This is a page describing work in progress. Once it is usable to the general community, this documentation will be moved to the Users Data Analysis section.

Event browser for Xtc files

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 This page summarizes initial attempts at an event browser or simple plotting tool for Xtc files that should be easy to use. It 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.

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

[user@psana0X ~] newrel ana-current myrelease
[user@psana0X ~] cd myrelease

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

[user@psana0X myrelease] addpkg XtcEventBrowser V00-00-06
[user@psana0X myrelease] scons

Xtc file reader: xtcsummary.py

Text output listing the contents of an xtc file. Similar to pyxtcreader, but instead of listing all datagrams, it loops through and prints only a summary.

...

xtcsummary.py <filename(s)>

XtcExplorer

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

XtcExplorer.py <filename(s)>

LclsXtcEventBrowser.py

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

...

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.

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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