Content

Data sample for test

xcs01116-r0144 event 10

Data with subtracted pedestals

ped = det.pedestals(run)

data_ped = det.raw(evt)-ped

This data has structure which can be corrected.

Old version of algorithm

EPIX100A has an option to turn on up to 3 regions for common mode correction, controlled by the bitword parameter #2: 

• bit#1 - common mode for 352x96-pixel 16 banks,   
• bit#2 - common mode for 96-pixel rows in 16 banks,   
• bit#3 - common mode for 352-pixel columns in 16 banks

det.common_mode_apply(run, data_calib6, cmpars=(4,6,30,10))

New development

Silke Nelson has implemented similar algorithm in python, which visually demonstrates better performance

Implementation in ImgAlgos

Apparently the difference is that np.median() method in python returns median as a float value. I our old median is integer - histogram bin number associated with integer ADU which is closest to the halh histogram statistics.

We apply float correction to integer median position.

det.common_mode_apply(run, data_calib6, cmpars=(4,6,30,10))

This algorithm reproduces result of Silke.

Check for other options of common mode algorithm

cmpars=(4,1,30,10)

bit#1 - common mode for 352x96-pixel 16 banks 

cmpars=(4,2,30,10)

 bit#2 - common mode for 96-pixel rows in 16 banks

cmpars=(4,4,30,10)

 bit#3 - common mode for 352-pixel columns in 16 banks

cmpars=(4,7,30,10)

combination of all three corrections

Summary

Sufficient common mode correction can be acheived with parameters cmpars=(4,6,30,10)

References

• Common mode correction algorithms (Algorithms for Epix100)
• Detector.AreaDetector