Page History
...
This is done mostly in the block in the SmallDataProducer smallssta_producer file below (<...>/smalldata_tools/examples/SmallDataProducersmalldata_producer.py).
| Code Block |
|---|
########################################################## ## ## User Input start --> ## ########################################################## ########################################################## # run independent parameters ########################################################## #aliases for experiment specific PVs go here epicsPV = ['s1h_w'] #tt calibration parameters (if passing None, init will use values used during recording ttCalibPars = None # (or [] or [p0, p1, p2] #aioParams=[[1],['laser'],[1.],[0.]] aioParams=[] ########################################################## ## ## <-- User Input end ## ########################################################## |
| Anchor | ||||
|---|---|---|---|---|
|
Should you have user motors e.g. for sample motion for which you would like to save the position or if you are using a lakeshore for temperature control or have other remotely controlled data you would like to save, you will need to add those to the epicsPV line. For the exact names, ask the beam line staff. Please note that 's1h_w' is only an example/placeholder. You can get the list of possible EPICS variables names/aliases by using
"detnames exp=<expname>:run=<run#> -e"
This will only work when you have the analysis environment setup by calling:
source /reg/g/psdm/etc/psconda.sh
Please note that these PVs will appear in event event, but they are NOT time matched. The time values are close, but can differ by up to second, usually it's better than that, but it is not reliable.
| Anchor | ||||
|---|---|---|---|---|
|
If we take a tt calibration run, we obtain a set of parameters for the conversion of the peak position on the timetool in pixel to ps. This should then be used in the DAQ and nothing needs to be done. Should you realize that this steps has been missed for at least some of the data or have you extracted better parameters, you can put the obtained parameters into the ttCalibPars parameter. They will be used for any new productions (e.g. for reprocessing of runs taken before the calibration was in place). If it is set to "None" or an empty list, the calibration saved in the data is used.
| Anchor | ||||
|---|---|---|---|---|
|
Values read in from our analog inputs are timestamped at 120Hz and saved in the data using the channel name. You can select channels to be saved, give them an alias and possibly apply a (linear) conversion.
aioParams=[[1],['laser'],[1.],[0.]]
First argument (necessary if using): list of channel numbers of interest (from 0-15)
Second: argument (necessary if using): list of channel names
Third: argument (optional, defaults to 1.): conversion factor
Fourth: argument (optional, defaults to 0.): offset
...
If you have been told you might have to reanalyze the time tool data or you have past experience and feel you can gain by doing so, you can add the event-by-event trace of the OPAL camera used for the time tool like this:
| Code Block |
|---|
##########################################################
## User Input start -->
##########################################################
<UserData inputs described elsewhere>
##adding raw timetool traces:
#defaultDets.append(ttRawDetector(env=ds.env()))
##adding wave8 traces:
#defaultDets.append(wave8Detector('Wave8WF'))
##########################################################
## <-- User Input end
##########################################################
|
Configuring the small data - addition of the time tool analysis 
Configuring the small data - addition of XTCAV values
Overview
Content Tools