diff --git a/Load.py b/Load.py
index 7db62c225176c470b4c3ae5ec786fd557044aa1a..70b0c78710685e14bd9c763f56bc9d38a7d73de1 100644
--- a/Load.py
+++ b/Load.py
@@ -40,13 +40,13 @@ mnemonics = {
'dim':None},
#Bunch Arrival Monitors
- "BAM6": {'source':'SCS_ILH_LAS/DOOCS/BAM_414_B2:output',
+ "BAM5": {'source':'SCS_ILH_LAS/DOOCS/BAM_414_B2:output',
'key':'data.lowChargeArrivalTime',
'dim':['BAMbunchId']},
- "BAM7": {'source':'SCS_ILH_LAS/DOOCS/BAM_1932M_TL:output',
+ "BAM6": {'source':'SCS_ILH_LAS/DOOCS/BAM_1932M_TL:output',
'key':'data.lowChargeArrivalTime',
'dim':['BAMbunchId']},
- "BAM8": {'source':'SCS_ILH_LAS/DOOCS/BAM_1932S_TL:output',
+ "BAM7": {'source':'SCS_ILH_LAS/DOOCS/BAM_1932S_TL:output',
'key':'data.lowChargeArrivalTime',
'dim':['BAMbunchId']},
diff --git a/bunch_pattern.py b/bunch_pattern.py
index 42f34a61f8724762be1c3670d1287b74a64ac5d6..16173935080c5d346c5eb3601b9aef3a74e79b30 100644
--- a/bunch_pattern.py
+++ b/bunch_pattern.py
@@ -181,22 +181,31 @@ def repRate(data, sase='sase3'):
f = 1/((sase[0,1] - sase[0,0])*12e-3/54.1666667)
return f
-def sortBAMdata(data, key='sase3'):
+def sortBAMdata(data, key='scs_ppl', sa3Offset=0):
''' Extracts beam arrival monitor data from the raw arrays 'BAM6', 'BAM7', etc...
according to the bunchPatternTable. The BAM arrays contain 7220 values, which
corresponds to FLASH busrt length of 800 us @ 9 MHz. The bunchPatternTable
only has 2700 values, corresponding to XFEL 600 us burst length @ 4.5 MHz.
- Hence, we truncate the BAM arrays to 5400 with a stride of 2 and match them
+ Hence, the BAM arrays are truncated to 5400 with a stride of 2 and matched
to the bunchPatternTable. If key is one of the sase, the given dimension name
of the bam arrays is 'sa[sase number]_pId', to match other data (XGM, TIM...).
+ If key is 'scs_ppl', the dimension is named 'ol_pId'
Inputs:
data: xarray Dataset containing BAM arrays
key: str, ['sase1', 'sase2', 'sase3', 'scs_ppl']
-
+ sa3Offset: int, used if key=='scs_ppl'. Offset in number of pulse_id
+ between the first OL and FEL pulses. An offset of 40 means that
+ the first laser pulse comes 40 pulse_id later than the FEL on a
+ grid of 4.5 MHz. Negative values shift the laser pulse before
+ the FEL one.
Output:
ndata: xarray Dataset with same keys as input data (but new bam arrays)
'''
a, b, mask = extractBunchPattern(key=key, runDir=data.attrs['run'])
+ if key == 'scs_ppl':
+ a3, b3, mask3 = extractBunchPattern(key='sase3', runDir=data.attrs['run'])
+ firstSa3_pId = a3.where(b3>0, drop=True)[0,0].values.astype(int)
+ mask = mask.roll(pulse_slot=firstSa3_pId+sa3Offset)
mask = mask.rename({'pulse_slot':'BAMbunchId'})
ndata = data
dropList = []
@@ -208,7 +217,11 @@ def sortBAMdata(data, key='sase3'):
bam = bam.where(mask, drop=True)
if 'sase' in key:
name = f'sa{key[4]}_pId'
- bam = bam.rename({'BAMbunchId':name})
+ elif key=='scs_ppl':
+ name = 'ol_pId'
+ else:
+ name = 'bam_pId'
+ bam = bam.rename({'BAMbunchId':name})
mergeList.append(bam)
mergeList.append(data.drop(dropList))
ndata = xr.merge(mergeList, join='inner')
diff --git a/xgm.py b/xgm.py
index 7289fc7af286fb5933edddb22f5507e2ce74d3a5..192508a07f9fb01a7d18e04b733f76922da8565e 100644
--- a/xgm.py
+++ b/xgm.py
@@ -9,6 +9,7 @@ import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from scipy.signal import find_peaks
+import ToolBox as tb
# XGM
def cleanXGMdata(data, npulses=None, sase3First=True):
@@ -815,7 +816,8 @@ def matchXgmTimPulseId(data, use_apd=True, intstart=None, intstop=None,
# Fast ADC
def fastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop,
- period=None, npulses=None, usePeakValue=False, peakType='pos'):
+ period=None, npulses=None, source='scs_ppl',
+ usePeakValue=False, peakType='pos'):
''' Computes peak integration from raw FastADC traces.
Inputs:
@@ -832,8 +834,13 @@ def fastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop,
two bunches @ 4.5 MHz.
npulses: number of pulses. If None, takes the maximum number of
pulses according to the bunch patter (field 'npulses_sase3')
+ source: str, nature of the pulses: 'sase[1,2 or 3]', or 'scs_ppl',
+ used to give name to the peak Id dimension.
usePeakValue: bool, if True takes the peak value of the signal,
otherwise integrates over integration region.
+ peakType: str, 'pos' or 'neg'. Used if usePeakValue is True to
+ indicate if min or max value should be extracted.
+
Output:
results: DataArray with dims trainId x max(sase3 pulses)
@@ -842,20 +849,28 @@ def fastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop,
keyraw = 'FastADC{}raw'.format(channel)
if keyraw not in data:
raise ValueError("Source not found: {}!".format(keyraw))
- if npulses is None:
- npulses = int(data['npulses_sase3'].max().values)
- if period is None:
- sa3 = data['sase3'].where(data['sase3']>1)
- 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 pulse length (221.5 ns / 9.23 ns = 24 samples)
- period = 24 * step
- else:
- period = 1
+ if npulses is None or period is None:
+ indices, npulses_bp, mask = tb.extractBunchPattern(runDir=data.attrs['run'],
+ key=source)
+ if npulses is None:
+ npulses = int(npulses_bp.max().values)
+ if period is None:
+ indices = indices_bp.where(indices_bp>1)
+ if npulses > 1:
+ #Calculate the number of pulses between two lasing pulses (step)
+ step = indices.where(npulses_bp>1, drop=True)[0,:2].values
+ step = int(step[1] - step[0])
+ #multiply by elementary pulse length (221.5 ns / 9.23 ns = 24 samples)
+ period = 24 * step
+ else:
+ period = 1
+ pulseId = 'peakId'
+ if source=='scs_ppl':
+ pulseId = 'ol_pId'
+ if 'sase' in source:
+ pulseId = f'sa{source[4]}_pId'
results = xr.DataArray(np.empty((data.trainId.shape[0], npulses)), coords=data[keyraw].coords,
- dims=['trainId', 'peakId'.format(channel)])
+ dims=['trainId', pulseId])
for i in range(npulses):
a = intstart + period*i
b = intstop + period*i
@@ -872,8 +887,8 @@ def fastAdcPeaks(data, channel, intstart, intstop, bkgstart, bkgstop,
results[:,i] = val
return results
-def autoFindFastAdcPeaks(data, channel=5, window='small', usePeakValue=False,
- display=False, plot=False):
+def autoFindFastAdcPeaks(data, channel=5, window='large', usePeakValue=False,
+ source='scs_ppl', display=False, plot=False):
''' Automatically finds peaks in channel of Fast ADC trace, a minimum width of 4
samples. The find_peaks function and determination of the peak integration
region and baseline subtraction is optimized for typical photodiode signals
@@ -929,7 +944,7 @@ def autoFindFastAdcPeaks(data, channel=5, window='small', usePeakValue=False,
f'rep. rate={1e6/(9.230769*period):.3f} kHz')
fAdcPeaks = fastAdcPeaks(data, channel=channel, intstart=intstart, intstop=intstop,
bkgstart=bkgstart, bkgstop=bkgstop, period=period, npulses=npulses,
- usePeakValue=usePeakValue, peakType=posNeg[:3])
+ source=source, usePeakValue=usePeakValue, peakType=posNeg[:3])
if plot:
plt.figure()
plt.plot(trace_plot, 'o-', ms=3)