DetInfo:XppSb3Pim-1|Ipimb-0:             IpimbConfig_V1 (1)   IpmFexConfig_V1 (1)   IpimbData_V1 (14640)   IpmFex_V1 (14640)
        DetInfo:XppSb4Pim-1|Ipimb-0:             IpimbConfig_V1 (1)   IpmFexConfig_V1 (1)   IpimbData_V1 (14640)   IpmFex_V1 (14640)
                          ProcInfo::             RunControlConfig_V1 (62)
XtcScanner is done!
-------------------------------------------------------------

xtcexplorer

XTC Explorer - Old - GUI interface that builds pyana modules for you.

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kinit
addpkg XtcExplorer
scons
xtcexplorer /reg/d/psdm/xpp/xppi0310/xtc/e81-r0098-s0*

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These are packages that you may want to look into. Pretty much all our examples here are using them:

• http://numpy.scipy.org/^{Image Removed} - Numerical package for python (arrays etc)
• http://www.scipy.org/^{Image Removed} - Scientific tools
• http://matplotlib.sourceforge.net/^{Image Removed} - plotting package

Other useful links:

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

Saving data arrays

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import numpy as np

myarray = np.arange(0,100)
np.save( "output_file_name.npy", myarray) 
np.savetxt( "output_file_name.txt", myarray) 

import scipy.io 

N_array = np.arange(0,100)
x_array = np.random(100)
y_array = np.random(100)
scipy.io.savemat( "output_file_name.mat", mdict={'N': N_array, 'x' : x_array, 'y' : y_array } )

import h5py

ofile = h5py.File("output_file_name.h5",'w')
group = ofile.create_group("MyGroup")
group.create_dataset('delaytime',data=np.array(self.h_delaytime))
group.create_dataset('rawsignal',data=np.array(self.h_ipm_rsig))
group.create_dataset('normsignal',data=np.array(self.h_ipm_nsig))
ofile.close()

Plotting with MatPlotLib

One of the most commonly used tools for plotting in python: matplotlib. Other alternatives exist too.

Matplotlib:

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

• 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()

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However, the latest IPython has loads of new and interesting features...

http://ipython.org/^{Image Removed}

[ofte@psana0106 xpptutorial]$ ipython --pylab
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]: ipm3 = load('point_scan_delay.npy')

In [2]: ipm3.shape
Out[2]: (200, 3)

In [3]: ion()

In [4]: delay = ipm3[:,0]

In [5]: ipmraw = ipm3[:,1]

In [6]: ipmnorm = ipm3[:,2]

In [7]: plot(delay,ipmnorm,'ro')
Out[7]: [<matplotlib.lines.Line2D object at 0x59c4c10>]

In [9]: draw()

In [10]:

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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:

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To see some examples of how to fetch the various data types in pyana (or psana), look at Devices and Datatypes.

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For the following two examples, check out the latest version of the pyana_examples package:

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# 

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useful 

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Point detector delay scan

The python code for this pyana module resides in pyana_examples/src/xppt_delayscan.py.

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Open an editor and save the following in a file named pyana.cfg:

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[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):

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

addpkg pyana_examples HEAD
scons

Datatypes, and how to find data from your detector/device in the xtc file.

Pyana and psana has follows this naming scheme for labeling the datatypes from various devices. You can find the
names by investigating the xtc file with the above-mentioned tools (pyxtcreader, xtcscanner, xtcexplorer).
To see some examples of how to fetch the various data types in pyana (or psana), look at Devices and Datatypes.

Point detector delay scan

The python code for this pyana module resides in pyana_examples/src/xppt_delayscan.py.  In this example, we do a point detector delay scan, where we get the time as scan points via a control PV, and where time rebinning based on phase cavity measurement is used to improve the time resolution. One IPIMB device (a.k.a. IPM3) is used for normalization (i0, I Zero) (parameter name ipimb_norm) and another IPIMB device (a.k.a. PIM3) channel 1 is used as the signal (parameter name ipimb_sig).

[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

pyana -n 200 /reg/d/psdm/XPP/xppi0310/xtc/e81-r0098-s0*

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• 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
  none none
  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
  none none
  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
  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 )

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pyana -n 200 /reg/d/psdm/XPP/xppi0310/xtc/e81-r0098-s0*

Image peak finding

Here are a collection of useful algorithms for image analysis: http://docs.scipy.org/doc/scipy/reference/ndimage.html^{Image Removed}

The python code for this pyana module example resides in pyana_examples/src/xppt_image_analysis.py.
This particular example is done with a CSPad image, but only a single section is available. For more typical CSPad module, see next section.

[pyana]

modules = pyana_examples.xppt_image_analysis
#modules = pyana_examples.xppt_delayscan

[pyana_examples.xppt_image_analysis]
source = XppGon-0|Cspad-0
region = 127.3, 188.4, 95.1, 126.9

[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

pyana -n 10 /reg/d/psdm/XPP/xppi0112/xtc/e162-r0055-s00-c00.xtc

pyana -n 1 /reg/d/psdm/XPP/xppi0112/xtc/e162-r0055-s00-c00.xtc

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

  Code Block
  none none
  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
  none none
  # 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
  none none
  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
  none none
  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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• In beginjob, find out from the configuration object what part of the CSPad was in use (sometimes sections are missing):

  Code Block
  none none
  def beginjob ( self, evt, env ) : config = env.getConfig(xtc.TypeId.Type.Id_CspadConfig, self.source) if not config: print '*** cspad config object is missing ***' return quads = range(4) # memorize this list of sections for later self.sections = map(config.sections, quads)

• In each event, get the current CSPad data:

  Code Block
  none none
  def event(self, evt, env): elements = evt.getCsPadQuads(self.source,env) pixel_array = np.zeros((4,8,185,388), dtype="uint16") for element in elements: data = element.data() # the 3-dimensional data array (list of 2d sections) quad = element.quad() # current quadrant number (integer value) # if any sections are missing, insert zeros if len( data ) < 8 : zsec = np.zeros( (185,388), dtype=data.dtype) for i in range (8) : if i not in self.sections[quad] : data = np.insert( data, i, zsec, axis=0 ) pixel_array[quad] = data

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• For each Quadrant (cspad_layout.txt):

  Code Block
  none none
  def get_quad_image( self, data3d, qn) : """get_quad_image Get an image for this quad (qn) @param data3d 3d data array (row vs. col vs. section) @param qn quad number """ pairs = [] for i in range (8) : # 1) insert gap between asics in the 2x1 asics = np.hsplit( data3d[i], 2) gap = np.zeros( (185,3), dtype=data3d.dtype ) # # gap should be 3 pixels wide pair = np.hstack( (asics[0], gap, asics[1]) ) # all sections are originally 185 (rows) x 388 (columns) # Re-orient each section in the quad if i==0 or i==1 : pair = pair[:,::-1].T # reverse columns, switch columns to rows. if i==4 or i==5 : pair = pair[::-1,:].T # reverse rows, switch rows to columns pairs.append( pair ) if self.small_angle_tilt : pair = scipy.ndimage.interpolation.rotate(pair,self.tilt_array[qn][i]) # make the array for this quadrant quadrant = np.zeros( (850, 850), dtype=data3d.dtype ) # insert the 2x1 sections according to for sec in range (8): nrows, ncols = pairs[sec].shape # colp,rowp are where the top-left corner of a section should be placed rowp = 850 - self.sec_offset[0] - (self.section_centers[0][qn][sec] + nrows/2) colp = 850 - self.sec_offset[1] - (self.section_centers[1][qn][sec] + ncols/2) quadrant[rowp:rowp+nrows, colp:colp+ncols] = pairs[sec][0:nrows,0:ncols] return quadrant

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For how to find the correct constants for each experiment, look at the CSPad alignment CSPAD Alignment page.

Non-interactive batch analysis

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