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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.
"""
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import numpy as np
from karabo_data.read_machinery import find_proposal
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import xarray as xr
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mnemonics = {
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# Machine
"sase3": {'source':'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key':'sase3.pulseIds.value',
'dim':['bunchId']},
"sase2": {'source':'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key':'sase2.pulseIds.value',
'dim':['bunchId']},
"sase1": {'source':'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key':'sase1.pulseIds.value',
'dim':['bunchId']},
"maindump": {'source':'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key':'maindump.pulseIds.value',
'dim':['bunchId']},
"bunchpattern": {'source':'SCS_RR_UTC/TSYS/TIMESERVER',
'key':'readBunchPatternTable.value',
'dim':None},
"npulses_sase3": {'source':'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key':'sase3.nPulses.value',
'dim':None},
"npulses_sase1": {'source':'SCS_RR_UTC/MDL/BUNCH_DECODER',
'key':'sase1.nPulses.value',
'dim':None},
# SA3
"nrj": {'source':'SA3_XTD10_MONO/MDL/PHOTON_ENERGY',
'key':'actualEnergy.value',
'dim':None},
"M2BEND": {'source': 'SA3_XTD10_MIRR-2/MOTOR/BENDER',
'key': 'actualPosition.value',
'dim':None},
"VSLIT": {'source':'SA3_XTD10_VSLIT/MDL/BLADE',
'key':'actualGap.value',
'dim':None},
"ESLIT": {'source':'SCS_XTD10_ESLIT/MDL/MAIN',
'key':'actualGap.value',
'dim':None},
"HSLIT": {'source':'SCS_XTD10_HSLIT/MDL/BLADE',
'key':'actualGap.value',
'dim':None},
"transmission": {'source':'SA3_XTD10_GATT/MDL/GATT_TRANSMISSION_MONITOR',
'key':'Estimated_Tr.value',
'dim':None},
"GATT_pressure": {'source':'P_GATT',
'key':'value.value',
'dim':None},
"navitar": {'source':'SCS_XTD10_IMGES/CAM/BEAMVIEW_NAVITAR:daqOutput',
'key':'data.image.pixels',
'dim':['x','y']},
"UND": {'source':'SA3_XTD10_UND/DOOCS/PHOTON_ENERGY',
'key':'actualPosition.value',
'dim':None},
# XTD10 XGM
## keithley
"XTD10_photonFlux": {'source':'SA3_XTD10_XGM/XGM/DOOCS',
'key':'pulseEnergy.photonFlux.value',
'dim':None},
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"XTD10_photonFlux_sigma": {'source':'SA3_XTD10_XGM/XGM/DOOCS',
'key':'pulseEnergy.photonFluxSigma.value',
'dim':None},
## ADC
"XTD10_XGM": {'source':'SA3_XTD10_XGM/XGM/DOOCS:output',
'key':'data.intensityTD',
'dim':['XGMbunchId']},
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"XTD10_XGM_sigma": {'source':'SA3_XTD10_XGM/XGM/DOOCS:output',
'key':'data.intensitySigmaTD',
'dim':['XGMbunchId']},
"XTD10_SA3": {'source':'SA3_XTD10_XGM/XGM/DOOCS:output',
'key':'data.intensitySa3TD',
'dim':['XGMbunchId']},
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"XTD10_SA3_sigma": {'source':'SA3_XTD10_XGM/XGM/DOOCS:output',
'key':'data.intensitySa3SigmaTD',
'dim':['XGMbunchId']},
"XTD10_SA1": {'source':'SA3_XTD10_XGM/XGM/DOOCS:output',
'key':'data.intensitySa1TD',
'dim':['XGMbunchId']},
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"XTD10_SA1_sigma": {'source':'SA3_XTD10_XGM/XGM/DOOCS:output',
'key':'data.intensitySa1SigmaTD',
'dim':['XGMbunchId']},
## low pass averaged ADC
"XTD10_slowTrain": {'source':'SA3_XTD10_XGM/XGM/DOOCS',
'key':'controlData.slowTrain.value',
'dim':None},
"XTD10_slowTrain_SA1": {'source':'SA3_XTD10_XGM/XGM/DOOCS',
'key':'controlData.slowTrainSa1.value',
'dim':None},
"XTD10_slowTrain_SA3": {'source':'SA3_XTD10_XGM/XGM/DOOCS',
'key':'controlData.slowTrainSa3.value',
'dim':None},
# SCS XGM
## keithley
"SCS_photonFlux": {'source':'SCS_BLU_XGM/XGM/DOOCS',
'key':'pulseEnergy.photonFlux.value',
'dim':None},
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"SCS_photonFlux_sigma": {'source':'SCS_BLU_XGM/XGM/DOOCS',
'key':'pulseEnergy.photonFluxSigma.value',
'dim':None},
"SCS_XGM": {'source':'SCS_BLU_XGM/XGM/DOOCS:output',
'key':'data.intensityTD',
'dim':['XGMbunchId']},
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"SCS_XGM_sigma": {'source':'SCS_BLU_XGM/XGM/DOOCS:output',
'key':'data.intensitySigmaTD',
'dim':['XGMbunchId']},
"SCS_SA1": {'source':'SCS_BLU_XGM/XGM/DOOCS:output',
'key':'data.intensitySa1TD',
'dim':['XGMbunchId']},
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"SCS_SA1_sigma": {'source':'SCS_BLU_XGM/XGM/DOOCS:output',
'key':'data.intensitySa1SigmaTD',
'dim':['XGMbunchId']},
"SCS_SA3": {'source':'SCS_BLU_XGM/XGM/DOOCS:output',
'key':'data.intensitySa3TD',
'dim':['XGMbunchId']},
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"SCS_SA3_sigma": {'source':'SCS_BLU_XGM/XGM/DOOCS:output',
'key':'data.intensitySa3SigmaTD',
'dim':['XGMbunchId']},
## low pass averaged ADC
"SCS_slowTrain": {'source':'SCS_BLU_XGM/XGM/DOOCS',
'key':'controlData.slowTrain.value',
'dim':None},
"SCS_slowTrain_SA1": {'source':'SCS_BLU_XGM/XGM/DOOCS',
'key':'controlData.slowTrainSa1.value',
'dim':None},
"SCS_slowTrain_SA3": {'source':'SCS_BLU_XGM/XGM/DOOCS',
'key':'controlData.slowTrainSa3.value',
'dim':None},
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# KBS
"HFM_CAPB": {'source':'SCS_KBS_HFM/ASENS/CAPB',
'key':'value.value',
'dim':None},
"HFM_CAPF": {'source':'SCS_KBS_HFM/ASENS/CAPF',
'key':'value.value',
'dim':None},
"HFM_CAPM": {'source':'SCS_KBS_HFM/ASENS/CAPM',
'key':'value.value',
'dim':None},
"HFM_BENDERB": {'source':'SCS_KBS_HFM/MOTOR/BENDERB',
'key':'encoderPosition.value',
'dim':None},
"HFM_BENDERF": {'source':'SCS_KBS_HFM/MOTOR/BENDERF',
'key':'encoderPosition.value',
'dim':None},
"VFM_CAPB": {'source':'SCS_KBS_VFM/ASENS/CAPB',
'key':'value.value',
'dim':None},
"VFM_CAPF": {'source':'SCS_KBS_VFM/ASENS/CAPF',
'key':'value.value',
'dim':None},
"VFM_CAPM": {'source':'SCS_KBS_VFM/ASENS/CAPM',
'key':'value.value',
'dim':None},
"VFM_BENDERB": {'source':'SCS_KBS_VFM/MOTOR/BENDERB',
'key':'encoderPosition.value',
'dim':None},
"VFM_BENDERF": {'source':'SCS_KBS_VFM/MOTOR/BENDERF',
'key':'encoderPosition.value',
'dim':None},
"AFS_PhaseShifter": {'source':'SCS_ILH_LAS/PHASESHIFTER/DOOCS',
'key':'actualPosition.value',
'dim':None},
"AFS_DelayLine": {'source':'SCS_ILH_LAS/MOTOR/LT3',
'key':'AActualPosition.value',
'dim':None},
"AFS_HalfWP": {'source':'SCS_ILH_LAS/MOTOR/ROT_OPA_BWP1',
'key':'actualPosition.value',
'dim':None},
"AFS_FocusLens": {'source':'SCS_ILH_LAS/MOTOR/LT_SPARE1',
'key':'actualPosition.value',
'dim':None},
# 2nd lens of telescope
"AFS_TeleLens": {'source':'SCS_ILH_LAS/MOTOR/LT2',
'key':'actualPosition.value',
'dim':None},
# PP LASER 800 nm path
"PP800_PhaseShifter": {'source':'SCS_ILH_LAS/DOOCS/PP800_PHASESHIFTER',
'key':'actualPosition.value',
'dim':None},
"PP800_SynchDelayLine": {'source':'SCS_ILH_LAS/DOOCS/PPL_OPT_DELAY',
'key':'actualPosition.value',
'dim':None},
"PP800_DelayLine": {'source':'SCS_ILH_LAS/MOTOR/LT3',
'key':'AActualPosition.value',
'dim':None},
"PP800_HalfWP": {'source':'SCS_ILH_LAS/MOTOR/ROT8WP1',
'key':'actualPosition.value',
'dim':None},
"PP800_FocusLens": {'source':'SCS_ILH_LAS/MOTOR/LT_SPARE1',
'key':'actualPosition.value',
'dim':None},
# 1st lens of telescope (setup of August 2019)
"PP800_TeleLens": {'source':'SCS_ILH_LAS/MOTOR/LT7',
'key':'actualPosition.value',
'dim':None},
# FFT
"scannerX": {'source':'SCS_CDIFFT_SAM/LMOTOR/SCANNERX',
'key':'actualPosition.value',
'dim':None},
"scannerY": {'source':'SCS_CDIFFT_SAM/MOTOR/SCANNERY',
'key':'actualPosition.value',
'dim':None},
"scannerY_enc": {'source':'SCS_CDIFFT_SAM/ENC/SCANNERY',
'key':'value.value',
'dim':None},
"SAM-Z": {'source':'SCS_CDIFFT_MOV/ENC/SAM_Z',
'key':'value.value',
'dim':None},
"magnet": {'source':'SCS_CDIFFT_MAG/ASENS/CURRENT',
'key':'value.value',
'dim':None},
"magnet_old": {'source':'SCS_CDIFFT_MAG/SUPPLY/CURRENT',
'dim':None},
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# FastCCD, if in raw folder, raw images
# if in proc folder, dark substracted and relative gain corrected
"fastccd": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
'key':'data.image.pixels',
'dim':['x', 'y']},
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# FastCCD with common mode correction
"fastccd_cm": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
'key':'data.image.pixels_cm',
'dim':['x', 'y']},
# FastCCD charge split correction in very low photon count regime
"fastccd_classified": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
'key':'data.image.pixels_classified',
'dim':['x', 'y']},
# FastCCD event multiplicity from the charge split correction:
# 0: no events
# 100, 101: single events
# 200-203: charge split into two pixels in four different orientations
# 300-303: charge split into three pixels in four different orientations
# 400-403: charge split into four pixels in four different orientations
# 1000: charge in more than four neighboring pixels. Cannot be produced by a single photon alone.
"fastccd_patterns": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
'key':'data.image.patterns',
'dim':['x', 'y']},
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"fastccd_gain": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
'key':'data.image.gain',
'dim':['x', 'y']},
# FastCCD mask, bad pixel map to be ignored if > 0
"fastccd_mask": {'source':'SCS_CDIDET_FCCD2M/DAQ/FCCD:daqOutput',
'key':'data.image.mask',
'dim':['x', 'y']},
# TIM
"MCP1apd": {'source':'SCS_UTC1_ADQ/ADC/1:network',
'key':'digitizers.channel_1_D.apd.pulseIntegral',
'dim':['apdId']},
"MCP1raw": {'source':'SCS_UTC1_ADQ/ADC/1:network',
'key':'digitizers.channel_1_D.raw.samples',
'dim':['samplesId']},
"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']},
"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']},
"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',
# FastADC
"FastADC0peaks": {'source':'SCS_UTC1_MCP/ADC/1:channel_0.output',
'key':'data.peaks',
'dim':['peakId']},
"FastADC0raw": {'source':'SCS_UTC1_MCP/ADC/1:channel_0.output',
'key':'data.rawData',
'dim':['fadc_samplesId']},
"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']},
"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']},
"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']},
"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']},
"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']},
"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']},
"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']},
"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']},
"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']},
# KARABACON
"KARABACON": {'source':'SCS_DAQ_SCAN/MDL/KARABACON',
'key': 'actualStep.value',
'dim': None},
#GOTTHARD
"Gotthard1": {'source':'SCS_PAM_XOX/DET/GOTTHARD_RECEIVER1:daqOutput',
'key': 'data.adc',
'dim': ['gott_pId','pixelId']},
"Gotthard2": {'source':'SCS_PAM_XOX/DET/GOTTHARD_RECEIVER2:daqOutput',
'key': 'data.adc',
'dim': ['gott_pId','pixelId']}
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}
def load(fields, runNB, proposalNB, subFolder='raw', display=False, validate=False,
subset=by_index[:], rois={}):
""" Load a run and extract the data. Output is an xarray with aligned trainIds
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Inputs:
fields: list of mnemonic strings to load specific data such as "fastccd", "SCS_XGM",
or dictionnaries defining a custom mnemonic such as
{"extra": {'SCS_CDIFFT_MAG/SUPPLY/CURRENT', 'actual_current.value', None}}
runNB: (str, int) run number as integer
proposalNB: (str, int) of the proposal number e.g. 'p002252' or 2252
subFolder: (str) sub-folder from which to load the data. Use 'raw' for raw
data or 'proc' for processed data.
display: (bool) whether to show the run.info or not
validate: (bool) whether to run karabo-data-validate or not
subset: a subset of train that can be load with by_index[:5] for the
first 5 trains
rois: a dictionnary of mnemonics with a list of rois definition and the desired
names, for example {'fastccd':{'ref':{'roi':by_index[730:890, 535:720],
'dim': ['ref_x', 'ref_y']}, 'sam':{'roi':by_index[1050:1210, 535:720],
'dim': ['sam_x', 'sam_y']}}}
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Outputs:
res: an xarray DataSet with aligned trainIds
"""
if isinstance(runNB, int):
runNB = 'r{:04d}'.format(runNB)
if isinstance(proposalNB,int):
proposalNB = 'p{:06d}'.format(proposalNB)
runFolder = os.path.join(find_proposal(proposalNB), subFolder, runNB)
run = RunDirectory(runFolder).select_trains(subset)
if validate:
get_ipython().system('karabo-data-validate ' + runFolder)
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if display:
print('Loading data from {}'.format(runFolder))
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run.info()
keys = []
vals = []
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# always load pulse pattern infos
fields += ["sase1", "sase3", "npulses_sase3", "npulses_sase1"]
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for f in fields:
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if type(f) == dict:
# extracting mnemomic defined on the spot
if len(f.keys()) > 1:
print('Loading only one "on-the-spot" mnemonic at a time, skipping all others !')
k = list(f.keys())[0]
v = f[k]
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else:
# extracting mnemomic from the table
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if f in mnemonics:
v = mnemonics[f]
k = f
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else:
print('Unknow mnemonic "{}". Skipping!'.format(f))
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continue
if k in keys:
continue # already loaded, skip
if display:
print('Loading {}'.format(k))
if v['source'] not in run.all_sources:
print('Source {} not found in run. Skipping!'.format(v['source']))
continue
if k not in rois:
# no ROIs selection, we read everything
vals.append(run.get_array(v['source'], v['key'], extra_dims=v['dim']))
keys.append(k)
else:
# ROIs selection, for each ROI we select a region of the data and save it with new name and dimensions
for nk,nv in rois[k].items():
vals.append(run.get_array(v['source'], v['key'], extra_dims=nv['dim'], roi=nv['roi']))
keys.append(nk)
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aligned_vals = xr.align(*vals, join='inner')
result = dict(zip(keys, aligned_vals))
result = xr.Dataset(result)
result.attrs['run'] = run
def concatenateRuns(runs):
""" Sorts and concatenate a list of runs with identical data variables along the
trainId dimension.
Input:
runs: (list) the xarray Datasets to concatenate
Output:
a concatenated xarray Dataset
"""
firstTid = {i: int(run.trainId[0].values) for i,run in enumerate(runs)}
orderedDict = dict(sorted(firstTid.items(), key=lambda t: t[1]))
orderedRuns = [runs[i] for i in orderedDict]
keys = orderedRuns[0].keys()
for run in orderedRuns[1:]:
if run.keys() != keys:
print('data fields between different runs are not identical. Cannot combine runs.')
return
result = xr.concat(orderedRuns, dim='trainId')
result.attrs['run'] = [run.attrs['run'] for run in orderedRuns]
result.attrs['runFolder'] = [run.attrs['runFolder'] for run in orderedRuns]
return result