def run_qa(self, qatype, inputs, clobber=True):
"""Run QA tests of a given type
Over-writes previous QA of this type, unless otherwise specified
qatype: str
Type of QA to be performed (e.g. SKYSUB)
inputs: tuple
Set of inputs for the tests
clobber: bool, optional [True]
Over-write previous QA
"""
from desispec.sky import qa_skysub
from desispec.fiberflat import qa_fiberflat
from desispec.fluxcalibration import qa_fluxcalib
# Check for previous QA if clobber==False
if not clobber:
# QA previously performed?
if 'METRICS' in self.qa_data[qatype]:
return
# Run
if qatype == 'SKYSUB':
# Expecting: frame, skymodel
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_skysub()
# Run
qadict = qa_skysub(self.qa_data[qatype]['PARAMS'], inputs[0],
inputs[1])
elif qatype == 'FIBERFLAT':
# Expecting: frame, fiberflat
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_fiberflat()
# Run
qadict = qa_fiberflat(self.qa_data[qatype]['PARAMS'], inputs[0],
inputs[1])
elif qatype == 'FLUXCALIB':
# Expecting: frame, fluxcalib
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_fluxcalib()
# Run
qadict = qa_fluxcalib(self.qa_data[qatype]['PARAMS'], inputs[0],
inputs[1])
else:
raise ValueError('Not ready to perform {:s} QA'.format(qatype))
# Update
self.qa_data[qatype]['METRICS'] = qadict
def run_qa(self, qatype, inputs, clobber=True):
"""Run QA tests of a given type
Over-writes previous QA of this type, unless otherwise specified
qatype: str
Type of QA to be performed (e.g. SKYSUB)
inputs: tuple
Set of inputs for the tests
clobber: bool, optional [True]
Over-write previous QA
"""
from desispec.sky import qa_skysub
from desispec.fiberflat import qa_fiberflat
from desispec.fluxcalibration import qa_fluxcalib
# Check for previous QA if clobber==False
if not clobber:
# QA previously performed?
if 'QA' in self.qa_data[qatype].keys():
return
# Run
if qatype == 'SKYSUB':
# Expecting: frame, skymodel
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_skysub()
# Run
qadict = qa_skysub(self.qa_data[qatype]['PARAM'],
inputs[0], inputs[1])
elif qatype == 'FIBERFLAT':
# Expecting: frame, fiberflat
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_fiberflat()
# Run
qadict = qa_fiberflat(self.qa_data[qatype]['PARAM'], inputs[0], inputs[1])
elif qatype == 'FLUXCALIB':
# Expecting: frame, fluxcalib
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_fluxcalib()
# Run
qadict = qa_fluxcalib(self.qa_data[qatype]['PARAM'], inputs[0], inputs[1])
else:
raise ValueError('Not ready to perform {:s} QA'.format(qatype))
# Update
self.qa_data[qatype]['QA'] = qadict
def run_qa(self, qatype, inputs, clobber=True):
"""Run QA tests of a given type
Over-writes previous QA of this type, unless otherwise specified
qatype: str
Type of QA to be performed (e.g. SKYSUB)
inputs: tuple
Set of inputs for the tests
clobber: bool, optional [True]
Over-write previous QA
Returns:
bool
True = Calculation performed
False = Calculation not performed
"""
from desispec.sky import qa_skysub
from desispec.fiberflat import qa_fiberflat
from desispec.fluxcalibration import qa_fluxcalib
from desispec.qa.qalib import SNRFit
# Check for previous QA if clobber==False
if (not clobber) and (qatype in self.qa_data.keys()):
# QA previously performed?
if 'METRICS' in self.qa_data[qatype]:
return False
# Run
if qatype == 'SKYSUB':
# Expecting: frame, skymodel
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_skysub()
# Run
qadict = qa_skysub(self.qa_data[qatype]['PARAMS'], inputs[0],
inputs[1])
elif qatype == 'FIBERFLAT':
# Expecting: frame, fiberflat
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_fiberflat()
# Run
qadict = qa_fiberflat(self.qa_data[qatype]['PARAMS'], inputs[0],
inputs[1])
elif qatype == 'FLUXCALIB':
# Expecting: frame, fluxcalib
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_fluxcalib()
# Run
qadict = qa_fluxcalib(self.qa_data[qatype]['PARAMS'], inputs[0],
inputs[1])
elif qatype == 'S2N':
# Expecting only a frame
assert len(inputs) == 1
# Init parameters (as necessary)
self.init_s2n()
# Run
qadict, fitsnr = SNRFit(inputs[0],
self.night,
self.camera,
self.expid,
self.qa_data[qatype]['PARAMS'],
fidboundary=None,
offline=True)
# Remove undesired
for key in ['RA', 'DEC']:
qadict.pop(key)
else:
raise ValueError('Not ready to perform {:s} QA'.format(qatype))
# Update
self.qa_data[qatype]['METRICS'] = qadict
# Return
return True
def run_qa(self, qatype, inputs, clobber=True):
"""Run QA tests of a given type
Over-writes previous QA of this type, unless otherwise specified
qatype: str
Type of QA to be performed (e.g. SKYSUB)
inputs: tuple
Set of inputs for the tests
clobber: bool, optional [True]
Over-write previous QA
Returns:
bool
True = Calculation performed
False = Calculation not performed
"""
from desispec.sky import qa_skysub
from desispec.fiberflat import qa_fiberflat
from desispec.fluxcalibration import qa_fluxcalib
from desispec.qa.qalib import SNRFit
# Check for previous QA if clobber==False
if (not clobber) and (qatype in self.qa_data.keys()):
# QA previously performed?
if 'METRICS' in self.qa_data[qatype]:
return False
# Run
if qatype == 'SKYSUB':
# Expecting: frame, skymodel
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_skysub()
# Run
qadict = qa_skysub(self.qa_data[qatype]['PARAMS'],
inputs[0], inputs[1])
elif qatype == 'FIBERFLAT':
# Expecting: frame, fiberflat
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_fiberflat()
# Run
qadict = qa_fiberflat(self.qa_data[qatype]['PARAMS'], inputs[0], inputs[1])
elif qatype == 'FLUXCALIB':
# Expecting: frame, fluxcalib
assert len(inputs) == 2
# Init parameters (as necessary)
self.init_fluxcalib()
# Run
qadict = qa_fluxcalib(self.qa_data[qatype]['PARAMS'], inputs[0], inputs[1])
elif qatype == 'S2N':
# Expecting only a frame
assert len(inputs) == 1
# Init parameters (as necessary)
self.init_s2n()
# Run
qadict,fitsnr = SNRFit(inputs[0], self.night, self.camera, self.expid,
self.qa_data[qatype]['PARAMS'], fidboundary=None,
offline=True)
# Remove undesired
for key in ['RA', 'DEC']:
qadict.pop(key)
else:
raise ValueError('Not ready to perform {:s} QA'.format(qatype))
# Update
self.qa_data[qatype]['METRICS'] = qadict
# Return
return True