Event display / xtc file browser

Xtc files contain the raw data streamed from the DAQ online system, therefore they are not indexed and the events don't always line up in the "right" order. Therefore it's not straight-forward to browse (back and fourth) through an xtc file. This tool is also not a real browser, but allows a simple-to-run interface to the xtc files.

This tool is written in python, relying on PyQt4 for graphical user interface. The data processing is done via the pyana framework and visualization provided by matplotlib.

Note! This tool is under development... features are being added and new versions available often. This documentation might be slightly outdated, but the version mentioned here should work as advertised.

To run, you need to set up an offline release in your directory:

[user@psana0XXX ~] newrel ana-current myrelease
[user@psana0XXX ~] cd myrelease

Add the xtc browser package to your analysis release and "compile":

[user@psana0XXX myrelease] addpkg XtcEventBrowser V00-00-07
[user@psana0XXX myrelease] scons

Run the program with the command 'xtcbrowser' and optionally give the input xtc files that you want to read as arguments. You can also browse to find files after launching the browser.

[user@psana0XXX myrelease] xtcbrowser /reg/d/psdm/cxi/cxi80410/xtc/e55-r0581*

This will open a Gui. After opening file(s), click the "Quick Scan" button to scan the first 1000 events in the file. After scanning, a new window will pop up, allowing to select detectors/devices to make plots from. The plots are made via pyana. Configuration file for pyana will be generated automatically. To customize your analysis, you can edit the pyana config file and pyana files in XtcEventBrowser package to fit your need, then run pyana by itself (see the pyana section of confluence).

A few things to note about the different detectors:

• CsPad: CsPad data is reconstructed in pyana_cspad.py. To run this module by itself with pyana:

  pyana -m XtcEventBrowser/src/pyana_cspad.py <xtc files>
  

  Options must be specified in a configuration file, or the default values will be used:
  Unknown macro: {bq}

  image_source = CxiDs1-0|Cspad-0 # specify image source
  draw_each_event = 0 # if this is 1 (True), image is drawn for each event
  dark_img_file = my_darks.npy # Name of dark-image file (binary numpy array file) if you have one, or want to make one.
  collect_darks = 0 # This must be set to 1 if you want to create the dark-image file

XtcScanner

A rewrite of xtcsummary.py, to be usable as a library module for the event browser. Can be run just like the script above:

XtcScanner.py <filename(s)>

XtcBrowserMain.py

XtcBrowserMain is a class in the XtcEventBrowser package. It opens a GUI:

• Input xtc file name(s) via:
  • file browser
  • text input line
• Options to scan the files for contents (making use of XtcExplorer)
  • Quick scan reads only the first 1000 events (sufficient to see what detectors were in use)
  • Full scan of all files (needed to know how many events and calibration cycles were in the given run(s).
• Output from scan can be used to configure pyana. Checkboxes for each detector found.
• Run simple pyana analysis via a button
• Can be run from interactive ipython session
• (Should be able to) Return prompt to ipython to continue working on plots / redraw.
• Write pyana script for the simple analysis done by the Gui, which the user can modify to have more control and options for further analysis.

Main control widget. From here, select file(s) to browse, then select "Scan" or "Quick Scan" to provide a summary of the file.
Output of the Scan is currently text-only in the terminal or ipython window.
After the Scan, Checkboxes (will) allow you to select which sources you want to display data from. A button also allows you to run pyana. Here's an example output of beam energy by pyana.

Further analysis with pyana

Any serious data analysis will need more customized tools than we can provide in a GUI interface. This will require the user / analyst to program his/her own tools. Pyana is a complete framework for programming a user analysis in python. The Gui Event Browser can provide simple analysis code that can be expanded by the user. "Blank" analysis code can also be generated with Andy's codegen script (try codegen -h and codegen -p for options).

More information about pyana can be found on confluence.

Data visualization with NumPy (arrays) and MatPlotLib (plots).

Saving (and loading) a numpy array (e.g. image) to (from) a file

import numpy as np

# binary file .npy format
np.save("filename.npy", array)
array = np.load("filename.npy")

# txt file
np.savetxt("filename.dat", array)
array = loadtxt("filename.dat")

A comparison with MatLab.

%
%
%
a1 = subplot(121);
loglog(channels(:,1),channels(:,2),'o')
xlabel('CH0')
ylabel('CH1')
a2 = subplot(122);
loglog(channels(:,3),channels(:,4),'o')
xlabel('CH2')
ylabel('CH3')

import matplotlib.pyplot as plt
import numpy as np

a1 = plt.subplot(221)
plt.loglog(channels[:,0],channels[:,1], 'o' )
plt.xlabel('CH0')
plt.ylabel('CH1')
a2 = plt.subplot(222)
plt.loglog(channels[:,2],channels[:,3], 'o' )
plt.xlabel('CH2')
plt.ylabel('CH3')

axes(a1)
hold on
lims(1:2,:) = ginput(2);

axes(a2)
hold on
lims(3:4,:) = ginput(2);

plt.axes(a1)
plt.hold(True)
limslista = plt.ginput(2)

plt.axes(a2)
plt.hold(True)

limslistb = plt.ginput(2)
limsa = np.array(limslista)
limsb = np.array(limslistb)

lims = np.hstack( [limsa, limsb] )

fbool1 = (channels(:,1)>min(lims(1:2,1)))&(channels(:,1)<max(lims(1:2,1)))
fbool2 = (channels(:,2)>min(lims(1:2,2)))&(channels(:,2)<max(lims(1:2,2)));
fbool = fbool1&fbool2
loglog(channels(fbool,1),channels(fbool,2),'or')

fbool3 = (channels(:,3)>min(lims(3:4,3)))&(channels(:,3)<max(lims(3:4,3)))
fbool4 = (channels(:,4)>min(lims(3:4,4)))&(channels(:,4)<max(lims(3:4,4)));
fbool = fbool3&fbool4
loglog(channels(fbool,3),channels(fbool,4),'or') 

fbools0 = (channels[:,0]>lims[:,0].min())&(channels[:,0]<lims[:,0].max())
fbools1 = (channels[:,1]>lims[:,1].min())&(channels[:,1]<lims[:,1].max())
fbools = fbools0 & fbools1

fbools2 = (channels[:,2]>lims[:,2].min())&(channels[:,2]<lims[:,2].max())
fbools3 = (channels[:,3]>lims[:,3].min())&(channels[:,3]<lims[:,3].max())
fbools = fbools2&fbools3