diff --git a/src/toolbox_scs/constants.py b/src/toolbox_scs/constants.py
index 6c041d3d154a5fd817113de8be4e44bb8f63e14d..c54d72e30033d1bd1d1eb2a83c017d8846708ab9 100644
--- a/src/toolbox_scs/constants.py
+++ b/src/toolbox_scs/constants.py
@@ -47,22 +47,28 @@ mnemonics = {
# Bunch Arrival Monitors
"BAM414": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_414_B2:output',
'key': 'data.absoluteTD',
- 'dim': ['BAMbunchId']},
+ 'dim': ['BAMbunchId'],
+ 'extract': 'BAM'},
{'source': 'SCS_ILH_LAS/DOOCS/BAM_414_B2:output',
'key': 'data.lowChargeArrivalTime',
- 'dim': ['BAMbunchId']},),
+ 'dim': ['BAMbunchId'],
+ 'extract': 'BAM'},),
"BAM1932M": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_1932M_TL:output',
'key': 'data.absoluteTD',
- 'dim': ['BAMbunchId']},
+ 'dim': ['BAMbunchId'],
+ 'extract': 'BAM'},
{'source': 'SCS_ILH_LAS/DOOCS/BAM_1932M_TL:output',
'key': 'data.lowChargeArrivalTime',
- 'dim': ['BAMbunchId']},),
+ 'dim': ['BAMbunchId'],
+ 'extract': 'BAM'},),
"BAM1932S": ({'source': 'SCS_ILH_LAS/DOOCS/BAM_1932S_TL:output',
'key': 'data.absoluteTD',
- 'dim': ['BAMbunchId']},
+ 'dim': ['BAMbunchId'],
+ 'extract': 'BAM'},
{'source': 'SCS_ILH_LAS/DOOCS/BAM_1932S_TL:output',
'key': 'data.lowChargeArrivalTime',
- 'dim': ['BAMbunchId']},),
+ 'dim': ['BAMbunchId'],
+ 'extract': 'BAM'},),
# SA3
"nrj": ({'source': 'SA3_XTD10_MONO/MDL/PHOTON_ENERGY',
@@ -228,22 +234,28 @@ mnemonics = {
# ADC
"XTD10_XGM": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
'key': 'data.intensityTD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"XTD10_XGM_sigma": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
'key': 'data.intensitySigmaTD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"XTD10_SA3": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
'key': 'data.intensitySa3TD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"XTD10_SA3_sigma": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
'key': 'data.intensitySa3SigmaTD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"XTD10_SA1": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
'key': 'data.intensitySa1TD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"XTD10_SA1_sigma": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS:output',
'key': 'data.intensitySa1SigmaTD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
# low pass averaged ADC
"XTD10_slowTrain": ({'source': 'SA3_XTD10_XGM/XGM/DOOCS',
'key': 'controlData.slowTrain.value',
@@ -269,22 +281,28 @@ mnemonics = {
# ADC
"SCS_XGM": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
'key': 'data.intensityTD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"SCS_XGM_sigma": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
'key': 'data.intensitySigmaTD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"SCS_SA1": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
'key': 'data.intensitySa1TD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"SCS_SA1_sigma": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
'key': 'data.intensitySa1SigmaTD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"SCS_SA3": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
'key': 'data.intensitySa3TD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
"SCS_SA3_sigma": ({'source': 'SCS_BLU_XGM/XGM/DOOCS:output',
'key': 'data.intensitySa3SigmaTD',
- 'dim': ['XGMbunchId']},),
+ 'dim': ['XGMbunchId'],
+ 'extract': 'XGM'},),
# low pass averaged ADC
"SCS_slowTrain": ({'source': 'SCS_BLU_XGM/XGM/DOOCS',
'key': 'controlData.slowTrain.value',
@@ -742,25 +760,29 @@ mnemonics = {
'dim': ['apdId']},),
"MCP1raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_D.raw.samples',
- 'dim': ['samplesId']},),
+ 'dim': ['samplesId'],
+ 'extract': 'peaks'},),
"MCP2apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_C.apd.pulseIntegral',
'dim': ['apdId']},),
"MCP2raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_C.raw.samples',
- 'dim': ['samplesId']},),
+ 'dim': ['samplesId'],
+ 'extract': 'peaks'},),
"MCP3apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_B.apd.pulseIntegral',
'dim': ['apdId']},),
"MCP3raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_B.raw.samples',
- 'dim': ['samplesId']},),
+ 'dim': ['samplesId'],
+ 'extract': 'peaks'},),
"MCP4apd": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_A.apd.pulseIntegral',
'dim': ['apdId']},),
"MCP4raw": ({'source': 'SCS_UTC1_ADQ/ADC/1:network',
'key': 'digitizers.channel_1_A.raw.samples',
- 'dim': ['samplesId']},),
+ 'dim': ['samplesId'],
+ 'extract': 'peaks'},),
# FastADC
"FastADC0peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_0.output',
@@ -768,61 +790,71 @@ mnemonics = {
'dim': ['peakId']},),
"FastADC0raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_0.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC1peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_1.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC1raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_1.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_2.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_2.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC3peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_3.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC3raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_3.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC4peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_4.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC4raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_4.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC5peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_5.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC5raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_5.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC6peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_6.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC6raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_6.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC7peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_7.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC7raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_7.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC8peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_8.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC8raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_8.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
"FastADC9peaks": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_9.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC9raw": ({'source': 'SCS_UTC1_MCP/ADC/1:channel_9.output',
'key': 'data.rawData',
- 'dim': ['fadc_samplesId']},),
+ 'dim': ['fadc_samplesId'],
+ 'extract': 'peaks'},),
# FastADC 2
"FastADC2_0peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_0.output',
@@ -830,61 +862,87 @@ mnemonics = {
'dim': ['peakId']},),
"FastADC2_0raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_0.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_1peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_1.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_1raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_1.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_2peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_2.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_2raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_2.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_3peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_3.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_3raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_3.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_4peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_4.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_4raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_4.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_5peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_5.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_5raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_5.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_6peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_6.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_6raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_6.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_7peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_7.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_7raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_7.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_8peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_8.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_8raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_8.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
"FastADC2_9peaks": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_9.output',
'key': 'data.peaks',
'dim': ['peakId']},),
"FastADC2_9raw": ({'source': 'SCS_UTC2_FADC/ADC/1:channel_9.output',
'key': 'data.rawData',
- 'dim': ['fadc2_samplesId']},),
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
+ "FFT_PD2raw": ({'source': 'SCS_FFT_DIAG/ADC/PD2:output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
+ "FFT_MCPraw": ({'source': 'SCS_FFT_MCP/ADC/MCP:output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
+ "FFT_REFLraw": ({'source': 'SCS_FFT_REFL/ADC/DIODE_UP:output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
+ "I0_ILHraw": ({'source': 'SCS_ILH_LAS/ADC/I0_ILH:output',
+ 'key': 'data.rawData',
+ 'dim': ['fadc2_samplesId'],
+ 'extract': 'peaks'},),
# KARABACON
"KARABACON": ({'source': 'SCS_DAQ_SCAN/MDL/KARABACON',
diff --git a/src/toolbox_scs/detectors/digitizers.py b/src/toolbox_scs/detectors/digitizers.py
index 29a98d97312c791d3d62ee3ec50fb32855d68b76..2fb86b511c1dd0cef161b5d578566e650c12a79a 100644
--- a/src/toolbox_scs/detectors/digitizers.py
+++ b/src/toolbox_scs/detectors/digitizers.py
@@ -830,6 +830,8 @@ def digitizer_type(mnemonic=None, mnemo_dict=None, source=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',
diff --git a/src/toolbox_scs/load.py b/src/toolbox_scs/load.py
index f7608ccc7e644a66eecad9846e22229f00be0a7b..a038c025a619de85de53830d43aab567fc89d8bf 100644
--- a/src/toolbox_scs/load.py
+++ b/src/toolbox_scs/load.py
@@ -44,14 +44,10 @@ def load(proposalNB=None, runNB=None,
validate=False,
subset=None,
rois={},
- extract_adq412=True,
- extract_fadc=True,
- extract_fadc2=True,
+ extract_digitizers=True,
extract_xgm=True,
extract_bam=True,
- adq412_bp='sase3',
- fadc_bp='scs_ppl',
- fadc2_bp='sase3',
+ bunchPattern='sase3',
extract_tim=None,
extract_laser=None,
tim_bp=None,
@@ -92,17 +88,9 @@ def load(proposalNB=None, runNB=None,
'dim': ['ref_x', 'ref_y']},
'sam': {'roi':by_index[1050:1210, 535:720],
'dim': ['sam_x', 'sam_y']}}}
- extract_adq412: bool
- If True, extracts the peaks from ADQ412 variables (e.g. 'MCP2raw',
- 'MCP3apd') and aligns the pulse Id with the sase3 bunch pattern.
- extract_fadc: bool
- If True, extracts the peaks from FastADC variables (e.g. 'FastADC5raw',
- 'FastADC3peaks') and aligns the pulse Id according to the fadc_bp bunch
- pattern.
- extract_fadc2: bool
- If True, extracts the peaks from FastADC2 variables (e.g.
- 'FastADC2_5raw', 'FastADC2_3peaks') and aligns the pulse Id according
- to the fadc2_bp bunch pattern.
+ extract_digitizers: bool
+ If True, extracts the peaks from digitizer variables and aligns the
+ pulse Id according to the fadc_bp bunch pattern.
extract_xgm: bool
If True, extracts the values from XGM variables (e.g. 'SCS_SA3',
'XTD10_XGM') and aligns the pulse Id with the sase1 / sase3 bunch
@@ -110,15 +98,13 @@ def load(proposalNB=None, runNB=None,
extract_bam: bool
If True, extracts the values from BAM variables (e.g. 'BAM1932M')
and aligns the pulse Id with the sase3 bunch pattern.
- adq412_bp: str
- bunch pattern used to extract the ADQ412 digitizer pulses.
- Ignored if extract_adq412=False.
- fadc_bp: str
+ bunchpattern: str
bunch pattern used to extract the Fast ADC pulses.
- Ignored if extract_fadc=False.
- fadc2_bp: str
- bunch pattern used to extract the Fast ADC 2 pulses.
- Ignored if extract_fadc2=False.
+ A string or a dict as in::
+
+ {'FFT_PD2': 'sase3', 'ILH_I0': 'scs_ppl'}
+
+ Ignored if extract_digitizers=False.
DEPRECATED ARGUMENTS:
extract_tim: DEPRECATED. Use extract_adq412 instead.
@@ -187,7 +173,8 @@ def load(proposalNB=None, runNB=None,
continue
if k not in rois:
# no ROIs selection, we read everything
- arr = run.get_array(*v.values(), name=k)
+ arr = run.get_array(v['source'], v['key'],
+ extra_dims=v['dim'], name=k)
if len(arr) == 0:
log.warning(f'Empty array for {f}: {v["source"]}, {v["key"]}. '
'Skipping!')
@@ -222,42 +209,28 @@ def load(proposalNB=None, runNB=None,
data = xr.merge(data_arrays, join='inner')
data.attrs['runFolder'] = runFolder
- # backward compatibility with old-defined variables:
- if extract_tim is not None:
- extract_adq412 = extract_tim
- if extract_laser is not None:
- extract_fadc = extract_laser
- if tim_bp is not None:
- adq412_bp = tim_bp
- if laser_bp is not None:
- fadc_bp = laser_bp
-
- adq412 = [k for k in run_mnemonics if 'MCP' in k and k in data]
- if extract_adq412 and len(adq412) > 0:
- data = tbdet.get_digitizer_peaks(run, mnemonics=adq412,
- merge_with=data,
- bunchPattern=adq412_bp)
-
- fadc = [k for k in run_mnemonics if ('FastADC' in k)
- and ('FastADC2_' not in k) and (k in data)]
- if extract_fadc and len(fadc) > 0:
- data = tbdet.get_digitizer_peaks(run, mnemonics=fadc, merge_with=data,
- bunchPattern=fadc_bp)
- fadc2 = [k for k in run_mnemonics if 'FastADC2_' in k and k in data]
- if extract_fadc2 and len(fadc2) > 0:
- data = tbdet.get_digitizer_peaks(run, mnemonics=fadc2, merge_with=data,
- bunchPattern=fadc2_bp)
-
- xgm = ['XTD10_XGM', 'XTD10_XGM_sigma', 'XTD10_SA3', 'XTD10_SA3_sigma',
- 'XTD10_SA1', 'XTD10_SA1_sigma', 'SCS_XGM', 'SCS_XGM_sigma',
- 'SCS_SA1', 'SCS_SA1_sigma', 'SCS_SA3', 'SCS_SA3_sigma']
- xgm = [k for k in xgm if k in data]
- if extract_xgm and len(xgm) > 0:
- data = tbdet.get_xgm(run, mnemonics=xgm, merge_with=data)
-
- bam = [k for k in run_mnemonics if 'BAM' in k and k in data]
- if extract_bam and len(bam) > 0:
- data = tbdet.get_bam(run, mnemonics=bam, merge_with=data)
+ if extract_digitizers:
+ bp = bunchPattern
+ for k, v in run_mnemonics.items():
+ if k not in data or v.get('extract') != 'peaks':
+ continue
+ if isinstance(bunchPattern, dict):
+ bp = bunchPattern.get(k)
+ if bp is None:
+ continue
+ data = tbdet.get_digitizer_peaks(
+ run, mnemonics=k, merge_with=data, bunchPattern=bp)
+ if extract_xgm:
+ for k, v in run_mnemonics.items():
+ if k not in data or v.get('extract') != 'XGM':
+ continue
+ data = tbdet.get_xgm(run, mnemonics=k, merge_with=data)
+
+ if extract_bam:
+ for k, v in run_mnemonics.items():
+ if k not in data or v.get('extract') != 'BAM':
+ continue
+ data = tbdet.get_bam(run, mnemonics=k, merge_with=data)
return run, data