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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 This documentation might be slightly outdated, but the version mentioned here should work as advertised.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:
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[user@psana0XXX myrelease] addpkg XtcEventBrowser V00-00-0607 [user@psana0XXX myrelease] scons |
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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:
Options must be specified in a configuration file, or the default values will be used.Code Block pyana -m XtcEventBrowser/src/pyana_cspad.py <xtc files>
Image source should be specified with e.g. "image_source = CxiDs1-0|Cspad-0".
The module gives the option of drawing each event (by default off) by setting "draw_each_event = 1".
You can also draw images background subtracted if you supply a numpy array file with a dark image: "dark_img_file = my_darks.npy".
If you have a run with dark images you can create the dark image file by setting the dark image file name and setting "collect_darks = 1".
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:
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MatLab | MatPlotLib | Comments | |||||
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Loglog plot of one array vs. another
| Loglog plot of one array vs. another
| channels is a 4xN array of floats, where N is the number of events. Each column corresponds to one out of four Ipimb channels. | ]]></ac:plain-text-body></ac:structured-macro> | ||||
test | test | Test | |||||
array of limits from graphical input | array of limits from graphical input |
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filter | filter |
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