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Images 1) for subtracted pedestals and 2) common mode correction algorithm #1:
Comparison of Unbonded vs. Default Common Mode
This is for a dark run, and was generated using this script:
| Code Block |
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import psana
from matplotlib import pyplot as plt
import numpy as np
dataset_name = "exp=cxil5316:run=1"
ds = psana.DataSource(dataset_name)
psana_det = psana.Detector("DscCsPad")
evt = ds.events().next()
pedestal = psana_det.pedestals(evt)
data = psana_det.raw_data(evt) - pedestal
data_cm = psana_det.raw_data(evt) - pedestal
unbond_cm = psana_det.common_mode_correction(evt, data_cm, [5, 50])
default_cm = psana_det.common_mode_correction(evt, data_cm, [1, 50, 10, 100])
unbond=unbond_cm[:,0,0]
default=default_cm[:,0,0]
plt.subplot(2,1,1)
plt.title('Default vs. Unbond Correction (ADU)')
plt.plot(unbond,default,'o')
plt.subplot(2,1,2)
plt.title('Default-Unbond Correction (ADU)')
plt.hist(default-unbond)
plt.show() |
Summary for CSPAD
Common mode correction for CSPAD works with algorith #1 and shows significant effect.
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