... | ... | @@ -32,16 +32,16 @@ Or a more complex one (comparing laser shot to preshot) at: |
|
|
|
|
|
The basic routines are:
|
|
|
|
|
|
#### open_run()
|
|
|
### open_run()
|
|
|
|
|
|
run = open_run(proposal, runNo, data='proc')\
|
|
|
The data=‘proc’ defines that we are reading the processed data, i.e. after subtraction of dark frames and after gain correction. For Jungfrau detector that means, that resulting data are already calibrated, and value of each pixel corresponds to deposited energy in \[keV\]. It is possible to read the ‘raw’ data as well; those could be available faster, but might be harder to interpret (not recommended).
|
|
|
|
|
|
#### get_shot_trainId()
|
|
|
### get_shot_trainId()
|
|
|
|
|
|
trainId=em.get_shot_trainId(run) - tries to find out which run contained the data with laser shot. Might need to be updated for new campaigns.
|
|
|
|
|
|
#### get_image()
|
|
|
### get_image()
|
|
|
|
|
|
jf=em.get_image(run,runNo,'JF1',trainId,debug,threshold_lower,recalc)
|
|
|
|
... | ... | @@ -57,7 +57,7 @@ The image is saved (cached) as tiff file into a ‘./data’ folder. From there, |
|
|
|
|
|
The **dictionary** with the diagnostics can be avoided and the source name provided directly. In that case, not trainId offset is used, i.e. Jungfrau_image=em.get_image(run,runNo,diag_nickname='whatever', diag=”HED_IA1_JF500K1/DET/JNGFR01:daqOutput”, trainId=trainId,debug=debug,recalc=recalc)
|
|
|
|
|
|
### Image calibration
|
|
|
#### Image calibration
|
|
|
Image calibration is now made only very simply for ePix 1. It works like that you load an array with the dark run into extra_mmm.ePix1_dark, then the code automatcially subtracts this image from all the ePix data.
|
|
|
Example:
|
|
|
```
|
... | ... | |