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| Cloak |
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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:
none
The ControlConfig object may contain several pvControl and pvMonitor objects. In this case
there's only one, but make sure the name matches anyway: none
- 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:
none
Use "XppSb3Ipm-1|Ipimb-0" (a.k.a. IPM3) sum of all channels for normalization and filtering
none
Use "XppSb3Pim-1|Ipimb-0" (a.k.a. PIM3) channel 1 as signal
none
Get the phase cavity:
none
Compute delay time and fill histograms
none
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Image peak finding
Here are a collection of useful algorithms for image analysis: http://docs.scipy.org/doc/scipy/reference/ndimage.html
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| Panel |
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Try some plotting of CSPad data using xtcexplorer. Launch the explorer and load xpp48712 run 66 (a dark run): | Code Block |
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xtcexplorer /reg/d/psdm/XPP/xpp48712/xtc/e153-r0066-s00-c00.xtc
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- *Look through a couple of events, then "Quit Pyana" and edit the configuration file. Add an output file name, and switch to "NoDisplay" and run 100 events to collect an average of dark images.
- With darks collected, load another file from the same experiment: run 141. Edit the pyana configuration file to use the file you just generated to subtract darks. Run the explorer in "SlideShow" mode again.
- Change the color scale of the plot by left and right clicking on the colorbar.
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Non-interactive batch analysis
Pyana jobs are designed to do batch analysis, but matplotlib plotting does not go well with this. If you want your job to produce graphics, make sure to use a matplotlib backend that writes the graphics directly to file, e.g. png files.
Multiprocessing
Pyana can make use of multiple core processing. On the command line, add the option '-p N' where N is the number of cores to use.
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