diff --git a/doc/changelog.rst b/doc/changelog.rst
index 16ab02c306b8000eebd2dd64bbb268f0da6d6e93..43f8f4bc5d69ff6228dc5bcd550269ab627e9bdc 100644
--- a/doc/changelog.rst
+++ b/doc/changelog.rst
@@ -21,6 +21,7 @@ unreleased
- adds Chem diagnostics and Gotthard II mnemonics :mr:`292`
- make hRIXS centroid threshold configurable :mr:`298`
- drop MaranaX non-existing mnemonics and add attributes on dataset :mr:`300`
+ - streamline digitizer functions :mr:`304`
- **New Features**
diff --git a/src/toolbox_scs/constants.py b/src/toolbox_scs/constants.py
index 41ab20c3b58b3db628315d5c7308e3208d45de5e..ba1dd3d7a7954435f3716a4487295049175c4e91 100644
--- a/src/toolbox_scs/constants.py
+++ b/src/toolbox_scs/constants.py
@@ -799,28 +799,32 @@ mnemonics = {
# TIM
"MCP1apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_D.apd.pulseIntegral',
- 'dim': ['apdId']},),
+ 'dim': ['apdId'],
+ 'extract': 'peaks'},),
"MCP1raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_D.raw.samples',
'dim': ['samplesId'],
'extract': 'peaks'},),
"MCP2apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_C.apd.pulseIntegral',
- 'dim': ['apdId']},),
+ 'dim': ['apdId'],
+ 'extract': 'peaks'},),
"MCP2raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_C.raw.samples',
'dim': ['samplesId'],
'extract': 'peaks'},),
"MCP3apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_B.apd.pulseIntegral',
- 'dim': ['apdId']},),
+ 'dim': ['apdId'],
+ 'extract': 'peaks'},),
"MCP3raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_B.raw.samples',
'dim': ['samplesId'],
'extract': 'peaks'},),
"MCP4apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_A.apd.pulseIntegral',
- 'dim': ['apdId']},),
+ 'dim': ['apdId'],
+ 'extract': 'peaks'},),
"MCP4raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_A.raw.samples',
'dim': ['samplesId'],
@@ -829,70 +833,80 @@ mnemonics = {
# FastADC
"FastADC0peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_0.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC0raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_0.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC1peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_1.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC1raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_1.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC2peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_2.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_2.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC3peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_3.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC3raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_3.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC4peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_4.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC4raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_4.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC5peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_5.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC5raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_5.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC6peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_6.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC6raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_6.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC7peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_7.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC7raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_7.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC8peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_8.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC8raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_8.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
'extract': 'peaks'},),
"FastADC9peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_9.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC9raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_9.output',
'key': 'data.rawData',
'dim': ['fadc_samplesId'],
@@ -901,70 +915,80 @@ mnemonics = {
# FastADC 2
"FastADC2_0peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_0.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_0raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_0.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_1peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_1.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_1raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_1.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_2peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_2.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_2raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_2.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_3peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_3.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_3raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_3.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_4peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_4.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_4raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_4.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_5peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_5.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_5raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_5.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_6peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_6.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_6raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_6.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_7peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_7.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_7raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_7.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_8peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_8.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_8raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_8.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
'extract': 'peaks'},),
"FastADC2_9peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_9.output',
'key': 'data.peaks',
- 'dim': ['peakId']},),
+ 'dim': ['peakId'],
+ 'extract': 'peaks'},),
"FastADC2_9raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_9.output',
'key': 'data.rawData',
'dim': ['fadc2_samplesId'],
diff --git a/src/toolbox_scs/detectors/digitizers.py b/src/toolbox_scs/detectors/digitizers.py
index d69efd42d7ff39b0f740ca057f25489cc31cfbf0..83672429db6a1d9897962733b8aa96fce38d9d13 100644
--- a/src/toolbox_scs/detectors/digitizers.py
+++ b/src/toolbox_scs/detectors/digitizers.py
@@ -143,8 +143,8 @@ def peaks_from_apd(array, params, digitizer, bpt, bunchPattern):
f'but the array length is only {array.sizes[arr_dim]}.')
npulses_apd = array.sizes[arr_dim]
mask = is_pulse_at(bpt, bunchPattern).rename({'pulse_slot': pulse_dim})
- mask = mask.sel(trainId=array.trainId)
- mask = mask.assign_coords({pulse_dim: np.arange(2700)})
+ mask = mask.where(mask.trainId.isin(array.trainId), drop=True)
+ mask = mask.assign_coords({pulse_dim: np.arange(bpt.sizes['pulse_slot'])})
pid = np.sort(np.unique(np.where(mask)[1]))
npulses_bpt = len(pid)
apd_coords = np.arange(pid[0], pid[0] + stride*npulses_apd, stride)
@@ -152,7 +152,8 @@ def peaks_from_apd(array, params, digitizer, bpt, bunchPattern):
if len(np.intersect1d(apd_coords, pid, assume_unique=True)) < npulses_bpt:
log.warning('Not all pulses were recorded. The digitizer '
'was set to record pulse ids '
- f'{apd_coords[apd_coords<2700]} but the bunch pattern for'
+ f'{apd_coords[apd_coords<bpt.sizes["pulse_slot"]]} but the'
+ 'bunch pattern for'
f' {bunchPattern} is {pid}. Skipping pulse ID alignment.')
noalign = True
array = array.isel({arr_dim: slice(0, npulses_apd)})
@@ -167,10 +168,8 @@ def peaks_from_apd(array, params, digitizer, bpt, bunchPattern):
def get_peaks(run,
- data=None,
- source=None,
- key=None,
- digitizer='ADQ412',
+ data,
+ mnemonic,
useRaw=True,
autoFind=True,
integParams=None,
@@ -190,15 +189,10 @@ def get_peaks(run,
array containing the raw traces or peak-integrated values from the
digitizer. If str, must be one of the ToolBox mnemonics. If None,
the data is loaded via the source and key arguments.
- source: str
- Name of digitizer source, e.g. 'SCS_UTC1_ADQ/ADC/1:network'. Only
- required if data is a DataArray or None.
- key: str
- Key for digitizer data, e.g. 'digitizers.channel_1_A.raw.samples'.
- Only required if data is a DataArray or is None.
- digitizer: string
- name of digitizer, e.g. 'FastADC' or 'ADQ412'. Used to determine
- the sampling rate.
+ mnemonic: str or dict
+ ToolBox mnemonic or dict with source and key as in
+ {'source': 'SCS_UTC1_ADQ/ADC/1:network',
+ 'key': 'digitizers.channel_1_A.raw.samples'}
useRaw: bool
If True, extract peaks from raw traces. If False, uses the APD (or
peaks) data from the digitizer.
@@ -210,12 +204,10 @@ def get_peaks(run,
integration: 'pulseStart', 'pulseStop', 'baseStart', 'baseStop',
'period', 'npulses'. Not used if autoFind is True. All keys are
required when bunch pattern is missing.
- bunchPattern: string or dict
+ bunchPattern: str
match the peaks to the bunch pattern: 'sase1', 'sase3', 'scs_ppl'.
This will dictate the name of the pulse ID coordinates: 'sa1_pId',
- 'sa3_pId' or 'scs_ppl'. Alternatively, a dict with source, key and
- pattern can be provided, e.g. {'source':'SCS_RR_UTC/TSYS/TIMESERVER',
- 'key':'bunchPatternTable.value', 'pattern':'sase3'}
+ 'sa3_pId' or 'scs_ppl'.
bpt: xarray DataArray
bunch pattern table
extra_dim: str
@@ -229,43 +221,25 @@ def get_peaks(run,
-------
xarray.DataArray containing digitizer peaks with pulse coordinates
"""
- if data is None and (source is None or key is None):
- raise ValueError('At least data or source + key arguments '
- 'are required.')
- # Load data
- arr = None
- run_mnemonics = mnemonics_for_run(run)
- if data is None:
- log.debug(f'Loading array from DataCollection with {source}, {key}')
- arr = run.get_array(source, key)
- if isinstance(data, str):
- log.debug(f'Loading array from mnemonic {data}')
- m = run_mnemonics[data]
- source = m['source']
- key = m['key']
- arr = run.get_array(source, key, m['dim'])
- if arr is None:
- log.debug('Using array provided in data argument.')
- if source is None or key is None:
- raise ValueError('source and/or key arguments missing.')
- arr = data
+ arr = data
dim = [d for d in arr.dims if d != 'trainId'][0]
# Load bunch pattern table
+ run_mnemonics = mnemonics_for_run(run)
if bpt is None and bunchPattern != 'None':
- if isinstance(bunchPattern, dict):
- bpt = run.get_array(bunchPattern['source'], bunchPattern['key'],
- extra_dims=['pulse_slot'])
- pattern = bunchPattern['pattern']
if 'bunchPatternTable' in run_mnemonics:
m = run_mnemonics['bunchPatternTable']
bpt = run.get_array(m['source'], m['key'], m['dim'])
- pattern = bunchPattern
+ pattern = bunchPattern
else:
pattern = bunchPattern
if bunchPattern == 'None':
bpt = None
+ # Find digitizer type
+ m = mnemonic if isinstance(mnemonic, dict) else run_mnemonics[mnemonic]
+ digitizer = digitizer_type(run, m.get('source'))
+
# 1. Peak-integrated data from digitizer
if useRaw is False:
# 1.1 No bunch pattern provided
@@ -279,13 +253,16 @@ def get_peaks(run,
return arr.isel({dim: indices}).rename({dim: extra_dim})
# 1.2 Bunch pattern is provided
- peak_params = channel_peak_params(run, source, key)
+ if isinstance(mnemonic, dict):
+ peak_params = channel_peak_params(run, mnemonic.get('source'),
+ mnemonic.get('key'))
+ else:
+ peak_params = channel_peak_params(run, mnemonic)
log.debug(f'Digitizer peak integration parameters: {peak_params}')
return peaks_from_apd(arr, peak_params, digitizer, bpt, bunchPattern)
# 2. Use raw data from digitizer
# minimum pulse period @ 4.5MHz, according to digitizer type
- digitizer = digitizer_type(source=source)
min_distance = 1
if digitizer == 'FastADC':
min_distance = 24
@@ -338,7 +315,7 @@ def get_peaks(run,
else:
mask = is_pulse_at(bpt.sel(trainId=valid_tid), pattern)
mask = mask.rename({'pulse_slot': extra_dim})
- mask = mask.assign_coords({extra_dim: np.arange(2700)})
+ mask = mask.assign_coords({extra_dim: np.arange(bpt.sizes['pulse_slot'])})
mask_on = mask.where(mask, drop=True).fillna(False).astype(bool)
log.info(f'Bunch pattern of {pattern} changed during the run.')
pid = mask_on.coords[extra_dim]
@@ -727,7 +704,7 @@ def check_peak_params(run, mnemonic, raw_trace=None, ntrains=200, params=None,
log.warning('The digitizer did not record peak-integrated data.')
if not plot:
return params
- digitizer = digitizer_type(mnemonic, run_mnemonics)
+ digitizer = digitizer_type(run, run_mnemonics[mnemonic]['source'])
min_distance = 24 if digitizer == "FastADC" else 440
if 'bunchPatternTable' in run_mnemonics and bunchPattern != 'None':
sel = run.select_trains(np.s_[:ntrains])
@@ -829,27 +806,35 @@ def plotPeakIntegrationWindow(raw_trace, params, bp_params, show_all=False):
return fig, ax
-def digitizer_type(mnemonic=None, mnemo_dict=None, source=None):
- if mnemonic is not None:
- source = mnemo_dict[mnemonic]['source']
- if ':channel' in source:
- return 'FastADC'
- if ':output' in source:
- return 'FastADC'
- if ':network' in source:
- return 'ADQ412'
- dic = {'XTD10_MCP': 'FastADC',
- 'FastADC': 'FastADC',
- 'PES': 'ADQ412',
- 'MCP': 'ADQ412'}
- for k, v in dic.items():
- if k in mnemonic:
- return v
- log.warning(f'Could not find digitizer type from mnemonic {mnemonic}.')
- return 'ADQ412'
-
-
-def get_tim_peaks(run, mnemonics=None, merge_with=None,
+def digitizer_type(run, source):
+ """
+ Finds the digitizer type based on the class Id / name of the source.
+ Example source: 'SCS_UTC1_MCP/ADC/1'. Defaults to ADQ412 if not found.
+ """
+ ret = None
+ if '_MCP/ADC/1' in source:
+ ret = 'FastADC'
+ if '_ADQ/ADC/1' in source:
+ ret = 'ADQ412'
+ if ret is None:
+ digi_dict = {'FastAdc': 'FastADC',
+ 'FastAdcLegacy': 'FastADC',
+ 'AdqDigitizer': 'ADQ412',
+ 'PyADCChannel': 'FastADC',
+ 'PyADCChannelLegacy': 'FastADC'
+ }
+ try:
+ source = source.split(':')[0]
+ classId = run.get_run_value(source, 'classId.value')
+ ret = digi_dict.get(classId)
+ except Exception as e:
+ log.warning(str(e))
+ log.warning(f'Could not find digitizer type from source {source}.')
+ ret = 'ADQ412'
+ return ret
+
+
+def get_tim_peaks(run, mnemonic=None, merge_with=None,
bunchPattern='sase3', integParams=None,
keepAllSase=False):
"""
@@ -862,10 +847,8 @@ def get_tim_peaks(run, mnemonics=None, merge_with=None,
----------
run: extra_data.DataCollection
DataCollection containing the digitizer data.
- mnemonics: str or list of str
- mnemonics for TIM, e.g. "MCP2apd" or ["MCP2apd", "MCP3raw"].
- If None, defaults to "MCP2apd" in case no merge_with dataset
- is provided.
+ mnemonic: str
+ mnemonics for TIM, e.g. "MCP2apd".
merge_with: xarray Dataset
If provided, the resulting Dataset will be merged with this
one. The TIM variables of merge_with (if any) will also be
@@ -885,16 +868,16 @@ def get_tim_peaks(run, mnemonics=None, merge_with=None,
Returns
-------
- xarray Dataset with all TIM variables substituted by
+ xarray Dataset with TIM variables substituted by
the peak caclulated values (e.g. "MCP2raw" becomes
"MCP2peaks"), merged with Dataset *merge_with* if provided.
"""
- return get_digitizer_peaks(run, mnemonics, merge_with,
+ return get_digitizer_peaks(run, mnemonic, merge_with,
bunchPattern, integParams,
keepAllSase)
-def get_laser_peaks(run, mnemonics=None, merge_with=None,
+def get_laser_peaks(run, mnemonic=None, merge_with=None,
bunchPattern='scs_ppl', integParams=None):
"""
Extracts laser photodiode signal (peak intensity) from Fast ADC
@@ -906,10 +889,9 @@ def get_laser_peaks(run, mnemonics=None, merge_with=None,
----------
run: extra_data.DataCollection
DataCollection containing the digitizer data.
- mnemonics: str or list of str
- mnemonics for FastADC corresponding to laser signal, e.g.
- "FastADC2peaks" or ["FastADC2raw", "FastADC3peaks"]. If None,
- defaults to "MCP2apd" in case no merge_with dataset is provided.
+ mnemonic: str
+ mnemonic for FastADC corresponding to laser signal, e.g.
+ "FastADC2peaks" or 'I0_ILHraw'.
merge_with: xarray Dataset
If provided, the resulting Dataset will be merged with this
one. The FastADC variables of merge_with (if any) will also be
@@ -930,16 +912,16 @@ def get_laser_peaks(run, mnemonics=None, merge_with=None,
the peak caclulated values (e.g. "FastADC2raw" becomes
"FastADC2peaks").
"""
- return get_digitizer_peaks(run, mnemonics, merge_with,
+ return get_digitizer_peaks(run, mnemonic, merge_with,
bunchPattern, integParams, False)
-def get_digitizer_peaks(run, mnemonics=None, merge_with=None,
- bunchPattern='None', integParams=None,
- digitizer=None, keepAllSase=False):
+def get_digitizer_peaks(run, mnemonic, merge_with=None,
+ bunchPattern='sase3', integParams=None,
+ keepAllSase=False):
"""
- Automatically computes digitizer peaks. Sources can be loaded on the
- fly via the mnemonics argument, or processed from an existing data set
+ Automatically computes digitizer peaks. A source can be loaded on the
+ fly via the mnemonic argument, or processed from an existing data set
(merge_with). The bunch pattern table is used to assign the pulse
id coordinates.
@@ -947,18 +929,14 @@ def get_digitizer_peaks(run, mnemonics=None, merge_with=None,
----------
run: extra_data.DataCollection
DataCollection containing the digitizer data.
- mnemonics: str or list of str
- mnemonics for FastADC or TIM, e.g. "FastADC2raw" or ["MCP2raw",
- "MCP3apd"]. If None and no merge_with dataset is provided,
- defaults to "MCP2apd" if digitizer is ADQ412 or
- "FastADC5raw" if digitizer is FastADC.
+ mnemonic: str
+ mnemonic for FastADC or ADQ412, e.g. "I0_ILHraw" or "MCP3apd".
+ The data is either loaded from the DataCollection or taken from
+ merge_with.
merge_with: xarray Dataset
- If provided, the resulting Dataset will be merged with this
- one. The FastADC variables of merge_with (if any) will also be
- computed and merged.
- bunchPattern: str
+ If provided, the resulting Dataset will be merged with this one.
+ bunchPattern: str or dict
'sase1' or 'sase3' or 'scs_ppl', 'None': bunch pattern
- used to extract peaks.
integParams: dict
dictionnary for raw trace integration, e.g.
{'pulseStart':100, 'pulsestop':200, 'baseStart':50,
@@ -970,48 +948,22 @@ def get_digitizer_peaks(run, mnemonics=None, merge_with=None,
Returns
-------
- xarray Dataset with all Fast ADC variables substituted by
- the peak caclulated values (e.g. "FastADC2raw" becomes
+ xarray Dataset with digitizer peak variables. Raw variables are
+ substituted by the peak caclulated values (e.g. "FastADC2raw" becomes
"FastADC2peaks").
"""
- if mnemonics is None and merge_with is None:
- raise ValueError("at least one of mnemonics or merge_with "
- "arguments is expected.")
-
run_mnemonics = mnemonics_for_run(run)
- # find digitizer type and get the list of mnemonics to process
- def to_processed_name(name):
- return name.replace('raw', 'peaks').replace('apd', 'peaks')
- if mnemonics is None:
- if digitizer is None:
- for v in merge_with:
- if 'FastADC2_' in v:
- digitizer = 'FastADC2'
- break
- if 'FastADC' in v:
- digitizer = 'FastADC'
- break
- if 'MCP' in v:
- digitizer = 'ADQ412'
- break
- if digitizer is None:
- log.warning(f'No array with digitizer data '
- 'to extract. Skipping.')
- return merge_with
- mnemonics = mnemonics_to_process(mnemonics, merge_with,
- digitizer, to_processed_name)
- else:
- mnemonics = [mnemonics] if isinstance(mnemonics, str) else mnemonics
- digitizer = digitizer_type(mnemonics[0], run_mnemonics)
-
- if len(mnemonics) == 0:
- log.info(f'No array with unaligned {digitizer} peaks to extract. '
- 'Skipping.')
+ if mnemonic not in run_mnemonics:
+ log.warning('Mnemonic not found in run. Skipping.')
return merge_with
- else:
- log.info(f'Extracting {digitizer} peaks from {mnemonics}.')
-
+ if bool(merge_with) and mnemonic in merge_with:
+ for d in merge_with[mnemonic].dims:
+ if d in ['sa3_pId', 'ol_pId']:
+ log.warning(f'{mnemonic} already extracted. '
+ 'Skipping.')
+ return merge_with
# check if bunch pattern table exists
+ bpt = None
if bool(merge_with) and 'bunchPatternTable' in merge_with:
bpt = merge_with['bunchPatternTable']
log.debug('Using bpt from merge_with dataset.')
@@ -1024,39 +976,29 @@ def get_digitizer_peaks(run, mnemonics=None, merge_with=None,
bpt = run.get_array(m['source'], m['key'], m['dim'])
log.debug('Loaded bpt from DataCollection.')
else:
- bpt = None
-
- # iterate over mnemonics and merge arrays in dataset
+ log.warning('Could not load bunch pattern table.')
+ # prepare resulting dataset
if bool(merge_with):
- mw_ds = merge_with.drop(mnemonics, errors='ignore')
+ mw_ds = merge_with.drop(mnemonic, errors='ignore')
else:
mw_ds = xr.Dataset()
+ # iterate over mnemonics and merge arrays in dataset
autoFind = True if integParams is None else False
- names = []
- vals = []
- for k in mnemonics:
- useRaw = True if 'raw' in k else False
- m = run_mnemonics[k]
- if bool(merge_with) and k in merge_with:
- data = merge_with[k]
- else:
- data = None
- peaks = get_peaks(run, data,
- source=m['source'],
- key=m['key'],
- digitizer=digitizer,
- useRaw=useRaw,
- autoFind=autoFind,
- integParams=integParams,
- bunchPattern=bunchPattern,
- bpt=bpt)
- name = to_processed_name(k)
- names.append(name)
- vals.append(peaks)
+ m = run_mnemonics[mnemonic]
+ useRaw = True if 'raw' in mnemonic else False
+ if bool(merge_with) and mnemonic in merge_with:
+ data = merge_with[mnemonic]
+ else:
+ data = run.get_array(m['source'], m['key'], m['dim'])
+ peaks = get_peaks(run, data, mnemonic,
+ useRaw=useRaw,
+ autoFind=autoFind,
+ integParams=integParams,
+ bunchPattern=bunchPattern,
+ bpt=bpt)
+ name = mnemonic.replace('raw', 'peaks').replace('apd', 'peaks')
join = 'outer' if keepAllSase else 'inner'
- aligned_vals = xr.align(*vals, join=join)
- ds = xr.Dataset(dict(zip(names, aligned_vals)))
- ds = mw_ds.merge(ds, join=join)
+ ds = mw_ds.merge(peaks.rename(name), join=join)
return ds
diff --git a/src/toolbox_scs/load.py b/src/toolbox_scs/load.py
index 268dc9fff81e50c51c12f4270bd363bb7168f6d7..275e555a12a1fcceae21c3983974014957370aa6 100644
--- a/src/toolbox_scs/load.py
+++ b/src/toolbox_scs/load.py
@@ -238,7 +238,7 @@ def load(proposalNB=None, runNB=None,
if bp is None:
continue
ds = tbdet.get_digitizer_peaks(
- run, mnemonics=k, merge_with=ds, bunchPattern=bp)
+ run, mnemonic=k, merge_with=ds, bunchPattern=bp)
if extract_xgm:
for k, v in run_mnemonics.items():
if k not in ds or v.get('extract') != 'XGM':