From d1c66c32308e696ae9fe8161198a6febf744145d Mon Sep 17 00:00:00 2001
From: Laurent Mercadier <laurent.mercadier@xfel.eu>
Date: Thu, 23 Apr 2020 09:59:30 +0200
Subject: [PATCH] calibrateXGMs(): attempts to load slowTrain data and adds
plot
---
xgm.py | 62 ++++++++++++++++++++++++++++++++++++++--------------------
1 file changed, 41 insertions(+), 21 deletions(-)
diff --git a/xgm.py b/xgm.py
index edade0b..ecb78ef 100644
--- a/xgm.py
+++ b/xgm.py
@@ -234,29 +234,49 @@ def calibrateXGMs(data, rollingWindow=200, plot=False):
factors: numpy ndarray of shape 1 x 2 containing
[XTD10 calibration factor, SCS calibration factor]
'''
- XTD10_factor = np.nan
- SCS_factor = np.nan
- if "XTD10_slowTrain" in data or "SCS_slowTrain" in data:
- if "XTD10_slowTrain" in data:
- XTD10_factor = np.mean(data.XTD10_photonFlux/data.XTD10_slowTrain)
-
- else:
- print('no XTD10 XGM data. Skipping calibration for XTD10 XGM')
- if "SCS_slowTrain" in data:
- #XTD10_SA3_contrib = data.XTD10_slowTrain_SA3 * data.npulses_sase3 / (
- # data.XTD10_slowTrain * (data.npulses_sase3+data.npulses_sase1))
- #SCS_SA3_SLOW = data.SCS_photonFlux*(data.npulses_sase3+
- # data.npulses_sase1)*XTD10_SA3_contrib/data.npulses_sase3
- #SCS_factor = np.mean(SCS_SA3_SLOW/data.SCS_slowTrain_SA3)
- SCS_factor = np.mean(data.SCS_photonFlux/data.SCS_slowTrain)
+ noSlowTrain=False
+ results = np.array([np.nan, np.nan], dtype=float)
+ slowTrainTraces = []
+ for i,whichXgm in enumerate(['XTD10', 'SCS']):
+ if f'{whichXgm}_slowTrain' not in data:
+ mnemo = tb.mnemonics[f'{whichXgm}_slowTrain']
+ if mnemo['key'] in data.attrs['run'].keys_for_source(mnemo['source']):
+ slowTrain = data.attrs['run'].get_array(mnemo['source'], mnemo['key'])
+ else:
+ noSlowTrain=True
+ print('No averages of fast data (slowTrain) available.'+
+ ' Attempting calibration from fast data.')
+ break
else:
- print('no SCS XGM data. Skipping calibration for SCS XGM')
-
- #TODO: plot the results of calibration
- return np.array([XTD10_factor, SCS_factor])
- else:
+ slowTrain = data[f'{whichXgm}_slowTrain']
+ results[i] = np.mean(data[f'{whichXgm}_photonFlux']/slowTrain)
+ slowTrainTraces.append(slowTrain)
+ if noSlowTrain:
return calibrateXGMsFromAllPulses(data, rollingWindow, plot)
-
+ else:
+ if plot:
+ plt.figure(figsize=(8,4))
+ plt.subplot(211)
+ plt.plot(data['XTD10_photonFlux'], label='XTD10 photon flux')
+ plt.plot(slowTrainTraces[0]*results[0], label='calibrated XTD10 fast signal')
+ plt.ylabel(r'Energy [$\mu$J]')
+ plt.xlabel('train Id')
+ plt.legend(fontsize=8, loc='upper left')
+ plt.twinx()
+ plt.plot(slowTrainTraces[0], label='uncalibrated XTD10 fast signal', color='C4')
+ plt.ylabel(r'Uncalibrated energy')
+ plt.legend(fontsize=8, loc='upper right')
+ plt.subplot(212)
+ plt.plot(data['SCS_photonFlux'], label='SCS photon flux')
+ plt.plot(slowTrainTraces[1]*results[1], label='calibrated SCS fast signal')
+ plt.ylabel(r'Energy [$\mu$J]')
+ plt.xlabel('train Id')
+ plt.legend(fontsize=8, loc='upper left')
+ plt.twinx()
+ plt.plot(slowTrainTraces[1], label='uncalibrated SCS fast signal', color='C4')
+ plt.ylabel(r'Uncalibrated energy')
+ plt.legend(fontsize=8, loc='upper right')
+ return results
def calibrateXGMsFromAllPulses(data, rollingWindow=200, plot=False):
''' Calibrate the fast (pulse-resolved) signals of the XTD10 and SCS XGM
--
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