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# -*- coding: utf-8 -*-
""" Toolbox for SCS.
Various utilities function to quickly process data measured at the SCS instruments.
Copyright (2019) SCS Team.
"""
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
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def pulsePatternInfo(data, plot=False):
''' display general information on the pulse patterns operated by SASE1 and SASE3.
This is useful to track changes of number of pulses or mode of operation of
SASE1 and SASE3. It also determines which SASE comes first in the train and
the minimum separation between the two SASE sub-trains.
Inputs:
data: xarray Dataset containing pulse pattern info from the bunch decoder MDL:
{'sase1, sase3', 'npulses_sase1', 'npulses_sase3'}
plot: bool enabling/disabling the plotting of the pulse patterns
Outputs:
print of pulse pattern info. If plot==True, plot of the pulse pattern.
'''
#Which SASE comes first?
npulses_sa3 = data['npulses_sase3']
npulses_sa1 = data['npulses_sase1']
dedicated = False
if np.all(npulses_sa1.where(npulses_sa3 !=0, drop=True) == 0):
dedicated = True
print('No SASE 1 pulses during SASE 3 operation')
if np.all(npulses_sa3.where(npulses_sa1 !=0, drop=True) == 0):
dedicated = True
print('No SASE 3 pulses during SASE 1 operation')
if dedicated==False:
pulseIdmin_sa1 = data['sase1'].where(npulses_sa1 != 0).where(data['sase1']>1).min().values
pulseIdmax_sa1 = data['sase1'].where(npulses_sa1 != 0).where(data['sase1']>1).max().values
pulseIdmin_sa3 = data['sase3'].where(npulses_sa3 != 0).where(data['sase3']>1).min().values
pulseIdmax_sa3 = data['sase3'].where(npulses_sa3 != 0).where(data['sase3']>1).max().values
#print(pulseIdmin_sa1, pulseIdmax_sa1, pulseIdmin_sa3, pulseIdmax_sa3)
if pulseIdmin_sa1 > pulseIdmax_sa3:
t = 0.220*(pulseIdmin_sa1 - pulseIdmax_sa3 + 1)
print('SASE 3 pulses come before SASE 1 pulses (minimum separation %.1f µs)'%t)
elif pulseIdmin_sa3 > pulseIdmax_sa1:
t = 0.220*(pulseIdmin_sa3 - pulseIdmax_sa1 + 1)
print('SASE 1 pulses come before SASE 3 pulses (minimum separation %.1f µs)'%t)
else:
print('Interleaved mode')
#What is the pulse pattern of each SASE?
for key in['sase3', 'sase1']:
print('\n*** %s pulse pattern: ***'%key.upper())
npulses = data['npulses_%s'%key]
sase = data[key]
if not np.all(npulses == npulses[0]):
print('Warning: number of pulses per train changed during the run!')
#take the derivative along the trainId to track changes in pulse number:
diff = npulses.diff(dim='trainId')
#only keep trainIds where a change occured:
diff = diff.where(diff !=0, drop=True)
#get a list of indices where a change occured:
idx_change = np.argwhere(np.isin(npulses.trainId.values,
diff.trainId.values, assume_unique=True))[:,0]
#add index 0 to get the initial pulse number per train:
idx_change = np.insert(idx_change, 0, 0)
print('npulses\tindex From\tindex To\ttrainId From\ttrainId To\trep. rate [kHz]')
for i,idx in enumerate(idx_change):
n = npulses[idx]
idxFrom = idx
trainIdFrom = npulses.trainId[idx]
if i < len(idx_change)-1:
idxTo = idx_change[i+1]-1
else:
idxTo = npulses.shape[0]-1
trainIdTo = npulses.trainId[idxTo]
if n <= 1:
print('%i\t%i\t\t%i\t\t%i\t%i'%(n, idxFrom, idxTo, trainIdFrom, trainIdTo))
else:
f = 1/((sase[idxFrom,1] - sase[idxFrom,0])*222e-6)
print('%i\t%i\t\t%i\t\t%i\t%i\t%.0f'%(n, idxFrom, idxTo, trainIdFrom, trainIdTo, f))
print('\n')
if plot:
plt.figure(figsize=(6,3))
plt.plot(data['npulses_sase3'].trainId, data['npulses_sase3'], 'o-',
ms=3, label='SASE 3')
plt.xlabel('trainId')
plt.ylabel('pulses per train')
plt.plot(data['npulses_sase1'].trainId, data['npulses_sase1'], '^-',
ms=3, color='C2', label='SASE 1')
plt.legend()
plt.tight_layout()
def repRate(data, sase='sase3'):
''' Calculates the pulse repetition rate in sase according
to the bunch pattern and assuming a minimum pulse
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separation of 221.54e-9 seconds.
Inputs:
data: xarray Dataset containing pulse pattern
sase: sase in which the repetition rate is
calculated (1,2 or 3)
Output:
f: repetition rate in kHz
'''
assert sase in data, 'key "{}" not found in data!'.format(sase)
sase = data[sase].where(data['npulses_{}'.format(sase)]>1,
drop=True).values
if len(sase)==0:
print('Not enough pulses to extract repetition rate')
return 0
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f = 1/((sase[0,1] - sase[0,0])*221.54e-6)
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def cleanXGMdata(data, npulses=None, sase3First=True):
''' Cleans the XGM data arrays obtained from load() function.
The XGM "TD" data arrays have arbitrary size of 1000 and default value 1.0
when there is no pulse. This function sorts the SASE 1 and SASE 3 pulses.
For DAQ runs after April 2019, sase-resolved arrays can be used. For older runs,
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the function selectSASEinXGM can be used to extract sase-resolved pulses.
Inputs:
data: xarray Dataset containing XGM TD arrays.
npulses: number of pulses, needed if pulse pattern not available.
sase3First: bool, needed if pulse pattern not available.
Output:
xarray Dataset containing sase- and pulse-resolved XGM data, with
dimension names 'sa1_pId' and 'sa3_pId'
'''
dropList = []
mergeList = []
if ("XTD10_SA3" not in data and "XTD10_XGM" in data) or (
"SCS_SA3" not in data and "SCS_XGM" in data):
#no SASE-resolved arrays available
if 'SCS_XGM' in data:
sa3 = selectSASEinXGM(data, xgm='SCS_XGM', sase='sase3', npulses=npulses,
sase3First=sase3First).rename({'XGMbunchId':'sa3_pId'}).rename('SCS_SA3')
mergeList.append(sa3)
sa1 = selectSASEinXGM(data, xgm='SCS_XGM', sase='sase1', npulses=npulses,
sase3First=sase3First).rename({'XGMbunchId':'sa1_pId'}).rename('SCS_SA1')
mergeList.append(sa1)
dropList.append('SCS_XGM')
if 'XTD10_XGM' in data:
sa3 = selectSASEinXGM(data, xgm='XTD10_XGM', sase='sase3', npulses=npulses,
sase3First=sase3First).rename({'XGMbunchId':'sa3_pId'}).rename('XTD10_SA3')
mergeList.append(sa3)
sa1 = selectSASEinXGM(data, xgm='XTD10_XGM', sase='sase1', npulses=npulses,
sase3First=sase3First).rename({'XGMbunchId':'sa1_pId'}).rename('XTD10_SA1')
mergeList.append(sa1)
dropList.append('XTD10_XGM')
keys = []
else:
keys = ["XTD10_XGM", "XTD10_SA3", "XTD10_SA1",
"XTD10_XGM_sigma", "XTD10_SA3_sigma", "XTD10_SA1_sigma"]
keys += ["SCS_XGM", "SCS_SA3", "SCS_SA1",
"SCS_XGM_sigma", "SCS_SA3_sigma", "SCS_SA1_sigma"]
for key in keys:
if key not in data:
continue
if "sa3" in key.lower():
sase = 'sa3_'
elif "sa1" in key.lower():
sase = 'sa1_'
else:
dropList.append(key)
continue
res = data[key].where(data[key] != 1.0, drop=True).rename(
{'XGMbunchId':'{}pId'.format(sase)}).rename(key)
dropList.append(key)
mergeList.append(res)
mergeList.append(data.drop(dropList))
subset = xr.merge(mergeList, join='outer')
for k in data.attrs.keys():
subset.attrs[k] = data.attrs[k]
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return subset
def selectSASEinXGM(data, sase='sase3', xgm='SCS_XGM', sase3First=True, npulses=None):
''' Extract SASE1- or SASE3-only XGM data.
There are various cases depending on i) the mode of operation (10 Hz
with fresh bunch, dedicated trains to one SASE, pulse on demand),
ii) the potential change of number of pulses per train in each SASE
and iii) the order (SASE1 first, SASE3 first, interleaved mode).
Inputs:
data: xarray Dataset containing xgm data
sase: key of sase to select: {'sase1', 'sase3'}
xgm: key of xgm to select: {'SA3_XGM', 'SCS_XGM'}
sase3First: bool, optional. Used in case no bunch pattern was recorded
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npulses: int, optional. Required in case no bunch pattern was recorded.
Output:
DataArray that has all trainIds that contain a lasing
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