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• exp=sxrx22915:run=104
• Camp.0:pnCCD.1
• /reg/d/psdm/sxr/sxrx22915/calib/PNCCD::CalibV1/Camp.0:pnCCD.1/
• all plots for event 10

Raw image

Mask

mask = det.mask(evt, calib=True, status=True)

returns a combination of masks

• /reg/d/psdm/sxr/sxrx22915/calib/PNCCD::CalibV1/Camp.0:pnCCD.1/pixel_status/103-end.data
• /reg/d/psdm/sxr/sxrx22915/calib/PNCCD::CalibV1/Camp.0:pnCCD.1/pixel_mask/0-end.data

Image AddedImage Added

Pedestals

Gain

Corrected data

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Standard Detector correction (128 in rows)

raw - pedestal cm in rows(128) and columns (512) using mask for square regions

Image AddedImage Added

Last version of common mode correction for pnccd

• Bill requested to correct all pixels.
• For each group of pixels common mode is evaluated using pixels defined by mask (1), then applied to all pixels in the group.

Best pnccd image correction algorithm for sxrx22915

• raw data - pedestal
• apply gain factor
• apply common mode correction in rows (128 pixel), mode=1
• apply common mode correction in columns (512 pixel), mode=4
• apply mask from status (bad pixels only)

Summary

• Image for raw data has horizontal and vertical stripes
• pedestals and gain, averaged for many images (common mode is averaged to zero), have vertical strips ONLY!
• So, common mode effect gives strong contribution in rows (horizantal strips)
• Median algorithms gives bands in image depending on regions of image brightness
• Algorithm properly accounting masked pixel regions with large signal removes image artifacts.

References

• Common mode correction algorithms
• Common mode correction improvement for Epix100

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