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and the files have names like Cube_<expname>_Run<runnumber>_<cubename><optional _on/off>.h5

h5ls -r on an example file looks like this:

/Cfg__epix10ka2m__calib_mask Dataset {16, 352, 384}
/Cfg__epix10ka2m__gain   Dataset {7, 16, 352, 384}
/Cfg__epix10ka2m__ix     Dataset {16, 352, 384}/
Cfg__epix10ka2m__iy     Dataset {16, 352, 384}
/Cfg__epix10ka2m__mask   Dataset {16, 352, 384}
/Cfg__epix10ka2m__ped    Dataset {7, 16, 352, 384}
/Cfg__epix10ka2m__rms    Dataset {7, 16, 352, 384}
/Cfg__epix10ka2m__x      Dataset {16, 352, 384}
/Cfg__epix10ka2m__y      Dataset {16, 352, 384}
/binVar                  Dataset {13}
/binVar_bins             Dataset {13}
/cubeSelection           Dataset {1}
/epix10ka2m              Dataset {13, 16, 352, 384}
/ipm5__sum               Dataset {13}
/nEntries                Dataset {13}
/std_binVar_bins         Dataset {13}
/std_delay               Dataset {13}
/std_ipm5__sum           Dataset {13}

/Cfg__<detnames>__<parametername>

are calibration parameters for a detector. This will be added whenever detector data from the big xtc file is added.

/nEntries is somewhat self-explanatory

/binVar_bins are the bin boundaries for the binning variable.

/std__<varname> are the std calculated from all data points in the bin.

/ipm5__sum and /epix10ka2m are the main data: ipm5__sum is from the smalldata and in this case can be used for normalization of the incoming beam intensity. epix10ka2m is the full data for the epix10ka2m detector.

cubeSelection is a dataset stored for its attribute which is the text of the selection criteria used for the creating of this cube.

Advanced options (in progress)

Multi-dimensional binning

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Indices for the events in all dimensions will be created and turned into one big flat index after which xarray binning is used. Finally, the data is reshaped into the expected form.

Returning event-by-event data (from smallData

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ONLY when using SmallDataAna.makeCubeData)

ana.addToCube(<cubename>, targetVar, isIdxVar=True)

When variables are added to the cube is isIdxVars=True (default is False), in additional to the cube-dictionary, a second dictionary with list of values for each bin of the cube are returned. This only works for the SmallDataAna method, NOT the SmallDataAna_psana one. You can then treat the returned data further, e.g. by pic,ing one event for each bin out of the list and write out the data for those events. This is demonstrated in PickedEventsCube.pyIt is possible to do this.

Getting data from laser-off images closest in time for each cube slice

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