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import numpy as np
from .base import DeviceBase
class XGM(DeviceBase):
control_keys = [
('beamPosition/ixPos', 'f4', ()),
('beamPosition/iyPos', 'f4', ()),
('current/bottom/output', 'f4', ()),
('current/bottom/rangeCode', 'i4', ()),
('current/left/output', 'f4', ()),
('current/left/rangeCode', 'i4', ()),
('current/right/output', 'f4', ()),
('current/right/rangeCode', 'i4', ()),
('current/top/output', 'f4', ()),
('current/top/rangeCode', 'i4', ()),
('gasDosing/measuredPressure', 'f4', ()),
('gasDosing/pressureSetPoint', 'f4', ()),
('gasSupply/gasTypeId', 'i4', ()),
('gasSupply/gsdCompatId', 'i4', ()),
('pollingInterval', 'i4', ()),
('pressure/dcr', 'f4', ()),
('pressure/gasType', 'i4', ()),
('pressure/pressure1', 'f4', ()),
('pressure/pressureFiltered', 'f4', ()),
('pressure/rd', 'f4', ()),
('pressure/rsp', 'f4', ()),
('pulseEnergy/conversion', 'f8', ()),
('pulseEnergy/crossUsed', 'f4', ()),
('pulseEnergy/gammaUsed', 'f4', ()),
('pulseEnergy/gmdError', 'i4', ()),
('pulseEnergy/nummberOfBrunches', 'f4', ()),
('pulseEnergy/photonFlux', 'f4', ()),
('pulseEnergy/pressure', 'f4', ()),
('pulseEnergy/temperature', 'f4', ()),
('pulseEnergy/usedGasType', 'i4', ()),
('pulseEnergy/wavelengthUsed', 'f4', ()),
('signalAdaption/dig', 'i4', ()),
]
extra_run_values = [
('classId', None, 'DoocsXGM'),
]
# Technically, only the part before the / is the output channel.
# But there is a structure associated with the part one level after that,
# and we don't know what else to call it.
output_channels = ('output/data',)
instrument_keys = [
('intensityTD', 'f4', (1000,)),
('intensityAUXTD', 'f4', (1000,)),
('intensitySigma/x_data', 'f4', (1000,)),
('intensitySigma/y_data', 'f4', (1000,)),
('xTD', 'f4', (1000,)),
('yTD', 'f4', (1000,)),
]
def write_instrument(self, f):
super().write_instrument(f)
# Annotate intensityTD with some units to test retrieving them
# Karabo stores ASCII strings, assigning bytes is a shortcut to mimic that
ds = f[f'INSTRUMENT/{self.device_id}:output/data/intensityTD']
ds.attrs['metricPrefixEnum']= np.array([14], dtype=np.int32)
ds.attrs['metricPrefixName'] = b'micro'
ds.attrs['metricPrefixSymbol'] = b'u'
ds.attrs['unitEnum'] = np.array([15], dtype=np.int32)
ds.attrs['unitName'] = b'joule'
ds.attrs['unitSymbol'] = b'J'
# Also annotate a CONTROL key, where attributes are split across
# the parent key group and the value dataset.
# (The timestamp dataset has its own, but distinct attributes)
# Specific examples taken from p5696, r32
grp = f[f'CONTROL/{self.device_id}/beamPosition/ixPos']
grp.attrs['alias'] = b'IX.POS'
grp.attrs['description'] = b'Calculated X position [mm]'
grp.attrs['daqPolicy'] = np.array([-1], dtype=np.int32)
# daqPolicy is intentionally different, the correct schema value
# is -1 as above!
ds = grp['value']
ds.attrs['alias'] = b'IX.POS'
ds.attrs['daqPolicy'] = np.array([1], dtype=np.int32)
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