Page History
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| Date | Detector | Gain mode | Photon source | Occupancy (photons/pixel) | Per-pixel histogram fit HDF5 | Experiment/Run | Deployed gain file | Comments |
|---|---|---|---|---|---|---|---|---|
| 2016-02-16 | DsaCsPad | high | Iron K-alpha (6.4keV) | 0.015 | /reg/g/psdm/detector/alignment/cspad/ calib-cxi-camera2-2016-02-05/calib/ CsPad::CalibV1/CxiDs2.0:Cspad.0/pixel_gain/ run16_DsaCsPad_flat_orig.h5 | cxi01516/16 | cxi01516-r0016-2016-02-18-FeKalpha.data | Success rate was low ~0.75%. Filled in the rest with average gain from each asic. |
| 2016-04-13 | DsaCsPad | high | Cu K-alpha (8.0keV) | 0.043 | /reg/g/psdm/detector/alignment/cspad/ calib-cxi-camera2-2016-02-05/calib/ CsPad::CalibV1/CxiDs2.0:Cspad.0/pixel_gain/ run6_DsaCsPad_flatIso.h5 | cxi00516/6 | run6_DsaCsPad_flatIso.data | Success rate was high ~0.99%, but there were some scattering in the centre, so filled that in with the average gain from each asic. Experiment: cxi00516 run6 |
| 2018-04 | DsaCsPad | Cu | cxilr6716 | gain_prelim.npy | From Derek Mendez: Its not perfect but seems to improve the data (pretty important for the correlations experiment). Interestingly in this camera there is also a lone quad that is off. The initial goal was to measure an intensity-dependent gain correction (hence the different transmissions), but for now I just use the average gain cal from these data. One such average-gain map is stored as a 3D numpy array (same shape array returned by det.raw(event) ), this is in *cxilt1417/scratch/gain/gain_prelim.npy* . the correction would be >> gain = np.load("gain_prelim.npy") >> assert ( np.all(gain!=0) ) >> data = det.calib(None) >> data /= gain |
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