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         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})

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You can read the current

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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.

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