diff --git a/src/toolbox_scs/detectors/digitizers.py b/src/toolbox_scs/detectors/digitizers.py
index 6ef871390e0ecddbff66c21744383d178977c19e..4790ac1fdab03af5b4a72461d0878b31dcdc79ba 100644
--- a/src/toolbox_scs/detectors/digitizers.py
+++ b/src/toolbox_scs/detectors/digitizers.py
@@ -40,7 +40,7 @@ log = logging.getLogger(__name__)
def peaks_from_raw_trace(traces, pulseStart, pulseStop, baseStart, baseStop,
- period, npulses, extra_dim):
+ period=None, npulses=None, extra_dim=None):
"""
Computes peaks from raw digitizer traces by trapezoidal integration.
@@ -62,7 +62,7 @@ def peaks_from_raw_trace(traces, pulseStart, pulseStop, baseStart, baseStop,
npulses: int
number of pulses. Ignored if intstart is a 1D-array.
extra_dim: str
- Name given to the dimension along the peaks.
+ Name given to the dimension along the peaks. Defaults to 'pulseId'.
Returns
-------
@@ -87,6 +87,8 @@ def peaks_from_raw_trace(traces, pulseStart, pulseStop, baseStart, baseStop,
else:
pulses = range(0, npulses*period, period)
+ if extra_dim is None:
+ extra_dim = 'pulseId'
results = xr.DataArray(np.empty((traces.shape[0], len(pulses))),
coords=coords,
dims=['trainId', extra_dim])
@@ -1259,7 +1261,7 @@ def timFactorFromTable(voltage, photonEnergy, mcp=1):
#############################################################################################
def extract_digitizer_peaks(proposal, runNB, mnemonic, bunchPattern=None,
- integParams=None, autoFind=True, save=True,
+ integParams=None, autoFind=True, save=False,
subdir='usr/processed_runs'):
"""
Extract the peaks from digitizer raw traces and saves them into a file.
@@ -1321,7 +1323,6 @@ def extract_digitizer_peaks(proposal, runNB, mnemonic, bunchPattern=None,
pulse_period = 440
pattern = None
- regular = True
try:
if bunchPattern == 'sase3':
pattern = XrayPulses(run)
@@ -1331,58 +1332,56 @@ def extract_digitizer_peaks(proposal, runNB, mnemonic, bunchPattern=None,
log.warning(e)
bunchPattern = None
+ # Extract pulse_ids, npulses and period from bunch pattern
+ pulse_ids, npulses, period = None, None, None
+ regular = True
+ if pattern is not None:
+ if pattern.is_constant_pattern() is False:
+ log.info('The number of pulses changed during the run.')
+ pulse_ids = np.unique(pattern.pulse_ids(labelled=False, copy=False))
+ npulses, period = None, None
+ regular = False
+ else:
+ pulse_ids = pattern.peek_pulse_ids(labelled=False)
+ npulses = len(pulse_ids)
+ if npulses > 1:
+ periods = np.diff(pulse_ids)
+ if len(np.unique(periods)) > 1:
+ regular = False
+ else:
+ period = np.unique(periods)[0] * pulse_period
+
+ # Use integration parameters, adjust them to match bunch pattern if necessary
if integParams is not None:
required_keys = ['pulseStart', 'pulseStop', 'baseStart',
- 'baseStop', 'period', 'npulses']
+ 'baseStop']
+ if bunchPattern is None:
+ required_keys += ['period', 'npulses']
if not all(name in integParams for name in required_keys):
raise TypeError('All keys of integParams argument '
f'{required_keys} are required.')
params = integParams.copy()
autoFind = False
if pattern is not None:
- # use period and npulses from pattern
- pulse_ids = pattern.peek_pulse_ids(labelled=False)
- npulses_from_bp = len(pulse_ids)
- period_from_bp = 0
- if npulses_from_bp > 1:
- period_from_bp = min(np.diff(pulse_ids)) * pulse_period
- if (npulses_from_bp != params['npulses'] or
- period_from_bp != params['period']):
+ if (npulses != params.get('npulses') or
+ period != params.get('period')):
log.warning(f'Integration parameters '
- f'(npulses={params["npulses"]}, '
- f'period={params["period"]}) do not match the '
- f'the bunch pattern (npulses={npulses_from_bp}, '
- f'period={period_from_bp}). Using bunch pattern parameters.')
- params['npulses'] = npulses_from_bp
- params['period'] = period_from_bp
- else:
- period = params['period']
- npulses = params['npulses']
- pulse_ids = np.arange(params['npulses'], dtype=np.uint64)
-
- elif pattern is not None:
- if pattern.is_constant_pattern() is False:
- print('The number of pulses changed during the run.')
- pulse_ids = np.unique(pattern.pulse_ids(labelled=False, copy=False))
- regular = False
+ f'(npulses={params.get("npulses")}, '
+ f'period={params.get("period")}) do not match the '
+ f'the bunch pattern (npulses={npulses}, '
+ f'period={period}). Using bunch pattern parameters.')
+ params['npulses'] = npulses
+ params['period'] = period
else:
- pulse_ids = pattern.peek_pulse_ids(labelled=False)
- npulses = len(pulse_ids)
- period = 0
- if npulses > 1:
- periods = np.diff(pulse_ids)
- if len(np.unique(periods)) > 1:
- regular = False
- period = min(periods)
- npulses = int((max(pulse_ids) - min(pulse_ids)) / period) + 1
- period *= pulse_period
- else:
- pulse_ids = npulses = period = None
+ pulse_ids = np.arange(0, params['npulses'], params['period'],
+ dtype=np.uint64)
+ start = params['pulseStart']
+ params['pulseStart'] = [start + (pid - pulse_ids[0]) * pulse_period
+ for pid in pulse_ids]
- # generate average trace
- traces = run[source, key].xarray(name=mnemonic.replace('raw', 'avg'),
- extra_dims=mnemo['dim'])
- trace = traces.mean('trainId')
+ # load traces and generate average
+ traces = run[source, key].xarray(name=mnemonic, extra_dims=mnemo['dim'])
+ trace = traces.mean('trainId').rename(mnemonic.replace('raw', 'avg'))
# find peak integration parameters
if autoFind == True:
@@ -1393,31 +1392,34 @@ def extract_digitizer_peaks(proposal, runNB, mnemonic, bunchPattern=None,
f'found integration parameters (npulses={params["npulses"]}, '
f'period={params["period"]}) do not match. Using bunch '
'pattern parameters.')
- params['period'] = period
- params['npulses'] = min(len(trace) // period, npulses)
+ params["npulses"] = npulses
+ params["period"] = period
if pulse_ids is None:
pulse_ids = np.arange(params['npulses'], dtype=np.uint64)
-
- if params is None:
- log.warning('Could not find peak integration parameters.')
- return xr.DataArray()
+ start = params['pulseStart']
+ params['pulseStart'] = [start + (pid - pulse_ids[0]) * pulse_period
+ for pid in pulse_ids]
# extract peaks
data = peaks_from_raw_trace(traces, **params, extra_dim=extra_dim)
data = data.rename(mnemonic.replace('raw', 'peaks'))
- data = data.assign_coords({extra_dim: pulse_ids})
- if regular is False:
- period = int(period / pulse_period)
+ if regular is True:
+ data = data.assign_coords({extra_dim: pulse_ids})
+ else:
mask = pattern.pulse_mask(labelled=False)
mask = xr.DataArray(mask, dims=['trainId', extra_dim],
coords={'trainId': run[source, key].train_ids,
extra_dim: np.arange(mask.shape[1])})
- mask = mask.sel({extra_dim: slice(pulse_ids[0],
- pulse_ids[0] + npulses*period,period)})
+ mask = mask.sel({extra_dim: pulse_ids})
data = data.where(mask, drop=True)
data.attrs['params_keys'] = list(params.keys())
+ if regular:
+ params['pulseStart'] = params['pulseStart'][0]
+ for p in params:
+ if params[p] is None:
+ params[p] = 'None'
data.attrs[f'params_{data.name}'] = list(params.values())
if save:
save_peaks(proposal, runNB, data, trace, subdir)