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LavueController server allows for communication with LaVue GUI via tango interface, e.g. with user scripts or macros


Attributes

The user can pass to and read back from the LaVue GUI  the following attributes:

  • BeamCenterX: x-coordinate of the beam center in pixels, e.g. used in Angle/Q Tool
  • BeamCenterY: y-coordinate of the beam center in pixels, e.g. used in Angle/Q Tool
  • PixelSizeX: x-size of the detector pixel  in micrometers, e.g. used in Angle/Q Tool
  • PixelSizeY: y-size of the detector pixel  in micrometers, e.g. used in Angle/Q Tool
  • DetectorDistance: detector distance from the sample in mm, e.g. used in Angle/Q Tool
  • Energy:  beam energy in eV, e.g. used in Angle/Q Tool
  • DetectorROIs: JSON dictionary with  Regions Of Interests ranges, e.g.
    {"pilatusrois": [[67, 131, 124, 153], [67, 69, 117, 119], [134, 129, 184, 179], [125, 72, 175, 122]]}

Moreover the user can read from the LaVue GUI:

  • DetectorROIsValues: JSON dictionary with Regions Of Interests sums, e.g.
    {"pilatusrois": [8167.0, 2262.0, 478.0, 1069.0]}
  • DetectorROIsParams: JSON list of image transformations performed by lavue, e.g.
    ["transpose", "flip-left-right", "flip-up-down"]
  • ToolResults: JSON dictionary with tool results, i.e. 1d diffractogram plot or position of peaks

Finally, the user can change state of lavue by writing to

  • LavueState: JSON dictionary with lavue configuration with parameters corresponding to command-line parameters of lavue  (to display them: lavue -h). The currently supported commands are:  source, configuration, start, stop, imagefile, offset, rangewindow, dsfactor, dsreduction, filters, mbuffer, maskfile, maskhighvalue, transformation, scaling, levels, autofactor, gradient, viewrange, tool, toolconfig, tangodevice, doordevice, analysisdevice, log.

             e.g.

         import tango
         import json

         lc = tango.DeviceProxy("p09/lavuecontroller/1")

         # start the Test source
         lc.LavueState = json.dumps({"source": "test", "start": True})

         # stop an image source
         lc.LavueState = json.dumps({"stop":True})

         # set the hidra source with a server configured
         lc.LavueState = json.dumps({"source": "hidra", "configuration": "haspilatus1m.desy.de"})

         # start Tango Attribute image source with a tango attribute from TangoTest server
         # lc.LavueState = json.dumps({"source": "tangoattr", "configuration": "sys/tg_test/1/double_image_ro", "start": True})

         # set ROI tool in the tool combobox
         lc.LavueState = json.dumps({"tool": "roi"})

         # set display intensity levels
         lc.LavueState = json.dumps({"levels": "m20,20"})

         # set level auto factor
         lc.LavueState = json.dumps({"autofactor": 1})

# set auto levels
lc.LavueState = json.dumps({"autofactor": ""})
# set mask values above lc.LavueState = json.dumps({"maskhighvalue": 30000}) # set flip-up-down transformation lc.LavueState = json.dumps({"transformation": "flip-up-down"}) # set log intensity scaling lc.LavueState = json.dumps({"scaling": "log"}) # set eiger image source with configuration defined by alias "eiger" # (otherwise you have to set as configuration the whole URL string) lc.LavueState = json.dumps({"source":"http", "configuration":"eiger", "start":True})

You can read the current LaVue state via LavueState. To update the viewrange parameter write  to LavueState  an empty JSON dictionary or {"__update__": true}.

Properties

The DynamicROIsValues device property can be set to true or false. When it is true dynamic attributes with ROIs sums are added.

They names are defined by ROIs aliases from Lavue GUI.

The DynamicROIs device property can be set to true or false. When it is true dynamic attributes with ROIs bounds are added.

They names are defined by ROIs aliases from Lavue GUI.

The ROIAttributesNames device property contains names of dynamic attributes which will be created event if the corresponding ROIs aliases in Lavue GUI are missing.

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