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Commit 6d4e31e9 authored by Laurent Mercadier's avatar Laurent Mercadier
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Merge branch 'tim_calibration_table' into 'master' 

Tim calibration table

See merge request SCS/ToolBox!11
parents 5cffbe80 29d74c4d
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xgm.py
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...@@ -195,7 +195,7 @@ def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM'): ...@@ -195,7 +195,7 @@ def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM'):
return result return result
def calcContribSASE(data, sase='sase1', xgm='SA3_XGM'): def saseContribution(data, sase='sase1', xgm='SA3_XGM'):
''' Calculate the relative contribution of SASE 1 or SASE 3 pulses ''' Calculate the relative contribution of SASE 1 or SASE 3 pulses
for each train in the run. Supports fresh bunch, dedicated trains for each train in the run. Supports fresh bunch, dedicated trains
and pulse on demand modes. and pulse on demand modes.
...@@ -211,13 +211,11 @@ def calcContribSASE(data, sase='sase1', xgm='SA3_XGM'): ...@@ -211,13 +211,11 @@ def calcContribSASE(data, sase='sase1', xgm='SA3_XGM'):
''' '''
xgm_sa1 = selectSASEinXGM(data, 'sase1', xgm=xgm) xgm_sa1 = selectSASEinXGM(data, 'sase1', xgm=xgm)
xgm_sa3 = selectSASEinXGM(data, 'sase3', xgm=xgm) xgm_sa3 = selectSASEinXGM(data, 'sase3', xgm=xgm)
if np.all(xgm_sa1.trainId.isin(xgm_sa3.trainId).values) == False: #Fill missing train ids with 0
print('Dedicated mode') r = xr.align(*[xgm_sa1, xgm_sa3], join='outer', exclude=['XGMbunchId'])
r = xr.align(*[xgm_sa1, xgm_sa3], join='outer', exclude=['SA3_XGM_dim', 'SA1_XGM_dim']) xgm_sa1 = r[0].fillna(0)
xgm_sa1 = r[0] xgm_sa3 = r[1].fillna(0)
xgm_sa1.fillna(0)
xgm_sa3 = r[1]
xgm_sa3.fillna(0)
contrib = xgm_sa1.sum(axis=1)/(xgm_sa1.sum(axis=1) + xgm_sa3.sum(axis=1)) contrib = xgm_sa1.sum(axis=1)/(xgm_sa1.sum(axis=1) + xgm_sa3.sum(axis=1))
if sase=='sase1': if sase=='sase1':
return contrib return contrib
...@@ -300,7 +298,7 @@ def calibrateXGMs(data, rollingWindow=200, plot=False): ...@@ -300,7 +298,7 @@ def calibrateXGMs(data, rollingWindow=200, plot=False):
print('calibration factor SA3 XGM: %f'%sa3_calib_factor) print('calibration factor SA3 XGM: %f'%sa3_calib_factor)
# Calibrate SCS XGM with SASE3-only contribution # Calibrate SCS XGM with SASE3-only contribution
sa3contrib = calcContribSASE(data, 'sase3', 'SA3_XGM') sa3contrib = saseContribution(data, 'sase3', 'SA3_XGM')
if not noSCS: if not noSCS:
scs_sase3_fast = selectSASEinXGM(data, 'sase3', 'SCS_XGM').mean(axis=1) scs_sase3_fast = selectSASEinXGM(data, 'sase3', 'SCS_XGM').mean(axis=1)
meanFast = scs_sase3_fast.rolling(trainId=rollingWindow).mean() meanFast = scs_sase3_fast.rolling(trainId=rollingWindow).mean()
...@@ -343,7 +341,7 @@ def calibrateXGMs(data, rollingWindow=200, plot=False): ...@@ -343,7 +341,7 @@ def calibrateXGMs(data, rollingWindow=200, plot=False):
return np.array([sa3_calib_factor, scs_calib_factor]) return np.array([sa3_calib_factor, scs_calib_factor])
def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, t_offset=1760, mcp=1, npulses=None): def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, mcp=1, t_offset=None, npulses=None):
''' Computes peak integration from raw MCP traces. ''' Computes peak integration from raw MCP traces.
Inputs: Inputs:
...@@ -352,21 +350,32 @@ def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, t_offset=1760, mcp=1, n ...@@ -352,21 +350,32 @@ def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, t_offset=1760, mcp=1, n
intstop: trace index of integration stop intstop: trace index of integration stop
bkgstart: trace index of background start bkgstart: trace index of background start
bkgstop: trace index of background stop bkgstop: trace index of background stop
t_offset: index separation between two pulses
mcp: MCP channel number mcp: MCP channel number
npulses: number of pulses t_offset: index separation between two pulses. If None, checks the
pulse pattern and determine the t_offset assuming mininum pulse
separation of 220 ns and digitizer resolution of 2 GHz.
npulses: number of pulses. If None, takes the maximum number of
pulses according to the bunch patter (field 'npulses_sase3')
Output: Output:
results: DataArray with dims trainId x max(sase3 pulses)*1MHz/intra-train rep.rate results: DataArray with dims trainId x max(sase3 pulses)
''' '''
keyraw = 'MCP{}raw'.format(mcp) keyraw = 'MCP{}raw'.format(mcp)
if keyraw not in data: if keyraw not in data:
raise ValueError("Source not found: {}!".format(keyraw)) raise ValueError("Source not found: {}!".format(keyraw))
if npulses is None: if npulses is None:
npulses = int((data['sase3'].max().values + 1)/4) npulses = int(data['npulses_sase3'].max().values)
sa3 = data['sase3'].where(data['sase3']>1)/4 sa3 = data['sase3'].where(data['sase3']>1)
sa3 -= sa3[:,0] if t_offset is None:
if npulses > 1:
#Calculate the number of pulses between two lasing pulses (step)
step = sa3.where(data['npulses_sase3']>1, drop=True)[0,:2].values
step = int(step[1] - step[0])
#multiply by elementary samples length (220 ns @ 2 GHz = 440)
t_offset = 440 * step
else:
t_offset = 1
results = xr.DataArray(np.empty((sa3.shape[0], npulses)), coords=sa3.coords, results = xr.DataArray(np.empty((sa3.shape[0], npulses)), coords=sa3.coords,
dims=['trainId', 'MCP{}fromRaw'.format(mcp)]) dims=['trainId', 'MCP{}fromRaw'.format(mcp)])
for i in range(npulses): for i in range(npulses):
...@@ -380,7 +389,7 @@ def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, t_offset=1760, mcp=1, n ...@@ -380,7 +389,7 @@ def mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, t_offset=1760, mcp=1, n
def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None, def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
bkgstart=None, bkgstop=None, t_offset=1760, npulses=None): bkgstart=None, bkgstop=None, t_offset=None, npulses=None):
''' Extract peak-integrated data from TIM where pulses are from SASE3 only. ''' Extract peak-integrated data from TIM where pulses are from SASE3 only.
If use_apd is False it calculates integration from raw traces. If use_apd is False it calculates integration from raw traces.
The missing values, in case of change of number of pulses, are filled The missing values, in case of change of number of pulses, are filled
...@@ -399,19 +408,36 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None, ...@@ -399,19 +408,36 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
tim: DataArray of shape trainId only for SASE3 pulses x N tim: DataArray of shape trainId only for SASE3 pulses x N
with N=max(number of pulses per train) with N=max(number of pulses per train)
''' '''
key = 'MCP{}apd'.format(mcp) sa3 = data['sase3'].where(data['sase3']>1, drop=True)
npulses_sa3 = data['npulses_sase3']
maxpulses = int(npulses_sa3.max().values)
step = 1
if maxpulses > 1:
#Calculate the number of non-lasing pulses between two lasing pulses (step)
step = sa3.where(data['npulses_sase3']>1, drop=True)[0,:2].values
step = int(step[1] - step[0])
if use_apd: if use_apd:
apd = data[key] apd = data['MCP{}apd'.format(mcp)]
initialDelay = data.attrs['run'].get_array(
'SCS_UTC1_ADQ/ADC/1', 'board1.apd.channel_0.initialDelay.value')[0].values
upperLimit = data.attrs['run'].get_array(
'SCS_UTC1_ADQ/ADC/1', 'board1.apd.channel_0.upperLimit.value')[0].values
nsamples = upperLimit - initialDelay
npulses_per_apd = int(nsamples/440)
sa3 /= npulses_per_apd
else: else:
apd = mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, t_offset, mcp, npulses) apd = mcpPeaks(data, intstart, intstop, bkgstart, bkgstop, mcp=mcp,
npulses_sa3 = data['npulses_sase3'] t_offset=t_offset, npulses=npulses)
sa3 = data['sase3'].where(data['sase3']>1, drop=True)/4 sa3 /= step
sa3 -= sa3[:,0] sa3 -= sa3[:,0]
sa3 = sa3.astype(int) sa3 = sa3.astype(int)
if np.all(npulses_sa3 == npulses_sa3[0]): if np.all(npulses_sa3 == npulses_sa3[0]):
tim = apd[:, sa3[0].values] tim = apd[:, sa3[0].values]
return tim return tim
maxpulses = int(npulses_sa3.max().values)
stride = 1
if use_apd:
stride = np.max([stride,int(step/npulses_per_apd)])
diff = npulses_sa3.diff(dim='trainId') diff = npulses_sa3.diff(dim='trainId')
#only keep trainIds where a change occured: #only keep trainIds where a change occured:
diff = diff.where(diff != 0, drop=True) diff = diff.where(diff != 0, drop=True)
...@@ -429,7 +455,7 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None, ...@@ -429,7 +455,7 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
else: else:
l = idx_change[i+1] l = idx_change[i+1]
b = npulses_sa3[idx].values b = npulses_sa3[idx].values
temp = apd[idx:l,:maxpulses].copy() temp = apd[idx:l,:maxpulses*stride:stride].copy()
temp[:,b:] = np.NaN temp[:,b:] = np.NaN
if tim is None: if tim is None:
tim = temp tim = temp
...@@ -438,8 +464,8 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None, ...@@ -438,8 +464,8 @@ def getTIMapd(data, mcp=1, use_apd=True, intstart=None, intstop=None,
return tim return tim
def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intstart=None, intstop=None, def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intstart=None,
bkgstart=None, bkgstop=None, t_offset=1760, npulses_apd=None): intstop=None, bkgstart=None, bkgstop=None, t_offset=None, npulses_apd=None):
''' Calibrate TIM signal (Peak-integrated signal) to the slow ion signal of SCS_XGM ''' Calibrate TIM signal (Peak-integrated signal) to the slow ion signal of SCS_XGM
(photocurrent read by Keithley, channel 'pulseEnergy.photonFlux.value'). (photocurrent read by Keithley, channel 'pulseEnergy.photonFlux.value').
The aim is to find F so that E_tim_peak[uJ] = F x TIM_peak. For this, we want to The aim is to find F so that E_tim_peak[uJ] = F x TIM_peak. For this, we want to
...@@ -468,7 +494,6 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst ...@@ -468,7 +494,6 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst
bkgstart: trace index of background start bkgstart: trace index of background start
bkgstop: trace index of background stop bkgstop: trace index of background stop
t_offset: index separation between two pulses t_offset: index separation between two pulses
mcp: MCP channel number
npulses_apd: number of pulses npulses_apd: number of pulses
Output: Output:
...@@ -486,9 +511,8 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst ...@@ -486,9 +511,8 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst
print('not enough consecutive data points with the largest number of pulses per train') print('not enough consecutive data points with the largest number of pulses per train')
start += rollingWindow start += rollingWindow
stop = np.min((ntrains, stop+rollingWindow)) stop = np.min((ntrains, stop+rollingWindow))
#print(start, stop)
filteredTIM = getTIMapd(data, mcp, use_apd, intstart, intstop, bkgstart, bkgstop, t_offset, npulses_apd) filteredTIM = getTIMapd(data, mcp, use_apd, intstart, intstop, bkgstart, bkgstop, t_offset, npulses_apd)
sa3contrib = calcContribSASE(data, 'sase3', 'SA3_XGM') sa3contrib = saseContribution(data, 'sase3', 'SA3_XGM')
avgFast = filteredTIM.mean(axis=1).rolling(trainId=rollingWindow).mean() avgFast = filteredTIM.mean(axis=1).rolling(trainId=rollingWindow).mean()
ratio = ((data['npulses_sase3']+data['npulses_sase1']) * ratio = ((data['npulses_sase3']+data['npulses_sase1']) *
data['SCS_XGM_SLOW'] * sa3contrib) / (avgFast*data['npulses_sase3']) data['SCS_XGM_SLOW'] * sa3contrib) / (avgFast*data['npulses_sase3'])
...@@ -505,9 +529,7 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst ...@@ -505,9 +529,7 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst
ax.plot(avgFast*F, label='Calibrated TIM rolling avg', color='C2') ax.plot(avgFast*F, label='Calibrated TIM rolling avg', color='C2')
ax.legend(loc='upper left', fontsize=8) ax.legend(loc='upper left', fontsize=8)
ax.set_ylabel('Energy [$\mu$J]', size=10) ax.set_ylabel('Energy [$\mu$J]', size=10)
#ax2=ax#.twinx()
ax.plot(filteredTIM.mean(axis=1)*F, label='Calibrated TIM train avg', alpha=0.2, color='C9') ax.plot(filteredTIM.mean(axis=1)*F, label='Calibrated TIM train avg', alpha=0.2, color='C9')
#ax2.set_ylabel('Calibrated TIM (MCP{}) [uJ]'.format(mcp))
ax.legend(loc='best', fontsize=8, ncol=2) ax.legend(loc='best', fontsize=8, ncol=2)
plt.xlabel('train in run') plt.xlabel('train in run')
...@@ -530,10 +552,6 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst ...@@ -530,10 +552,6 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst
ax.hist(filteredTIM.values.flatten()*F, bins=50, rwidth=0.8) ax.hist(filteredTIM.values.flatten()*F, bins=50, rwidth=0.8)
ax.set_ylabel('number of pulses', size=10) ax.set_ylabel('number of pulses', size=10)
ax.set_xlabel('Pulse energy MCP{} [uJ]'.format(mcp), size=10) ax.set_xlabel('Pulse energy MCP{} [uJ]'.format(mcp), size=10)
#ax2 = ax.twiny()
#ax2.set_xlabel('MCP 1 APD')
#toRaw = lambda x: x/F
#ax2.set_xlim((toRaw(ax.get_xlim()[0]),toRaw(ax.get_xlim()[1])))
ax.set_yscale('log') ax.set_yscale('log')
ax = plt.subplot(236) ax = plt.subplot(236)
...@@ -565,3 +583,64 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst ...@@ -565,3 +583,64 @@ def calibrateTIM(data, rollingWindow=200, mcp=1, plot=False, use_apd=True, intst
plt.tight_layout() plt.tight_layout()
return F return F
''' TIM calibration table
Dict with key= photon energy and value= array of polynomial coefficients for each MCP (1,2,3).
The polynomials correspond to a fit of the logarithm of the calibration factor as a function
of MCP voltage. If P is a polynomial and V the MCP voltage, the calibration factor (in microjoule
per APD signal) is given by -exp(P(V)).
This table was generated from the calibration of March 2019, proposal 900074, semester 201930,
runs 69 - 111 (Ni edge): https://in.xfel.eu/elog/SCS+Beamline/2323
runs 113 - 153 (Co edge): https://in.xfel.eu/elog/SCS+Beamline/2334
runs 163 - 208 (Fe edge): https://in.xfel.eu/elog/SCS+Beamline/2349
'''
tim_calibration_table = {
705.5: np.array([
[-6.85344690e-12, 5.00931986e-08, -1.27206912e-04, 1.15596821e-01, -3.15215367e+01],
[ 1.25613942e-11, -5.41566381e-08, 8.28161004e-05, -7.27230153e-02, 3.10984925e+01],
[ 1.14094964e-12, 7.72658935e-09, -4.27504907e-05, 4.07253378e-02, -7.00773062e+00]]),
779: np.array([
[ 4.57610777e-12, -2.33282497e-08, 4.65978738e-05, -6.43305156e-02, 3.73958623e+01],
[ 2.96325102e-11, -1.61393276e-07, 3.32600044e-04, -3.28468195e-01, 1.28328844e+02],
[ 1.14521506e-11, -5.81980336e-08, 1.12518434e-04, -1.19072484e-01, 5.37601559e+01]]),
851: np.array([
[ 3.15774215e-11, -1.71452934e-07, 3.50316512e-04, -3.40098861e-01, 1.31064501e+02],
[5.36341958e-11, -2.92533156e-07, 6.00574534e-04, -5.71083140e-01, 2.10547161e+02],
[ 3.69445588e-11, -1.97731342e-07, 3.98203522e-04, -3.78338599e-01, 1.41894119e+02]])
}
def timFactorFromTable(voltage, photonEnergy, mcp=1):
''' Returns an energy calibration factor for TIM integrated peak signal (APD)
according to calibration from March 2019, proposal 900074, semester 201930,
runs 69 - 111 (Ni edge): https://in.xfel.eu/elog/SCS+Beamline/2323
runs 113 - 153 (Co edge): https://in.xfel.eu/elog/SCS+Beamline/2334
runs 163 - 208 (Fe edge): https://in.xfel.eu/elog/SCS+Beamline/2349
Uses the tim_calibration_table declared above.
Inputs:
voltage: MCP voltage in volts.
photonEnergy: FEL photon energy in eV. Calibration factor is linearly
interpolated between the known values from the calibration table.
mcp: MCP channel (1, 2, or 3).
Output:
f: calibration factor in microjoule per APD signal
'''
energies = np.sort([key for key in tim_calibration_table])
if photonEnergy not in energies:
if photonEnergy > energies.max():
photonEnergy = energies.max()
elif photonEnergy < energies.min():
photonEnergy = energies.min()
else:
idx = np.searchsorted(energies, photonEnergy) - 1
polyA = np.poly1d(tim_calibration_table[energies[idx]][mcp-1])
polyB = np.poly1d(tim_calibration_table[energies[idx+1]][mcp-1])
fA = -np.exp(polyA(voltage))
fB = -np.exp(polyB(voltage))
f = fA + (fB-fA)/(energies[idx+1]-energies[idx])*(photonEnergy - energies[idx])
return f
poly = np.poly1d(tim_calibration_table[photonEnergy][mcp-1])
f = -np.exp(poly(voltage))
return f
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