In order to keep the filesizes small to avoid issue when analysis the smallData files, we try to extract the important information gleaned from areaDetectors at an event-by-event basis and only save these pieces of data.

Analysis functions

The following functions are readily available for use in smalldata_tools:

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The funtions definition can be found in <smalldata_tools>/smalldata_tools/ana_funcs, in case one want to take a close look at how the data are processed. These analysis functions are sub-classes of DetObjectFunc and the computation are done in the custom process method defined in each class.

Comments on the masks for area detectors

Each area detector loaded in smalldata_tools has the following psana masks defined:

• self.statusMask = self.det.mask(self.run, status=True): only accounts for the pixel status

• self.mask = self.det.mask(self.run, unbond=True, unbondnbrs=True, status=True,  edges=True, central=True): accounts for the geometry pixels

• self.cmask = self.det.mask(self.run, unbond=True, unbondnbrs=True, status=True,  edges=True, central=True,calib=True): generally the mask that fits most needs

Definitions of the psana mask's options:

• calib : bool - True/False = on/off mask from calib directory.
• status : bool - True/False = on/off mask generated from calib pixel_status.
• edges : bool - True/False = on/off mask of edges.
• central : bool - True/False = on/off mask of two central columns.
• unbond : bool - True/False = on/off mask of unbonded pixels.
• unbondnbrs : bool - True/False = on/off mask of unbonded pixel with four neighbors.
• unbondnbrs8: bool - True/False = on/off mask of unbonded pixel with eight neighbors.

Deprecated

Typical forms of userData

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